Overlaying Hi-C with Gene Expression
Ittai Eres
2019-03-14
Last updated: 2019-05-09
Checks: 6 0
Knit directory: HiCiPSC/
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | ff886b1 | Ittai Eres | 2019-04-30 | Build site. |
| html | db4d599 | Ittai Eres | 2019-04-24 | Build site. |
| html | 6f6db11 | Ittai Eres | 2019-04-23 | Build site. |
| html | a02a602 | Ittai Eres | 2019-03-14 | Build site. |
| Rmd | a6451a3 | Ittai Eres | 2019-03-14 | Add gene expression overlay (still needs more modification); update index |
First, load necessary libraries: limma, plyr, tidyr, data.table, reshape2, cowplot, plotly, dplyr, Hmisc, gplots, stringr, heatmaply, RColorBrewer, edgeR, tidyverse, and compiler
#Preparing the gene expression data Here I read in a dataframe of counts summarized at the gene-level, then do some pre-processing and normalization to obtain a voom object to later run linear modeling analyses on.
#Read in counts data, create DGEList object out of them and convert to log counts per million (CPM). Also read in metadata.
setwd("/Users/ittaieres/HiCiPSC")
counts <- fread("data/counts_iPSC.txt", header=TRUE, data.table=FALSE, stringsAsFactors = FALSE, na.strings=c("NA",""))
colnames(counts) <- c("genes", "C-3649", "G-3624", "H-3651", "D-40300", "F-28834", "B-28126", "E-28815", "A-21792")
rownames(counts) <- counts$genes
counts <- counts[,-1]
dge <- DGEList(counts, genes=rownames(counts))
#Now, convert counts into RPKM to account for gene length differences between species. First load in and re-organize metadata, then the gene lengths for both species, and then the function to convert counts to RPKM.
meta_data <- fread("data/Meta_data.txt", sep="\t",stringsAsFactors = FALSE,header=T,na.strings=c("NA",""))
meta_data$fullID <- c("C-3649", "H-3651", "B-28126", "D-40300", "G-3624", "A-21792", "E-28815", "F-28834")
ord <- data.frame(fullID=colnames(counts)) #Pull order of samples from expression object
left_join(ord, meta_data, by="fullID") -> group_ref #left join meta data to this to make sure sample IDs correctWarning: Column `fullID` joining factor and character vector, coercing into
character vector#Read in human and chimp gene lengths for the RPKM function:
human_lengths<- fread("data/human_lengths.txt", sep="\t",stringsAsFactors = FALSE,header=T,na.strings=c("NA",""))
chimp_lengths<- fread("data/chimp_lengths.txt", sep="\t",stringsAsFactors = FALSE,header=T,na.strings=c("NA",""))
#The function for RPKM conversion.
vRPKM <- function(expr.obj,chimp_lengths,human_lengths,meta4) {
if (is.null(expr.obj$E)) {
meta4%>%filter(SP=="C" & fullID %in% colnames(counts))->chimp_meta
meta4%>%filter(SP=="H" & fullID %in% colnames(counts))->human_meta
#using RPKM function:
#Put genes in correct order:
expr.obj$genes %>%select(Geneid=genes)%>%
left_join(.,chimp_lengths,by="Geneid")%>%select(Geneid,ch.length)->chlength
expr.obj$genes %>%select(Geneid=genes)%>%
left_join(.,human_lengths,by="Geneid")%>%select(Geneid,hu.length)->hulength
#Chimp RPKM
expr.obj$genes$Length<-(chlength$ch.length)
RPKMc=rpkm(expr.obj,normalized.lib.sizes=TRUE, log=TRUE)
RPKMc[,colnames(RPKMc) %in% chimp_meta$fullID]->rpkm_chimp
#Human RPKM
expr.obj$genes$Length<-hulength$hu.length
RPKMh=rpkm(expr.obj,normalized.lib.sizes=TRUE, log=TRUE)
RPKMh[,colnames(RPKMh) %in% human_meta$fullID]->rpkm_human
cbind(rpkm_chimp,rpkm_human)->allrpkm
expr.obj$E <- allrpkm
return(expr.obj)
}
else {
#Pull out gene order from voom object and add in gene lengths from feature counts file
#Put genes in correct order:
expr.obj$genes %>%select(Geneid=genes)%>%
left_join(.,chimp_lengths,by="Geneid")%>%select(Geneid,ch.length)->chlength
expr.obj$genes %>%select(Geneid=genes)%>%
left_join(.,human_lengths,by="Geneid")%>%select(Geneid,hu.length)->hulength
#Filter meta data to be able to separate human and chimp
meta4%>%filter(SP=="C")->chimp_meta
meta4%>%filter(SP=="H")->human_meta
#Pull out the expression data in cpm to convert to RPKM
expr.obj$E->forRPKM
forRPKM[,colnames(forRPKM) %in% chimp_meta$fullID]->rpkm_chimp
forRPKM[,colnames(forRPKM) %in% human_meta$fullID]->rpkm_human
#Make log2 in KB:
row.names(chlength)=chlength$Geneid
chlength %>% select(-Geneid)->chlength
as.matrix(chlength)->chlength
row.names(hulength)=hulength$Geneid
hulength %>% select(-Geneid)->hulength
as.matrix(hulength)->hulength
log2(hulength/1000)->l2hulength
log2(chlength/1000)->l2chlength
#Subtract out log2 kb:
sweep(rpkm_chimp, 1,l2chlength,"-")->chimp_rpkm
sweep(rpkm_human, 1,l2hulength,"-")->human_rpkm
colnames(forRPKM)->column_order
cbind(chimp_rpkm,human_rpkm)->vRPKMS
#Put RPKMS back into the VOOM object:
expr.obj$E <- (vRPKMS[,colnames(vRPKMS) %in% column_order])
return(expr.obj)
}
}
dge <- vRPKM(dge, chimp_lengths, human_lengths, group_ref) #Normalize via log2 RPKM.
#A typical low-expression filtering step: use default prior count adding (0.25), and filtering out anything that has fewer than half the individuals within each species having logCPM less than 1.5 (so want 2 humans AND 2 chimps with log2CPM >= 1.5)
lcpms <- cpm(dge$counts, log=TRUE) #Obtain log2CPM!
good.chimps <- which(rowSums(lcpms[,1:4]>=1.5)>=2) #Obtain good chimp indices
good.humans <- which(rowSums(lcpms[,5:8]>=1.5)>=2) #Obtain good human indices
filt <- good.humans[which(good.humans %in% good.chimps)] #Subsets us down to a solid 11,292 genes--will go for a similar percentage with RPKM cutoff vals! (25.6% of total)
#Repeat filtering step, this time on RPKMs. 0.4 was chosen as a cutoff as it obtains close to the same results as 1.5 lcpm (in terms of percentage of genes retained)
good.chimps <- which(rowSums(dge$E[,1:4]>=0.4)>=2) #Obtain good chimp indices.
good.humans <- which(rowSums(dge$E[,5:8]>=0.4)>=2) #Obtain good human indices.
RPKM_filt <- good.humans[which(good.humans %in% good.chimps)] #Still leaves us with 11,946 genes (27.1% of total)
#Do the actual filtering.
dge_filt <- dge[RPKM_filt,]
dge_filt$E <- dge$E[RPKM_filt,]
dge_filt$counts <- dge$counts[RPKM_filt,]
dge_filt$lcpm_counts <- cpm(dge$counts, log=TRUE)[RPKM_filt,] #Add this in to be able to look at log cpms later
dge_final <- calcNormFactors(dge_filt, method="TMM") #Calculate normalization factors with trimmed mean of M-values (TMM).
dge_norm <- calcNormFactors(dge, method="TMM") #Calculate normalization factors with TMM on dge before filtering out lowly expressed genes, for normalization visualization.
#Quick visualization of the filtering I've just performed:
col <- brewer.pal(8, "Paired")
par(mfrow=c(1,2))
plot(density(dge$E[,1]), col=col[1], lwd=2, ylim=c(0,0.35), las=2,
main="", xlab="")
title(main="A. Raw data", xlab="Log-RPKM")
abline(v=1, lty=3)
for (i in 2:8){
den <- density(dge$E[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", colnames(dge$E[,1:8]), text.col=col, bty="n")
plot(density(dge_final$E[,1]), col=col[1], lwd=2, ylim=c(0,0.35), las=2,
main="", xlab="")
title(main="B. Filtered data", xlab="Log-RPKM")
abline(v=1, lty=3)
for (i in 2:8){
den <- density(dge_final$E[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", colnames(dge_final[,1:8]), text.col=col, bty="n")
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
#Quick visualization of the normalization on the whole set of genes.
col <- brewer.pal(8, "Paired")
raw <- as.data.frame(dge$E[,1:8])
normed <- as.data.frame(dge_norm$E[,1:8])
par(mfrow=c(1,2))
boxplot(raw, las=2, col=col, main="")
title(main="Unnormalized data",ylab="Log-RPKM")
boxplot(normed, las=2, col=col, main="")
title(main="Normalized data",ylab="Log-RPKM")
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
#Now, observe normalization on the filtered set of genes.
col <- brewer.pal(8, "Paired")
raw <- as.data.frame(dge_filt$E[,1:8])
normed <- as.data.frame(dge_final$E[,1:8])
par(mfrow=c(1,2))
boxplot(raw, las=2, col=col, main="")
title(main="Unnormalized data",ylab="Log-RPKM")
boxplot(normed, las=2, col=col, main="")
title(main="Normalized data",ylab="Log-RPKM")
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
#Now, do some quick MDS plotting to make sure this expression data separates out species properly.
species <- c("C", "C", "C", "C", "H", "H", "H", "H")
color <- c(rep("red", 4), rep("blue", 4))
par(mfrow=c(1,1))
plotMDS(dge_final$E[,1:8], labels=species, col=color, main="MDS Plot") #Shows separation of the species along the logFC dimension representing the majority of the variance--orthogonal check to PCA, and looks great!
| Version | Author | Date |
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| a02a602 | Ittai Eres | 2019-03-14 |
###Now, apply voom to get quality weights.
meta.exp.data <- data.frame("SP"=c("C", "C", "C", "C", "H", "H", "H", "H"), "SX"=c("M","M" ,"F","F","F", "M","M","F"))
SP <- factor(meta.exp.data$SP,levels = c("H","C"))
SX <- factor(meta.exp.data$SX, levels=c("M", "F"))
exp.design <- model.matrix(~0+SP+SX) #Include both species and sex as covariates.
colnames(exp.design) <- c("Human", "Chimp", "Sex")
weighted.data <- voom(dge_final, exp.design, plot=TRUE, normalize.method = "cyclicloess")
##Obtain rest of LM results, with particular eye to DE table!
vfit <- lmFit(weighted.data, exp.design)
efit <- eBayes(vfit)
mycon <- makeContrasts(HvC = Human-Chimp, levels = exp.design)
diff_species <- contrasts.fit(efit, mycon)
finalfit <- eBayes(diff_species)
detable <- topTable(finalfit, coef = 1, adjust.method = "BH", number = Inf, sort.by="none")
plotSA(efit, main="Final model: Mean−variance trend")
#Get lists of the DE and non-DE genes so I can run separate analyses on them at any point.
DEgenes <- detable$genes[which(detable$adj.P.Val<=0.05)]
nonDEgenes <- detable$genes[-which(detable$adj.P.Val<=0.05)]
#Rearrange RPKM and weight columns in voom object to be similar to the rest of my setup throughout in other dataframes.
weighted.data$E <- weighted.data$E[,c(8, 6, 1, 4, 7, 5, 2, 3)]
weighted.data$weights <- weighted.data$weights[,c(8, 6, 1, 4, 7, 5, 2, 3)]
RPKM <- as.data.frame(weighted.data$E)
RPKM$genes <- rownames(RPKM) #Just to match what I had before on midway2, about to write this out.
saveRDS(RPKM, file="output/IEE.RPKM.RDS")
saveRDS(weighted.data, file="output/IEE_voom_object.RDS") #write this object out, can then be read in with readRDS.#Overlap Between Hi-C Data and Orthogonal Gene Expression Data In this section I find the overlap between the final filtered set of Hi-C significant hits and genes picked up on by an orthogonal RNA-seq experiment in the same set of cell lines. I utilize an in-house curated set of orthologous genes between humans and chimpanzees. Given that the resolution of the data is 10kb, I choose a simple and conservative approach and use a 1-nucleotide interval at the start of each gene as a proxy for the promoter. I then take a conservative pass and only use genes that had direct overlap with a bin from the Hi-C significant hits data, with more motivation explained below.
#Now, read in filtered data from linear_modeling_QC.Rmd.
data.filtered <- fread("output/data.4.filtered.lm.QC", header=TRUE, data.table=FALSE, stringsAsFactors = FALSE, showProgress=FALSE)
meta.data <- data.frame("SP"=c("H", "H", "C", "C", "H", "H", "C", "C"), "SX"=c("F", "M", "M", "F", "M", "F", "M", "F"), "Batch"=c(1, 1, 1, 1, 2, 2, 2, 2))
###Grab DC and non-DC Example Interactions from Data### For creating FIG3 and FIGS7
great.indices <- which(data.filtered$sp_BH_pval<=0.05&data.filtered$dist_diff<=10000&data.filtered$Hdist<=150000&data.filtered$disc_species=="B"&data.filtered$Hdist>=50000)
mydat <- data.filtered[great.indices,]
mydat <- select(mydat, sp_BH_pval, sp_beta, Hchr, H1, H2, Cchr, C1, C2, Hmean, Cmean, dist_diff, Hdist, Cdist)
mydat <- mydat[order(mydat$sp_BH_pval),]
which.max(mydat$sp_beta)[1] 29weak.indices <- which(data.filtered$sp_BH_pval>=0.95&data.filtered$dist_diff<=10000&data.filtered$Hdist<=150000&data.filtered$disc_species=="B"&data.filtered$Hdist>=50000)
mydat <- data.filtered[weak.indices,]
mydat <- select(mydat, sp_BH_pval, sp_beta, Hchr, H1, H2, Cchr, C1, C2, Hmean, Cmean, dist_diff, Hdist, Cdist)
mydat <- mydat[order(mydat$Hmean, decreasing = TRUE),]
##
#TABLES1
#Write out data.filtered regions that are DC for supplementary table:
TABLES1 <- select(data.filtered, H1, H2, C1, C2, sp_BH_pval, sp_beta) %>% filter(., sp_BH_pval<=0.05)
TABLES1$Hchr <- gsub("-.*", "", TABLES1$H1)
TABLES1$H1start <- as.numeric(gsub(".*-", "", TABLES1$H1))
TABLES1$H1end <- TABLES1$H1start+10000
TABLES1$H2start <- as.numeric(gsub(".*-", "", TABLES1$H2))
TABLES1$H2end <- TABLES1$H2start+10000
TABLES1$Cchr <- gsub("-.*", "", TABLES1$C1)
TABLES1$C1start <- as.numeric(gsub(".*-", "", TABLES1$C1))
TABLES1$C1end <- TABLES1$C1start+10000
TABLES1$C2start <- as.numeric(gsub(".*-", "", TABLES1$C2))
TABLES1$C2end <- TABLES1$C2start+10000
select(TABLES1, Hchr, H1start, H1end, H2start, H2end, Cchr, C1start, C1end, C2start, C2end, sp_BH_pval, sp_beta) -> TABLES1
fwrite(TABLES1, "output/DC_regions.txt", col.names = TRUE, row.names = FALSE, sep = "\t")
#####GENE Hi-C Hit overlap: First, I obtain and rearrange the necessary files to get genes from both species and their overlaps with Hi-C bins.
#Read in necessary files: human and chimp orthologous genes from the meta ortho exon trios file. Then rearrange the columns of humgenes and chimpgenes to use group_by on them.
humgenes <- fread("data/Human_orthoexon_extended_info.txt", stringsAsFactors = FALSE, header=TRUE, data.table=FALSE)
chimpgenes <- fread("data/Chimp_orthoexon_extended_info.txt", stringsAsFactors = FALSE, header=TRUE, data.table=FALSE)
humgenes <- as.data.frame(humgenes[,c(1,5:8)])
chimpgenes <- as.data.frame(chimpgenes[,c(1,5:8)])
colnames(humgenes) <- c("genes", "Hchr", "Hstart", "Hend", "Hstrand")
colnames(chimpgenes) <- c("genes", "Cchr", "Cstart", "Cend", "Cstrand")
humgenes$Hchr <- paste("chr", humgenes$Hchr, sep="") #All properly formatted now!
#Bedtools groupby appears to have not worked properly to create files of single TSSs for genes from the meta ortho exons file, but this totally does! I've also utilized dplyr's group_by on the original file as well to ensure the same results. Now just making gene BED files that are 1-nt overlap at the very beginning of the first exon. Since I maintain strand information and will utilize it in bedtools closest-to, I am not concerned about whether the nt overlap goes the right direction or not. Note that if plyr is accidentally loaded AFTER dplyr, this will have issues (periods added at the end of start coords):
group_by(humgenes, genes) %>% summarise(Hchr=unique(Hchr), Hstart=as.numeric(min(Hstart)), Hend=Hstart+1, Hstrand=unique(Hstrand), holder=".") -> humgenes
group_by(chimpgenes, genes) %>% summarise(Cchr=unique(Cchr), Cstart=min(Cstart), Cend=Cstart+1, Cstrand=unique(Cstrand), holder=".") -> chimpgenes
#Format both of these properly for how the code was previously written.
humgenes <- humgenes[,c(2:4, 1, 6, 5)]
chimpgenes <- chimpgenes[,c(2:4,1, 6, 5)]
#Now, prep bed files from the filtered data for each bin, in order to run bedtools-closest on them with the human and chimp gene data. This is for getting each bin's proximity to TSS by overlapping with the dfs just made (humgenes and chimpgenes). In the end this set of bedfiles is fairly useless, because really it would be preferable to get rid of duplicates so that I can merely group_by on a given bin afterwards and left_join as necessary. So somewhat deprecated, but I keep it here still:
hbin1 <- data.frame(chr=data.filtered$Hchr, start=as.numeric(gsub("chr.*-", "", data.filtered$H1)), end=as.numeric(gsub("chr.*-", "", data.filtered$H1))+10000)
hbin2 <- data.frame(chr=data.filtered$Hchr, start=as.numeric(gsub("chr.*-", "", data.filtered$H2)), end=as.numeric(gsub("chr.*-", "", data.filtered$H2))+10000)
cbin1 <- data.frame(chr=data.filtered$Cchr, start=as.numeric(gsub("chr.*-", "", data.filtered$C1)), end=as.numeric(gsub("chr.*-", "", data.filtered$C1))+10000)
cbin2 <- data.frame(chr=data.filtered$Cchr, start=as.numeric(gsub("chr.*-", "", data.filtered$C2)), end=as.numeric(gsub("chr.*-", "", data.filtered$C2))+10000)
#In most analyses, it will make more sense to have a single bed file for both sets of bins, and remove all duplicates. I create that here:
hbins <- rbind(hbin1[!duplicated(hbin1),], hbin2[!duplicated(hbin2),])
hbins <- hbins[!duplicated(hbins),]
cbins <- rbind(cbin1[!duplicated(cbin1),], cbin2[!duplicated(cbin2),])
cbins <- cbins[!duplicated(cbins),]
#Need to reformat hbins here for proper bedtools use:
hbins$chr <- gsub("Chr. ", "chr", hbins$chr) #All good.
#Now, write all of these files out for analysis with bedtools.
options(scipen=999)
#write.table(hbin1, "~/Desktop/Hi-C/gene_expression/10kb_filt_overlaps/unsorted/hbin1.bed", quote = FALSE, sep="\t", row.names = FALSE, col.names=FALSE)
#write.table(hbin2, "~/Desktop/Hi-C/gene_expression/10kb_filt_overlaps/unsorted/hbin2.bed", quote = FALSE, sep="\t", row.names = FALSE, col.names=FALSE)
#write.table(cbin1, "~/Desktop/Hi-C/gene_expression/10kb_filt_overlaps/unsorted/cbin1.bed", quote = FALSE, sep="\t", row.names = FALSE, col.names=FALSE)
#write.table(cbin2, "~/Desktop/Hi-C/gene_expression/10kb_filt_overlaps/unsorted/cbin2.bed", quote = FALSE, sep="\t", row.names = FALSE, col.names=FALSE)
write.table(humgenes, "data/hic_gene_overlap/humgenes.bed", quote=FALSE, sep="\t", row.names=FALSE, col.names=FALSE)
write.table(chimpgenes, "data/hic_gene_overlap/chimpgenes.bed", quote=FALSE, sep="\t", row.names=FALSE, col.names=FALSE)
write.table(hbins, "data/hic_gene_overlap/hbins.bed", quote=FALSE, sep="\t", row.names=FALSE, col.names=FALSE)
write.table(cbins, "data/hic_gene_overlap/cbins.bed", quote=FALSE, sep="\t", row.names=FALSE, col.names=FALSE)
options(scipen=0)
#Read in new, simpler bedtools closest files for genes. This is after running two commands, after sorting the files w/ sort -k1,1 -k2,2n in.bed > out.bed:
#bedtools closest -D a -a cgenes.sorted.bed -b cbins.sorted.bed > cgene.hic.overlap
#bedtools closest -D a -a hgenes.sorted.bed -b hbins.sorted.bed > hgene.hic.overlap
#A bash file showing this is included in the data/hic_gene_overlap directory (overlapper.sh)
hgene.hic <- fread("data/hic_gene_overlap/hgene.hic.overlap", header=FALSE, stringsAsFactors = FALSE, data.table=FALSE)
cgene.hic <- fread("data/hic_gene_overlap/cgene.hic.overlap", header=FALSE, stringsAsFactors = FALSE, data.table=FALSE)
#Visualize the overlap of genes with bins and see how many genes we get back!
hum.genelap <- data.frame(overlap=seq(0, 100000, 1000), perc.genes = NA, tot.genes=NA)
for(row in 1:nrow(hum.genelap)){
hum.genelap$perc.genes[row] <- sum(abs(hgene.hic$V10)<=hum.genelap$overlap[row])/length(hgene.hic$V10)
hum.genelap$tot.genes[row] <- sum(abs(hgene.hic$V10)<=hum.genelap$overlap[row])
}
c.genelap <- data.frame(overlap=seq(0, 100000, 1000), perc.genes=NA, tot.genes=NA)
for(row in 1:nrow(c.genelap)){
c.genelap$perc.genes[row] <- sum(abs(cgene.hic$V10)<=c.genelap$overlap[row])/length(cgene.hic$V10)
c.genelap$tot.genes[row] <- sum(abs(cgene.hic$V10)<=c.genelap$overlap[row])
}
c.genelap$type <- "chimp"
hum.genelap$type <- "human"
#Examine what the potential gains are here if we are more lenient about the overlap/closeness to a TSS...
ggoverlap <- rbind(hum.genelap, c.genelap)
ggplot(data=ggoverlap) + geom_line(aes(x=overlap, y=perc.genes*100, color=type)) + ggtitle("Percent of Total Genes Picked Up | Min. Distance from TSS") + xlab("Distance from TSS") + ylab("Percentage of genes in ortho exon trios file (~44k)") + scale_color_discrete(guide=guide_legend(title="Species")) + coord_cartesian(xlim=c(0, 30000)) + scale_x_continuous(breaks=seq(0, 30000, 5000))
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=ggoverlap) + geom_line(aes(x=overlap, y=tot.genes, color=type)) + ggtitle("Total # Genes Picked Up | Min. Distance from TSS") + xlab("Distance from TSS") + ylab("Total # of Genes Picked up On (of ~44k)") + scale_color_discrete(guide=guide_legend(title="Species")) + coord_cartesian(xlim=c(0, 30000)) + scale_x_continuous(breaks=seq(0, 30000, 5000))
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
#Start with a conservative pass--only take those genes that had an actual overlap with a bin, not ones that were merely close to one. Allowing some leeway to include genes that are within 1kb, 2kb, 3kb etc. of a Hi-C bin adds an average of ~800 genes per 1kb. We can also examine the distribution manually to motivate this decision:
quantile(abs(hgene.hic$V10), probs=seq(0, 1, 0.025)) 0% 2.5% 5% 7.5% 10% 12.5% 15%
0.0 0.0 0.0 0.0 0.0 0.0 0.0
17.5% 20% 22.5% 25% 27.5% 30% 32.5%
0.0 0.0 0.0 0.0 0.0 0.0 0.0
35% 37.5% 40% 42.5% 45% 47.5% 50%
0.0 0.0 0.0 0.0 0.0 0.0 0.0
52.5% 55% 57.5% 60% 62.5% 65% 67.5%
0.0 0.0 0.0 435.4 1741.0 2968.2 4324.0
70% 72.5% 75% 77.5% 80% 82.5% 85%
6226.2 8868.0 12336.0 16610.0 22034.4 29271.5 38300.2
87.5% 90% 92.5% 95% 97.5% 100%
50248.0 67698.4 92599.2 135125.0 243068.9 7581822.0 quantile(abs(cgene.hic$V10), probs=seq(0, 1, 0.025)) 0% 2.5% 5% 7.5% 10% 12.5%
0.0 0.0 0.0 0.0 0.0 0.0
15% 17.5% 20% 22.5% 25% 27.5%
0.0 0.0 0.0 0.0 0.0 0.0
30% 32.5% 35% 37.5% 40% 42.5%
0.0 0.0 0.0 0.0 0.0 0.0
45% 47.5% 50% 52.5% 55% 57.5%
0.0 0.0 0.0 0.0 0.0 0.0
60% 62.5% 65% 67.5% 70% 72.5%
237.4 1507.0 2800.0 4237.4 6125.6 8750.4
75% 77.5% 80% 82.5% 85% 87.5%
12181.0 16648.0 22317.4 29420.0 38490.0 50656.0
90% 92.5% 95% 97.5% 100%
67470.4 92415.3 136417.4 243285.0 21022081.0 #Note I looked at proportion of overlap with DE and with non-DE genes just for curiosity, and roughly 66% of the DE genes have overlap with a Hi-C bin while roughly 70% of the non-DE genes do. Since this result isn't particularly interesting I have collapsed that analysis here.
#Also are interested in seeing how this differs for DE and non-DE genes.
dehgene.hic <- hgene.hic[which(hgene.hic$V4 %in% DEgenes),]
decgene.hic <- cgene.hic[which(cgene.hic$V4 %in% DEgenes),]
nondehgene.hic <- hgene.hic[which(hgene.hic$V4 %in% nonDEgenes),]
nondecgene.hic <- cgene.hic[which(cgene.hic$V4 %in% nonDEgenes),]
sum(dehgene.hic$V10==0)[1] 1402sum(nondehgene.hic$V10==0)[1] 6367quantile(abs(dehgene.hic$V10), probs=seq(0, 1, 0.025)) 0% 2.5% 5% 7.5% 10% 12.5%
0.000 0.000 0.000 0.000 0.000 0.000
15% 17.5% 20% 22.5% 25% 27.5%
0.000 0.000 0.000 0.000 0.000 0.000
30% 32.5% 35% 37.5% 40% 42.5%
0.000 0.000 0.000 0.000 0.000 0.000
45% 47.5% 50% 52.5% 55% 57.5%
0.000 0.000 0.000 0.000 0.000 0.000
60% 62.5% 65% 67.5% 70% 72.5%
0.000 0.000 0.000 142.375 1302.000 2540.750
75% 77.5% 80% 82.5% 85% 87.5%
3770.750 5512.875 7596.000 11152.750 16301.000 21797.625
90% 92.5% 95% 97.5% 100%
31613.500 46132.250 67401.250 131755.375 5837313.000 quantile(abs(nondehgene.hic$V10), probs=seq(0, 1, 0.025)) 0% 2.5% 5% 7.5% 10% 12.5%
0.000 0.000 0.000 0.000 0.000 0.000
15% 17.5% 20% 22.5% 25% 27.5%
0.000 0.000 0.000 0.000 0.000 0.000
30% 32.5% 35% 37.5% 40% 42.5%
0.000 0.000 0.000 0.000 0.000 0.000
45% 47.5% 50% 52.5% 55% 57.5%
0.000 0.000 0.000 0.000 0.000 0.000
60% 62.5% 65% 67.5% 70% 72.5%
0.000 0.000 0.000 0.000 0.000 781.900
75% 77.5% 80% 82.5% 85% 87.5%
2000.000 3054.650 4423.400 6403.575 9956.100 15363.875
90% 92.5% 95% 97.5% 100%
23179.700 34837.575 54701.300 103895.750 6845725.000 And we can see that the majority of the genes (57.5%) have direct overlap with a bin. I’ll thus start with one very conservative set with only genes that have direct overlap with a bin. Later I may return to this and add and another slightly more lenient bin capturing ~10% more of the genes by allowing +/- 5kb of wiggle room.
#Linear Modeling Annotation In this next section I simply add information obtained from linear modeling on the Hi-C interaction frequencies to the appropriate genes having overlap with Hi-C bins. Because one Hi-C bin frequently shows up many times in the data, this means I must choose some kind of summary for Hi-C contact frequencies and linear modeling annotations for each gene. I toy with a variety of these summaries here, including choosing the minimum FDR contact, the maximum beta contact, the upstream contact, or summarizing all a bin’s contacts with the weighted Z-combine method or median FDR values.
hgene.hic.overlap <- filter(hgene.hic, V10==0) #Still leaves a solid ~26k genes.
cgene.hic.overlap <- filter(cgene.hic, V10==0) #Still leaves a solid ~26k genes.
#Add a column to both dfs indicating where along a bin the gene in question is found (from 0-10k):
hgene.hic.overlap$bin_pos <- abs(hgene.hic.overlap$V8-hgene.hic.overlap$V2)
cgene.hic.overlap$bin_pos <- abs(cgene.hic.overlap$V8-cgene.hic.overlap$V2)
#Rearrange columns and create another column of the bin ID.
hgene.hic.overlap <- hgene.hic.overlap[,c(4, 7:9, 6, 11, 1:2)]
hgene.hic.overlap$HID <- paste(hgene.hic.overlap$V7, hgene.hic.overlap$V8, sep="-")
cgene.hic.overlap <- cgene.hic.overlap[,c(4, 7:9, 6, 11, 1:2)]
cgene.hic.overlap$CID <- paste(cgene.hic.overlap$V7, cgene.hic.overlap$V8, sep="-")
colnames(hgene.hic.overlap) <- c("genes", "HiC_chr", "H1start", "H1end", "Hstrand", "bin_pos", "genechr", "genepos", "HID")
colnames(cgene.hic.overlap) <- c("genes", "HiC_chr", "C1start", "C1end", "Cstrand", "bin_pos", "genechr", "genepos", "CID")
#Gets me an hfinal table with a lot of the information concatenated together--now need the same thing for chimps, only to get the n contacts (since this could differ!)
hbindf <- select(data.filtered, "H1", "H2", "ALLvar", "SE", "sp_beta", "sp_pval", "sp_BH_pval", "Hdist")
names(hbindf) <- c("HID", "HID2", "ALLvar", "SE", "sp_beta", "sp_pval", "sp_BH_pval", "distance") #I have confirmed that all the HID2s are higher numbered coordinates than the HID1s, the only instance in which this isn't the case is when the two bins are identical (this should have been filtered out long before now).
hbindf <- hbindf[(which(hbindf$dist!=0)),] #Removes pairs where the same bin represents both mates. These instances occur exclusively when liftOver of the genomic coordinates from one species to another, and the subsequent rounding to the nearest 10kb, results in a contact between adjacent bins in one species being mapped as a contact between the same bin in the other species. Because there are less than 50 instances of this total in the dataset I simply remove it here without further worry.
#Remembering that all the first mates in the pair are lower coordinates than the second mates:
#This works for getting the FDR of closest downstream hits for the first column. Technically this would also make these the closest upstream hits for any bins that are UNIQUE and NOT REPEATED to the second column. For unique bins in this column, they have no upstream hits, and this gets their downstream hits. I can do the same thing but on the second set of IDs to get the potential upstream hits for any bins, then do a full_join on the two to get everything! Many of these metrics need to be done on a duplicated df to ensure I have all copies of a bin in one column, but the upstream and downstream analyses need to be run separately.
group_by(hbindf, HID) %>% summarise(DS_bin=HID2[which.min(distance)], DS_FDR=sp_BH_pval[which.min(distance)], DS_dist=distance[which.min(distance)]) -> hbin1.downstream
group_by(hbindf, HID2) %>% summarise(US_bin=HID[which.min(distance)], US_FDR=sp_BH_pval[which.min(distance)], US_dist=distance[which.min(distance)]) -> hbin2.upstream
colnames(hbin2.upstream) <- c("HID", "US_bin", "US_FDR", "US_dist")
Hstreams <- full_join(hbin1.downstream, hbin2.upstream, by="HID")
#Now, need to create a df with all the hits duplicated (but columns reversed) to account for duplicated bins in each column. This is better for any analysis that shares information across the interactions (minimums, sums, means, medians, etc.)
hbindf.flip <- hbindf[,c(2, 1, 3:7)]
colnames(hbindf.flip)[1:2] <- c("HID", "HID2")
hbindf_x2 <- rbind(hbindf[,1:7], hbindf.flip) #It's worth noting that a version of this with duplicates removed would be very useful for enrichment analyses...
#Now, use group_by from dplyr to combine information for a given bin across all its Hi-C contacts. Here I'll be pulling out contact, p-value, and bin with minimum FDR, its beta; the median FDR; the maximum beta and its FDR and bin; and a weighted combination method for p-values for species from linear modeling. This is based off of (http://onlinelibrary.wiley.com/doi/10.1111/j.1420-9101.2005.00917.x/full) under the assumption $s2.post is the actual error variance. (forthcoming)
group_by(hbindf_x2, HID) %>% summarise(min_FDR_bin=HID2[which.min(sp_BH_pval)], min_FDR=min(sp_BH_pval), min_FDR_pval=sp_pval[which.min(sp_BH_pval)], min_FDR_B=sp_beta[which.min(sp_BH_pval)], median_FDR=median(sp_BH_pval), weighted_Z.ALLvar=pnorm((sum((1/ALLvar)*((qnorm(1-sp_pval))))/sqrt(sum((1/ALLvar)^2))), lower.tail=FALSE), weighted_Z.s2post=pnorm(sum((1/(SE^2))*qnorm(1-sp_pval))/sqrt(sum(1/SE^2)), lower.tail=FALSE), fisher=-2*sum(log(sp_pval)), numcontacts=n(), max_B_bin=HID2[which.max(abs(sp_beta))], max_B_FDR=sp_BH_pval[which.max(abs(sp_beta))], max_B=sp_beta[which.max(abs(sp_beta))]) -> hbin.info
#Now, full_join the hbin.info and Hstreams dfs, incorporating all the information about the hbins in my data:
full_join(hbin.info, Hstreams, by="HID") -> hbin.full.info
#Now I combine the gene overlap tables and the full information tables for the human genes and bins!
left_join(hgene.hic.overlap, hbin.full.info, by="HID") -> humgenes.hic.full
colnames(humgenes.hic.full)[1:5] <- c("genes", "Hchr", "Hstart", "Hend", "Hstrand") #Fix column names for what was just created
###Now, do the whole thing over again for chimps, and then combine with the human gene overlap before joining on detable!
#Gets me a cfinal table with a lot of the information concatenated together.
cbindf <- select(data.filtered, "C1", "C2", "ALLvar", "SE", "sp_beta", "sp_pval", "sp_BH_pval", "Cdist") #Pulling out cols C1, C2, ALLvar, SE, sp_beta, sp_pval, sp_BH_pval, and Cdist
names(cbindf) <- c("CID", "CID2", "ALLvar", "SE", "sp_beta", "sp_pval", "sp_BH_pval", "distance") #I have confirmed that all the CID2s are higher numbered coordinates than the CID1s, the only instance in which this isn't the case is when the two bins are identical (this should have been filtered out long before now).
cbindf <- cbindf[(which(cbindf$dist!=0)),] #Removes rows from the cbindf that REALLY shouldn't be there to begin with. There are ~620 hits like this.
#Remembering that all the first mates in the pair are lower coordinates than the second mates:
#This works for getting the FDR of closest downstream hits for the first column. Technically this would also make these the closest upstream hits for any bins that are UNIQUE and NOT REPEATED to the second column. For unique bins in this column, they have no upstream hits, and this gets their downstream hits. I can do the same thing but on the second set of IDs to get the potential upstream hits for any bins, then do a full_join on the two to get everything! Many of these metrics need to be done on a duplicated df to ensure I have all copies of a bin in one column, but the upstream and downstream analyses need to be run separately.
group_by(cbindf, CID) %>% summarise(DS_bin=CID2[which.min(distance)], DS_FDR=sp_BH_pval[which.min(distance)], DS_dist=distance[which.min(distance)]) -> cbin1.downstream
group_by(cbindf, CID2) %>% summarise(US_bin=CID[which.min(distance)], US_FDR=sp_BH_pval[which.min(distance)], US_dist=distance[which.min(distance)]) -> cbin2.upstream
colnames(cbin2.upstream) <- c("CID", "US_bin", "US_FDR", "US_dist")
Cstreams <- full_join(cbin1.downstream, cbin2.upstream, by="CID")
#Now, need to create a df with all the hits duplicated (but columns reversed) to account for duplicated bins in each column. This is better for any analysis that shares information across the interactions (minimums, sums, means, medians, etc.)
cbindf.flip <- cbindf[,c(2, 1, 3:7)]
colnames(cbindf.flip)[1:2] <- c("CID", "CID2")
cbindf_x2 <- rbind(cbindf[,1:7], cbindf.flip)
#Group by again for chimp hits as was done for humans above.
group_by(cbindf_x2, CID) %>% summarise(min_FDR_bin=CID2[which.min(sp_BH_pval)], min_FDR=min(sp_BH_pval), min_FDR_B=sp_beta[which.min(sp_BH_pval)], median_FDR=median(sp_BH_pval), weighted_Z.ALLvar=pnorm((sum((1/ALLvar)*((qnorm(1-sp_pval))))/sqrt(sum((1/ALLvar)^2))), lower.tail=FALSE), weighted_Z.s2post=pnorm(sum((1/(SE^2))*qnorm(1-sp_pval))/sqrt(sum(1/SE^2)), lower.tail=FALSE), fisher=-2*sum(log(sp_pval)), numcontacts=n(), max_B_bin=CID2[which.max(abs(sp_beta))], max_B_FDR=sp_BH_pval[which.max(abs(sp_beta))], max_B=sp_beta[which.max(abs(sp_beta))]) -> cbin.info
#Now, full_join the cbin.info and Cstreams dfs, incorporating all the information about the cbins in my data:
full_join(cbin.info, Cstreams, by="CID") -> cbin.full.info
#Now I combine the gene overlap tables and the full information tables for the human genes and bins!
left_join(cgene.hic.overlap, cbin.full.info, by="CID") -> chimpgenes.hic.full
colnames(chimpgenes.hic.full)[1:5] <- c("genes", "Hchr", "Hstart", "Hend", "Hstrand") #Fix column names for what was just created
#Now, combine chimpgenes.hic.full and humgenes.hic.full before a final left_join on detable:
full_join(humgenes.hic.full, chimpgenes.hic.full, by="genes", suffix=c(".H", ".C")) -> genes.hic.info
left_join(detable, genes.hic.info, by="genes") -> gene.hic.overlap.info
#Clean this dataframe up, removing rows where there is absolutely no Hi-C information for the gene.
filt.indices <- rowSums(is.na(gene.hic.overlap.info)) #51 NA values are found when there is absolutely no Hi-C information.
filt.indices <- which(filt.indices==51)
gene.hic.filt <- gene.hic.overlap.info[-filt.indices,] #Still leaves a solid 8,174 genes. Note that I will have to choose human or chimp values here for many of these columns, as not all of the values are the same (and many are missing in one species relative to the other). In some cases, may be able to just take minimum or maximum value from either in order to get at what I want.
saveRDS(gene.hic.filt, "output/gene.hic.filt.RDS")#Differential Expression-Differential Hi-C Enrichment Analyses Now I look for enrichment of DHi-C in DE genes using a variety of different metrics to call DHi-C. I now look to see if genes that are differentially expressed are also differential in Hi-C contacts (DHi-C). That is to say, are differentially expressed genes enriched in their overlapping bins for Hi-C contacts that are also differential between the species? To do this I utilize p-values from my prior linear modeling as well as previous RNA-seq analysis. I construct a function to calculate proportions of DE and DHi-C genes, as well as a function to plot this out in a variety of different ways.
####Enrichment analyses!
#A function for calculating proportion of DE genes that are DHi-C under a variety of different paradigms. Accounts for when no genes are DHi-C and when all genes are DHi-C. Returns the proportion of DE genes that are also DHi-C, as well as the expected proportion based on conditional probability alone.
prop.calculator <- function(de.vec, hic.vec, i, k){
my.result <- data.frame(prop=NA, exp.prop=NA, chisq.p=NA, Dneither=NA, DE=NA, DHiC=NA, Dboth=NA)
bad.indices <- which(is.na(hic.vec)) #First obtain indices where Hi-C info is missing, if there are any, then remove from both vectors.
if(length(bad.indices>0)){
de.vec <- de.vec[-bad.indices]
hic.vec <- hic.vec[-bad.indices]}
de.vec <- ifelse(de.vec<=i, 1, 0)
hic.vec <- ifelse(hic.vec<=k, 1, 0)
if(sum(hic.vec, na.rm=TRUE)==0){#The case where no genes show up as DHi-C.
my.result[1,] <- c(0, 0, 0, sum(de.vec==0, na.rm=TRUE), sum(de.vec==1, na.rm=TRUE), 0, 0) #Since no genes are DHi-C, the proportion is 0 and our expectation is 0, set p-val=0 since it's irrelevant.
}
else if(sum(hic.vec)==length(hic.vec)){ #The case where every gene shows up as DHi-C
my.result[1,] <- c(1, 1, 0, 0, 0, sum(hic.vec==1&de.vec==0, na.rm = TRUE), sum(de.vec==1&hic.vec==1, na.rm=TRUE)) #If every gene is DHi-C, the observed proportion of DE genes DHi-C is 1, and the expected proportion of DE genes also DHi-C would also be 1 (all DE genes are DHi-C, since all genes are). Again set p-val to 0 since irrelevant comparison.
}
else{#The typical case, where we get an actual table
mytable <- table(as.data.frame(cbind(de.vec, hic.vec)))
my.result[1,1] <- mytable[2,2]/sum(mytable[2,]) #The observed proportion of DE genes that are also DHi-C. # that are both/total # DE
my.result[1,2] <- (((sum(mytable[2,])/sum(mytable))*((sum(mytable[,2])/sum(mytable))))*sum(mytable))/sum(mytable[2,]) #The expected proportion: (p(DE) * p(DHiC)) * total # genes / # DE genes
my.result[1,3] <- chisq.test(mytable)$p.value
my.result[1,4] <- mytable[1,1]
my.result[1,5] <- mytable[2,1]
my.result[1,6] <- mytable[1,2]
my.result[1,7] <- mytable[2,2]
}
return(my.result)
}
#This is a function that computes observed and expected proportions of DE and DHiC enrichments, and spits out a variety of different visualizations for them. As input it takes a dataframe, the names of its DHiC and DE p-value columns, and a name to represent the type of Hi-C contact summary for the gene that ends up on the x-axis of all the plots.
enrichment.plotter <- function(df, HiC_col, DE_col, xlab, xmax=0.3, i=c(0.01, 0.025, 0.05, 0.075, 0.1), k=seq(0.01, 1, 0.01), recip=F){
enrich.table <- data.frame(DEFDR = c(rep(i[1], 100), rep(i[2], 100), rep(i[3], 100), rep(i[4], 100), rep(i[5], 100)), DHICFDR=rep(k, 5), prop.obs=NA, prop.exp=NA, chisq.p=NA, Dneither=NA, DE=NA, DHiC=NA, Dboth=NA)
for(de.FDR in i){
for(hic.FDR in k){
enrich.table[which(enrich.table$DEFDR==de.FDR&enrich.table$DHICFDR==hic.FDR), 3:9] <- prop.calculator(df[,DE_col], df[,HiC_col], de.FDR, hic.FDR)
}
}
des.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=prop.obs, group=as.factor(DEFDR), color=as.factor(DEFDR))) +geom_line()+ geom_line(aes(y=prop.exp), linetype="dashed", size=0.5) + ggtitle("Enrichment of DC in DE Genes") + xlab(xlab) + ylab("Proportion of DE genes that are DC") + guides(color=guide_legend(title="FDR for DE Genes"))
dhics.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dboth+DHiC), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + geom_line(aes(y=(((((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth)))*(Dneither+DE+DHiC+Dboth))/(DHiC+Dboth))), linetype="dashed") + ylab("Proportion of DC genes that are DE") +xlab(xlab) + ggtitle("Enrichment of DE in DC Genes") + coord_cartesian(xlim=c(0, xmax)) + guides(color=guide_legend(title="DE FDR"))
joint.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dneither+DE+DHiC+Dboth), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + ylab("Proportion of ALL Genes both DE & DHi-C") + xlab(xlab) + geom_line(aes(y=((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth))), linetype="dashed") + ggtitle("Enrichment of Joint DE & DHi-C in All Genes")
chisq.p <- ggplot(data=enrich.table, aes(x=DHICFDR, y=-log10(chisq.p), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_point() + geom_hline(yintercept=-log10(0.05), color="red") + ggtitle("Chi-squared Test P-values for Enrichment of DC in DE Genes") + xlab(xlab) + ylab("-log10(chi-squared p-values)") + coord_cartesian(xlim=c(0, xmax)) + guides(color=guide_legend(title="DE FDR"))
if(recip==TRUE){
des.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=prop.obs, group=as.factor(DEFDR), color=as.factor(DEFDR))) +geom_line()+ geom_line(aes(y=prop.exp), linetype="dashed", size=0.5) + ggtitle("Enrichment of DE in DC Genes") + xlab(xlab) + ylab("Proportion of DC genes that are DE") + guides(color=guide_legend(title="FDR for DC Genes"))
dhics.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dboth+DHiC), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + geom_line(aes(y=(((((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth)))*(Dneither+DE+DHiC+Dboth))/(DHiC+Dboth))), linetype="dashed") + ylab("Proportion of DE genes that are DC") +xlab(xlab) + ggtitle("Enrichment of DC in DE Genes") + coord_cartesian(xlim=c(0, xmax)) + guides(color=guide_legend(title="DC FDR"))
joint.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dneither+DE+DHiC+Dboth), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + ylab("Proportion of ALL Genes both DE & DHi-C") + xlab(xlab) + geom_line(aes(y=((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth))), linetype="dashed") + ggtitle("Enrichment of Joint DE & DHi-C in All Genes")
chisq.p <- ggplot(data=enrich.table, aes(x=DHICFDR, y=-log10(chisq.p), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_point() + geom_hline(yintercept=-log10(0.05), color="red") + ggtitle("Chi-squared Test P-values for Enrichment of DE in DC Genes") + xlab(xlab) + ylab("-log10(chi-squared p-values)") + coord_cartesian(xlim=c(0, xmax)) + guides(color=guide_legend(title="DC FDR"))
}
print(des.enriched)
print(dhics.enriched)
print(joint.enriched)
print(chisq.p)
print(enrich.table)#[which(enrich.table$DEFDR==0.1),]) #Added to figure out comparison for the paper.
}
#Visualization of enrichment of DE/DC in one another. For most of these, using the gene.hic.filt df is sufficient as their Hi-C FDR numbers are the same. For the upstream genes it's a little more complicated because gene.hic.filt doesn't incorporate strand information on the genes, so use the specific US dfs for that, with the USFDR column.
#FIG6
enrichment.plotter(gene.hic.filt, "min_FDR.H", "adj.P.Val", "Minimum FDR of Hi-C Contacts Overlapping Gene", xmax=1) #FIG6A/BWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.04255319 0.04598145 0.80065262 6957 450 337
2 0.010 0.02 0.05531915 0.05963421 0.75877770 6857 444 437
3 0.010 0.03 0.06595745 0.06942298 0.83261502 6786 439 508
4 0.010 0.04 0.08723404 0.08037094 0.63327774 6711 429 583
5 0.010 0.05 0.09787234 0.09222050 0.72283415 6624 424 670
6 0.010 0.06 0.11276596 0.10419887 0.58278920 6538 417 756
7 0.010 0.07 0.13191489 0.11424523 0.24296869 6469 408 825
8 0.010 0.08 0.15319149 0.12660999 0.08609382 6383 398 911
9 0.010 0.09 0.16808511 0.14026275 0.08479409 6284 391 1010
10 0.010 0.10 0.18510638 0.15108192 0.03953562 6208 383 1086
11 0.010 0.11 0.19574468 0.16280268 0.05344168 6122 378 1172
12 0.010 0.12 0.20638298 0.17709943 0.09824852 6016 373 1278
13 0.010 0.13 0.21914894 0.18907779 0.09753188 5929 367 1365
14 0.010 0.14 0.23404255 0.20260175 0.09094805 5831 360 1463
15 0.010 0.15 0.25319149 0.21586811 0.04869491 5737 351 1557
16 0.010 0.16 0.26595745 0.22797527 0.04903338 5649 345 1645
17 0.010 0.17 0.28723404 0.24252962 0.02276395 5546 335 1748
18 0.010 0.18 0.30000000 0.25540958 0.02558375 5452 329 1842
19 0.010 0.19 0.31063830 0.27009274 0.04671135 5343 324 1951
20 0.010 0.20 0.33404255 0.28657908 0.02171933 5226 313 2068
21 0.010 0.21 0.34468085 0.29752705 0.02413636 5146 308 2148
22 0.010 0.22 0.36382979 0.31491499 0.02120831 5020 299 2274
23 0.010 0.23 0.38297872 0.32843895 0.01087114 4924 290 2370
24 0.010 0.24 0.39787234 0.34453890 0.01388704 4806 283 2488
25 0.010 0.25 0.40638298 0.36128284 0.04032397 4680 279 2614
26 0.010 0.26 0.42340426 0.37854199 0.04340600 4554 271 2740
27 0.010 0.27 0.43829787 0.39657393 0.06301265 4421 264 2873
28 0.010 0.28 0.45106383 0.41409067 0.10297024 4291 258 3003
29 0.010 0.29 0.45957447 0.42928903 0.18667669 4177 254 3117
30 0.010 0.30 0.47872340 0.44448738 0.13539529 4068 245 3226
31 0.010 0.31 0.49574468 0.46032973 0.12318869 3953 237 3341
32 0.010 0.32 0.50638298 0.47359608 0.15529178 3855 232 3439
33 0.010 0.33 0.52127660 0.49330242 0.22862114 3709 225 3585
34 0.010 0.34 0.53191489 0.50901597 0.32860267 3592 220 3702
35 0.010 0.35 0.54680851 0.52357032 0.32068358 3486 213 3808
36 0.010 0.36 0.55957447 0.53464709 0.28458022 3406 207 3888
37 0.010 0.37 0.57659574 0.55229263 0.29587452 3277 199 4017
38 0.010 0.38 0.58510638 0.57109737 0.55852360 3135 195 4159
39 0.010 0.39 0.60425532 0.58861412 0.50759008 3008 186 4286
40 0.010 0.40 0.62340426 0.60497166 0.42679569 2890 177 4404
41 0.010 0.41 0.63617021 0.61862442 0.44786720 2790 171 4504
42 0.010 0.42 0.65957447 0.63369397 0.24927971 2684 160 4610
43 0.010 0.43 0.66595745 0.64837713 0.43910651 2573 157 4721
44 0.010 0.44 0.68297872 0.66138588 0.33190722 2480 149 4814
45 0.010 0.45 0.70425532 0.67928903 0.25203249 2351 139 4943
46 0.010 0.46 0.70638298 0.69178259 0.51201850 2255 138 5039
47 0.010 0.47 0.72127660 0.70376095 0.42029215 2169 131 5125
48 0.010 0.48 0.72765957 0.71470891 0.55599688 2087 128 5207
49 0.010 0.49 0.74893617 0.72501288 0.25216518 2017 118 5277
50 0.010 0.50 0.75957447 0.73634724 0.26055053 1934 113 5360
51 0.010 0.51 0.77234043 0.74510562 0.17920816 1872 107 5422
52 0.010 0.52 0.78723404 0.75643998 0.12132559 1791 100 5503
53 0.010 0.53 0.80000000 0.76738794 0.09488613 1712 94 5582
54 0.010 0.54 0.80000000 0.77460072 0.19268893 1656 94 5638
55 0.010 0.55 0.80638298 0.78554869 0.28130408 1574 91 5720
56 0.010 0.56 0.81489362 0.79224626 0.23406830 1526 87 5768
57 0.010 0.57 0.82127660 0.79894384 0.23524188 1477 84 5817
58 0.010 0.58 0.83191489 0.80757342 0.18658093 1415 79 5879
59 0.010 0.59 0.83829787 0.81543019 0.20871901 1357 76 5937
60 0.010 0.60 0.84468085 0.82290057 0.22482961 1302 73 5992
61 0.010 0.61 0.84468085 0.82972694 0.40848811 1249 73 6045
62 0.010 0.62 0.85531915 0.83681092 0.29103305 1199 68 6095
63 0.010 0.63 0.85744681 0.84312210 0.41475289 1151 67 6143
64 0.010 0.64 0.86595745 0.84981968 0.34528784 1103 63 6191
65 0.010 0.65 0.86808511 0.85793405 0.56043576 1041 62 6253
66 0.010 0.66 0.87446809 0.86476043 0.57183797 991 59 6303
67 0.010 0.67 0.88297872 0.87107161 0.46923996 946 55 6348
68 0.010 0.68 0.88723404 0.87493560 0.44746542 918 53 6376
69 0.010 0.69 0.89148936 0.88060278 0.49804378 876 51 6418
70 0.010 0.70 0.90425532 0.88730036 0.26101231 830 45 6464
71 0.010 0.71 0.91063830 0.89167955 0.19778405 799 42 6495
72 0.010 0.72 0.91276596 0.89760433 0.29829145 754 41 6540
73 0.010 0.73 0.91276596 0.90069552 0.41041195 730 41 6564
74 0.010 0.74 0.91276596 0.90494590 0.60637991 697 41 6597
75 0.010 0.75 0.91489362 0.90868109 0.68931937 669 40 6625
76 0.010 0.76 0.92127660 0.91228748 0.53088677 644 37 6650
77 0.010 0.77 0.92340426 0.91834106 0.74392509 598 36 6696
78 0.010 0.78 0.93617021 0.92323545 0.31858392 566 30 6728
79 0.010 0.79 0.94042553 0.92658423 0.27318268 542 28 6752
80 0.010 0.80 0.94042553 0.93044822 0.43320979 512 28 6782
81 0.010 0.81 0.94468085 0.93379701 0.37702207 488 26 6806
82 0.010 0.82 0.95106383 0.93766100 0.25365094 461 23 6833
83 0.010 0.83 0.95319149 0.94268418 0.36350870 423 22 6871
84 0.010 0.84 0.95744681 0.94654817 0.32809305 395 20 6899
85 0.010 0.85 0.95957447 0.94938176 0.35162900 374 19 6920
86 0.010 0.86 0.96382979 0.95260175 0.28466184 351 17 6943
87 0.010 0.87 0.96595745 0.95646574 0.35559180 322 16 6972
88 0.010 0.88 0.96808511 0.95981453 0.41177892 297 15 6997
89 0.010 0.89 0.97021277 0.96277692 0.45153142 275 14 7019
90 0.010 0.90 0.97234043 0.96586811 0.50524322 252 13 7042
91 0.010 0.91 0.97234043 0.96857290 0.72888005 231 13 7063
92 0.010 0.92 0.97234043 0.97256569 1.00000000 200 13 7094
93 0.010 0.93 0.97872340 0.97617208 0.82731077 175 10 7119
94 0.010 0.94 0.98085106 0.97913447 0.91863894 153 9 7141
95 0.010 0.95 0.98936170 0.98196806 0.28734175 135 5 7159
96 0.010 0.96 0.99361702 0.98596084 0.21010339 106 3 7188
97 0.010 0.97 0.99574468 0.98969603 0.26955354 78 2 7216
98 0.010 0.98 0.99574468 0.99291602 0.63787646 53 2 7241
99 0.010 0.99 0.99787234 0.99639361 0.87697095 27 1 7267
100 0.010 1.00 1.00000000 1.00000000 0.00000000 0 0 7294
101 0.025 0.01 0.04008667 0.04598145 0.40811699 6521 886 320
102 0.025 0.02 0.05417118 0.05963421 0.50118987 6428 873 413
103 0.025 0.03 0.06175515 0.06942298 0.36418385 6359 866 482
104 0.025 0.04 0.07908992 0.08037094 0.92986540 6290 850 551
105 0.025 0.05 0.09100758 0.09222050 0.94014921 6209 839 632
106 0.025 0.06 0.10617551 0.10419887 0.87917713 6130 825 711
107 0.025 0.07 0.12351029 0.11424523 0.37478472 6068 809 773
108 0.025 0.08 0.14301192 0.12660999 0.12266812 5990 791 851
109 0.025 0.09 0.15817985 0.14026275 0.10535314 5898 777 943
110 0.025 0.10 0.17118093 0.15108192 0.07714846 5826 765 1015
111 0.025 0.11 0.18093174 0.16280268 0.12311221 5744 756 1097
112 0.025 0.12 0.19284940 0.17709943 0.19726588 5644 745 1197
113 0.025 0.13 0.20368364 0.18907779 0.24503897 5561 735 1280
114 0.025 0.14 0.21776815 0.20260175 0.23894176 5469 722 1372
115 0.025 0.15 0.23076923 0.21586811 0.25863722 5378 710 1463
116 0.025 0.16 0.24268689 0.22797527 0.27431510 5295 699 1546
117 0.025 0.17 0.26002167 0.24252962 0.20055876 5198 683 1643
118 0.025 0.18 0.27735645 0.25540958 0.11214225 5114 667 1727
119 0.025 0.19 0.28927411 0.27009274 0.17423383 5011 656 1830
120 0.025 0.20 0.30552546 0.28657908 0.18770567 4898 641 1943
121 0.025 0.21 0.31310943 0.29752705 0.28696110 4820 634 2021
122 0.025 0.22 0.32719393 0.31491499 0.41343550 4698 621 2143
123 0.025 0.23 0.33911159 0.32843895 0.48506892 4604 610 2237
124 0.025 0.24 0.35427952 0.34453890 0.53098069 4493 596 2348
125 0.025 0.25 0.36511376 0.36128284 0.82461460 4373 586 2468
126 0.025 0.26 0.38244854 0.37854199 0.82234069 4255 570 2586
127 0.025 0.27 0.39761647 0.39657393 0.97356639 4129 556 2712
128 0.025 0.28 0.40953413 0.41409067 0.79192756 4004 545 2837
129 0.025 0.29 0.42036836 0.42928903 0.58376998 3896 535 2945
130 0.025 0.30 0.44203684 0.44448738 0.90105437 3798 515 3043
131 0.025 0.31 0.46262189 0.46032973 0.90950188 3694 496 3147
132 0.025 0.32 0.47128927 0.47359608 0.90890753 3599 488 3242
133 0.025 0.33 0.49079090 0.49330242 0.89852910 3464 470 3377
134 0.025 0.34 0.50379198 0.50901597 0.76178410 3354 458 3487
135 0.025 0.35 0.51895991 0.52357032 0.79203805 3255 444 3586
136 0.025 0.36 0.53087757 0.53464709 0.83410230 3180 433 3661
137 0.025 0.37 0.54929577 0.55229263 0.87303785 3060 416 3781
138 0.025 0.38 0.56446371 0.57109737 0.69034430 2928 402 3913
139 0.025 0.39 0.58396533 0.58861412 0.78705856 2810 384 4031
140 0.025 0.40 0.59913326 0.60497166 0.72583096 2697 370 4144
141 0.025 0.41 0.61105092 0.61862442 0.63938850 2602 359 4239
142 0.025 0.42 0.63271939 0.63369397 0.97680174 2505 339 4336
143 0.025 0.43 0.64572048 0.64837713 0.88600567 2403 327 4438
144 0.025 0.44 0.66088841 0.66138588 1.00000000 2316 313 4525
145 0.025 0.45 0.67605634 0.67928903 0.85197499 2191 299 4650
146 0.025 0.46 0.68147346 0.69178259 0.49358312 2099 294 4742
147 0.025 0.47 0.69339112 0.70376095 0.48601593 2017 283 4824
148 0.025 0.48 0.70422535 0.71470891 0.47609690 1942 273 4899
149 0.025 0.49 0.71722644 0.72501288 0.59948415 1874 261 4967
150 0.025 0.50 0.73239437 0.73634724 0.80214655 1800 247 5041
151 0.025 0.51 0.74214518 0.74510562 0.85744227 1741 238 5100
152 0.025 0.52 0.75406284 0.75643998 0.88992554 1664 227 5177
153 0.025 0.53 0.76381365 0.76738794 0.81629526 1588 218 5253
154 0.025 0.54 0.76923077 0.77460072 0.70841336 1537 213 5304
155 0.025 0.55 0.77898158 0.78554869 0.63468979 1461 204 5380
156 0.025 0.56 0.78331528 0.79224626 0.50332084 1413 200 5428
157 0.025 0.57 0.78873239 0.79894384 0.43487585 1366 195 5475
158 0.025 0.58 0.79523294 0.80757342 0.33268639 1305 189 5536
159 0.025 0.59 0.80390033 0.81543019 0.35929147 1252 181 5589
160 0.025 0.60 0.81798483 0.82290057 0.71075923 1207 168 5634
161 0.025 0.61 0.82015168 0.82972694 0.43665107 1156 166 5685
162 0.025 0.62 0.82990249 0.83681092 0.57710222 1110 157 5731
163 0.025 0.63 0.83423619 0.84312210 0.45773667 1065 153 5776
164 0.025 0.64 0.84182015 0.84981968 0.49925907 1020 146 5821
165 0.025 0.65 0.84723727 0.85793405 0.34647690 962 141 5879
166 0.025 0.66 0.85482124 0.86476043 0.37378970 916 134 5925
167 0.025 0.67 0.86132178 0.87107161 0.37383585 873 128 5968
168 0.025 0.68 0.86782232 0.87493560 0.52023651 849 122 5992
169 0.025 0.69 0.87757313 0.88060278 0.80387588 814 113 6027
170 0.025 0.70 0.89274106 0.88730036 0.61608201 776 99 6065
171 0.025 0.71 0.89815818 0.89167955 0.53639052 747 94 6094
172 0.025 0.72 0.90249187 0.89760433 0.64268159 705 90 6136
173 0.025 0.73 0.90465872 0.90069552 0.71117663 683 88 6158
174 0.025 0.74 0.90790899 0.90494590 0.78930904 653 85 6188
175 0.025 0.75 0.91007584 0.90868109 0.92364448 626 83 6215
176 0.025 0.76 0.91549296 0.91228748 0.76053550 603 78 6238
177 0.025 0.77 0.91982665 0.91834106 0.91117477 560 74 6281
178 0.025 0.78 0.92957746 0.92323545 0.48070001 531 65 6310
179 0.025 0.79 0.93174431 0.92658423 0.56657290 507 63 6334
180 0.025 0.80 0.93174431 0.93044822 0.92353792 477 63 6364
181 0.025 0.81 0.93499458 0.93379701 0.93196347 454 60 6387
182 0.025 0.82 0.93824485 0.93766100 0.99549869 427 57 6414
183 0.025 0.83 0.94257855 0.94268418 1.00000000 392 53 6449
184 0.025 0.84 0.94907909 0.94654817 0.77470398 368 47 6473
185 0.025 0.85 0.95449621 0.94938176 0.49959474 351 42 6490
186 0.025 0.86 0.95882990 0.95260175 0.38641419 330 38 6511
187 0.025 0.87 0.95991333 0.95646574 0.64486750 301 37 6540
188 0.025 0.88 0.96316360 0.95981453 0.64361478 278 34 6563
189 0.025 0.89 0.96749729 0.96277692 0.47496923 259 30 6582
190 0.025 0.90 0.97074756 0.96586811 0.43938661 238 27 6603
191 0.025 0.91 0.97291441 0.96857290 0.48087407 219 25 6622
192 0.025 0.92 0.97399783 0.97256569 0.85995410 189 24 6652
193 0.025 0.93 0.97724810 0.97617208 0.90972118 164 21 6677
194 0.025 0.94 0.97941495 0.97913447 1.00000000 143 19 6698
195 0.025 0.95 0.98591549 0.98196806 0.40746218 127 13 6714
196 0.025 0.96 0.99241603 0.98596084 0.10378570 102 7 6739
197 0.025 0.97 0.99458288 0.98969603 0.16373325 75 5 6766
198 0.025 0.98 0.99674973 0.99291602 0.20393425 52 3 6789
199 0.025 0.99 0.99783315 0.99639361 0.62784727 26 2 6815
200 0.025 1.00 1.00000000 1.00000000 0.00000000 0 0 6841
201 0.050 0.01 0.04568166 0.04598145 1.00000000 6070 1337 293
202 0.050 0.02 0.05852962 0.05963421 0.89613487 5982 1319 381
203 0.050 0.03 0.06566738 0.06942298 0.58035757 5916 1309 447
204 0.050 0.04 0.08065667 0.08037094 1.00000000 5852 1288 511
205 0.050 0.05 0.09279086 0.09222050 0.97566462 5777 1271 586
206 0.050 0.06 0.10635261 0.10419887 0.80787562 5703 1252 660
207 0.050 0.07 0.11848680 0.11424523 0.61362569 5642 1235 721
208 0.050 0.08 0.13490364 0.12660999 0.32373319 5569 1212 794
209 0.050 0.09 0.15060671 0.14026275 0.23440403 5485 1190 878
210 0.050 0.10 0.16345468 0.15108192 0.16537309 5419 1172 944
211 0.050 0.11 0.17344754 0.16280268 0.24925195 5342 1158 1021
212 0.050 0.12 0.18629550 0.17709943 0.33840182 5249 1140 1114
213 0.050 0.13 0.19486081 0.18907779 0.56668342 5168 1128 1195
214 0.050 0.14 0.21056388 0.20260175 0.43402620 5085 1106 1278
215 0.050 0.15 0.22412562 0.21586811 0.42721337 5001 1087 1362
216 0.050 0.16 0.23768737 0.22797527 0.35653532 4926 1068 1437
217 0.050 0.17 0.25410421 0.24252962 0.27920583 4836 1045 1527
218 0.050 0.18 0.27194861 0.25540958 0.12497397 4761 1020 1602
219 0.050 0.19 0.28551035 0.27009274 0.16078178 4666 1001 1697
220 0.050 0.20 0.30121342 0.28657908 0.19171301 4560 979 1803
221 0.050 0.21 0.30835118 0.29752705 0.34382198 4485 969 1878
222 0.050 0.22 0.32762313 0.31491499 0.27157485 4377 942 1986
223 0.050 0.23 0.33904354 0.32843895 0.36696772 4288 926 2075
224 0.050 0.24 0.35760171 0.34453890 0.26895853 4189 900 2174
225 0.050 0.25 0.37330478 0.36128284 0.31537213 4081 878 2282
226 0.050 0.26 0.38758030 0.37854199 0.45927192 3967 858 2396
227 0.050 0.27 0.39900071 0.39657393 0.86112174 3843 842 2520
228 0.050 0.28 0.41827266 0.41409067 0.74815121 3734 815 2629
229 0.050 0.29 0.43040685 0.42928903 0.94931904 3633 798 2730
230 0.050 0.30 0.44825125 0.44448738 0.77680801 3540 773 2823
231 0.050 0.31 0.46966453 0.46032973 0.45642611 3447 743 2916
232 0.050 0.32 0.47822984 0.47359608 0.72322318 3356 731 3007
233 0.050 0.33 0.50035689 0.49330242 0.57965926 3234 700 3129
234 0.050 0.34 0.51391863 0.50901597 0.70694834 3131 681 3232
235 0.050 0.35 0.52819415 0.52357032 0.72391458 3038 661 3325
236 0.050 0.36 0.53818701 0.53464709 0.79190019 2966 647 3397
237 0.050 0.37 0.55674518 0.55229263 0.73344693 2855 621 3508
238 0.050 0.38 0.57316203 0.57109737 0.88655280 2732 598 3631
239 0.050 0.39 0.59457530 0.58861412 0.63772497 2626 568 3737
240 0.050 0.40 0.60956460 0.60497166 0.72014169 2520 547 3843
241 0.050 0.41 0.62241256 0.61862442 0.77022959 2432 529 3931
242 0.050 0.42 0.64311206 0.63369397 0.43680629 2344 500 4019
243 0.050 0.43 0.65738758 0.64837713 0.45365779 2250 480 4113
244 0.050 0.44 0.67309065 0.66138588 0.32147178 2171 458 4192
245 0.050 0.45 0.68665239 0.67928903 0.53483031 2051 439 4312
246 0.050 0.46 0.69521770 0.69178259 0.78283632 1966 427 4397
247 0.050 0.47 0.70378301 0.70376095 1.00000000 1885 415 4478
248 0.050 0.48 0.71520343 0.71470891 0.98994577 1816 399 4547
249 0.050 0.49 0.72448251 0.72501288 0.98718344 1749 386 4614
250 0.050 0.50 0.73804425 0.73634724 0.89992756 1680 367 4683
251 0.050 0.51 0.74946467 0.74510562 0.70417062 1628 351 4735
252 0.050 0.52 0.76231263 0.75643998 0.59520524 1558 333 4805
253 0.050 0.53 0.77087794 0.76738794 0.75914192 1485 321 4878
254 0.050 0.54 0.77730193 0.77460072 0.81656117 1438 312 4925
255 0.050 0.55 0.78800857 0.78554869 0.83222827 1368 297 4995
256 0.050 0.56 0.79229122 0.79224626 1.00000000 1322 291 5041
257 0.050 0.57 0.79942898 0.79894384 0.98944418 1280 281 5083
258 0.050 0.58 0.80513919 0.80757342 0.82752345 1221 273 5142
259 0.050 0.59 0.81299072 0.81543019 0.82435119 1171 262 5192
260 0.050 0.60 0.82655246 0.82290057 0.72119219 1132 243 5231
261 0.050 0.61 0.82940757 0.82972694 1.00000000 1083 239 5280
262 0.050 0.62 0.83654532 0.83681092 1.00000000 1038 229 5325
263 0.050 0.63 0.84296931 0.84312210 1.00000000 998 220 5365
264 0.050 0.64 0.84939329 0.84981968 0.99358208 955 211 5408
265 0.050 0.65 0.85438972 0.85793405 0.70581196 899 204 5464
266 0.050 0.66 0.86009993 0.86476043 0.60284549 854 196 5509
267 0.050 0.67 0.86581014 0.87107161 0.54510780 813 188 5550
268 0.050 0.68 0.87009279 0.87493560 0.57500464 789 182 5574
269 0.050 0.69 0.87794433 0.88060278 0.76916088 756 171 5607
270 0.050 0.70 0.89150607 0.88730036 0.61480650 723 152 5640
271 0.050 0.71 0.89578872 0.89167955 0.61764433 695 146 5668
272 0.050 0.72 0.90149893 0.89760433 0.62947213 657 138 5706
273 0.050 0.73 0.90435403 0.90069552 0.64807227 637 134 5726
274 0.050 0.74 0.90935046 0.90494590 0.56826399 611 127 5752
275 0.050 0.75 0.91363312 0.90868109 0.50954684 588 121 5775
276 0.050 0.76 0.92005710 0.91228748 0.27860461 569 112 5794
277 0.050 0.77 0.92505353 0.91834106 0.33726522 529 105 5834
278 0.050 0.78 0.93290507 0.92323545 0.14808085 502 94 5861
279 0.050 0.79 0.93504640 0.92658423 0.19883546 479 91 5884
280 0.050 0.80 0.93504640 0.93044822 0.49061471 449 91 5914
281 0.050 0.81 0.93861527 0.93379701 0.45815911 428 86 5935
282 0.050 0.82 0.94075660 0.93766100 0.63953097 401 83 5962
283 0.050 0.83 0.94361171 0.94268418 0.91915308 366 79 5997
284 0.050 0.84 0.94932191 0.94654817 0.65686096 344 71 6019
285 0.050 0.85 0.95289079 0.94938176 0.55216778 327 66 6036
286 0.050 0.86 0.95717345 0.95260175 0.41215371 308 60 6055
287 0.050 0.87 0.96074233 0.95646574 0.42707530 283 55 6080
288 0.050 0.88 0.96431121 0.95981453 0.38346634 262 50 6101
289 0.050 0.89 0.96859386 0.96277692 0.23306475 245 44 6118
290 0.050 0.90 0.97216274 0.96586811 0.17633893 226 39 6137
291 0.050 0.91 0.97430407 0.96857290 0.20280542 208 36 6155
292 0.050 0.92 0.97715917 0.97256569 0.28355744 181 32 6182
293 0.050 0.93 0.98001428 0.97617208 0.34473181 157 28 6206
294 0.050 0.94 0.98144183 0.97913447 0.57261651 136 26 6227
295 0.050 0.95 0.98572448 0.98196806 0.29083850 120 20 6243
296 0.050 0.96 0.99072091 0.98596084 0.12177157 96 13 6267
297 0.050 0.97 0.99428979 0.98969603 0.08279488 72 8 6291
298 0.050 0.98 0.99643112 0.99291602 0.11948034 50 5 6313
299 0.050 0.99 0.99785867 0.99639361 0.44466456 25 3 6338
300 0.050 1.00 1.00000000 1.00000000 0.00000000 0 0 6363
301 0.075 0.01 0.04756512 0.04598145 0.76655394 5725 1682 273
302 0.075 0.02 0.05945640 0.05963421 1.00000000 5640 1661 358
303 0.075 0.03 0.06964892 0.06942298 1.00000000 5582 1643 416
304 0.075 0.04 0.08380521 0.08037094 0.57945916 5522 1618 476
305 0.075 0.05 0.09569649 0.09222050 0.59777206 5451 1597 547
306 0.075 0.06 0.10872027 0.10419887 0.50716075 5381 1574 617
307 0.075 0.07 0.12117780 0.11424523 0.31759539 5325 1552 673
308 0.075 0.08 0.13703284 0.12660999 0.14487265 5257 1524 741
309 0.075 0.09 0.15402039 0.14026275 0.06356583 5181 1494 817
310 0.075 0.10 0.16534541 0.15108192 0.06197958 5117 1474 881
311 0.075 0.11 0.17440544 0.16280268 0.14265886 5042 1458 956
312 0.075 0.12 0.18573046 0.17709943 0.29578516 4951 1438 1047
313 0.075 0.13 0.19365798 0.18907779 0.59980375 4872 1424 1126
314 0.075 0.14 0.20894677 0.20260175 0.47085968 4794 1397 1204
315 0.075 0.15 0.21970555 0.21586811 0.67957048 4710 1378 1288
316 0.075 0.16 0.23556059 0.22797527 0.40529500 4644 1350 1354
317 0.075 0.17 0.25198188 0.24252962 0.30639453 4560 1321 1438
318 0.075 0.18 0.26840317 0.25540958 0.16345706 4489 1292 1509
319 0.075 0.19 0.28369196 0.27009274 0.15159826 4402 1265 1596
320 0.075 0.20 0.29841450 0.28657908 0.22188162 4300 1239 1698
321 0.075 0.21 0.30747452 0.29752705 0.31215222 4231 1223 1767
322 0.075 0.22 0.32559456 0.31491499 0.28454489 4128 1191 1870
323 0.075 0.23 0.33918460 0.32843895 0.28681863 4047 1167 1951
324 0.075 0.24 0.35900340 0.34453890 0.15363991 3957 1132 2041
325 0.075 0.25 0.37315968 0.36128284 0.24852885 3852 1107 2146
326 0.075 0.26 0.38901472 0.37854199 0.31516051 3746 1079 2252
327 0.075 0.27 0.40430351 0.39657393 0.46673784 3633 1052 2365
328 0.075 0.28 0.42355606 0.41409067 0.37277095 3531 1018 2467
329 0.075 0.29 0.43827860 0.42928903 0.40035154 3439 992 2559
330 0.075 0.30 0.45413364 0.44448738 0.36763845 3349 964 2649
331 0.075 0.31 0.47281993 0.46032973 0.24160853 3259 931 2739
332 0.075 0.32 0.48131370 0.47359608 0.47652300 3171 916 2827
333 0.075 0.33 0.50396376 0.49330242 0.32095825 3058 876 2940
334 0.075 0.34 0.51925255 0.50901597 0.34112784 2963 849 3035
335 0.075 0.35 0.53284258 0.52357032 0.38949728 2874 825 3124
336 0.075 0.36 0.54416761 0.53464709 0.37591885 2808 805 3190
337 0.075 0.37 0.56342016 0.55229263 0.29708804 2705 771 3293
338 0.075 0.38 0.58097395 0.57109737 0.35404210 2590 740 3408
339 0.075 0.39 0.60079275 0.58861412 0.24776817 2489 705 3509
340 0.075 0.40 0.61608154 0.60497166 0.28964930 2389 678 3609
341 0.075 0.41 0.62740657 0.61862442 0.40282175 2303 658 3695
342 0.075 0.42 0.64496036 0.63369397 0.27573746 2217 627 3781
343 0.075 0.43 0.65911665 0.64837713 0.29507972 2128 602 3870
344 0.075 0.44 0.67667044 0.66138588 0.12961786 2058 571 3940
345 0.075 0.45 0.69309173 0.67928903 0.16608745 1948 542 4050
346 0.075 0.46 0.70328426 0.69178259 0.24539639 1869 524 4129
347 0.075 0.47 0.71291053 0.70376095 0.35318216 1793 507 4205
348 0.075 0.48 0.72593431 0.71470891 0.24661891 1731 484 4267
349 0.075 0.49 0.73612684 0.72501288 0.24614692 1669 466 4329
350 0.075 0.50 0.74858437 0.73634724 0.19457905 1603 444 4395
351 0.075 0.51 0.75821065 0.74510562 0.15951937 1552 427 4446
352 0.075 0.52 0.77123443 0.75643998 0.10600560 1487 404 4511
353 0.075 0.53 0.78029445 0.76738794 0.15314164 1418 388 4580
354 0.075 0.54 0.78652322 0.77460072 0.18291573 1373 377 4625
355 0.075 0.55 0.79728199 0.78554869 0.18226375 1307 358 4691
356 0.075 0.56 0.80124575 0.79224626 0.30431445 1262 351 4736
357 0.075 0.57 0.80747452 0.79894384 0.32517355 1221 340 4777
358 0.075 0.58 0.81540204 0.80757342 0.36010525 1168 326 4830
359 0.075 0.59 0.82276331 0.81543019 0.38492383 1120 313 4878
360 0.075 0.60 0.83465459 0.82290057 0.15081849 1083 292 4915
361 0.075 0.61 0.83861835 0.82972694 0.27352063 1037 285 4961
362 0.075 0.62 0.84484711 0.83681092 0.31580725 993 274 5005
363 0.075 0.63 0.85390713 0.84312210 0.16739312 960 258 5038
364 0.075 0.64 0.86013590 0.84981968 0.17934823 919 247 5079
365 0.075 0.65 0.86579841 0.85793405 0.29915267 866 237 5132
366 0.075 0.66 0.87089468 0.86476043 0.41333492 822 228 5176
367 0.075 0.67 0.87599094 0.87107161 0.50832477 782 219 5216
368 0.075 0.68 0.87938845 0.87493560 0.54672125 758 213 5240
369 0.075 0.69 0.88618347 0.88060278 0.43472590 726 201 5272
370 0.075 0.70 0.89750849 0.88730036 0.13345531 694 181 5304
371 0.075 0.71 0.90147225 0.89167955 0.14347446 667 174 5331
372 0.075 0.72 0.90600227 0.89760433 0.20062798 629 166 5369
373 0.075 0.73 0.90826727 0.90069552 0.24393075 609 162 5389
374 0.075 0.74 0.91336353 0.90494590 0.18481372 585 153 5413
375 0.075 0.75 0.91732729 0.90868109 0.16511240 563 146 5435
376 0.075 0.76 0.92298981 0.91228748 0.07822928 545 136 5453
377 0.075 0.77 0.92865232 0.91834106 0.07997020 508 126 5490
378 0.075 0.78 0.93544734 0.92323545 0.03216397 482 114 5516
379 0.075 0.79 0.93771234 0.92658423 0.04680648 460 110 5538
380 0.075 0.80 0.93771234 0.93044822 0.18950157 430 110 5568
381 0.075 0.81 0.94110985 0.93379701 0.17644415 410 104 5588
382 0.075 0.82 0.94337486 0.93766100 0.28283795 384 100 5614
383 0.075 0.83 0.94790487 0.94268418 0.30981514 353 92 5645
384 0.075 0.84 0.95300113 0.94654817 0.18970159 332 83 5666
385 0.075 0.85 0.95583239 0.94938176 0.17857730 315 78 5683
386 0.075 0.86 0.95922990 0.95260175 0.15338378 296 72 5702
387 0.075 0.87 0.96262741 0.95646574 0.16839440 272 66 5726
388 0.075 0.88 0.96545866 0.95981453 0.19184912 251 61 5747
389 0.075 0.89 0.96885617 0.96277692 0.14322828 234 55 5764
390 0.075 0.90 0.97225368 0.96586811 0.10806748 216 49 5782
391 0.075 0.91 0.97451869 0.96857290 0.12070896 199 45 5799
392 0.075 0.92 0.97734994 0.97256569 0.18767180 173 40 5825
393 0.075 0.93 0.98018120 0.97617208 0.24277593 150 35 5848
394 0.075 0.94 0.98131370 0.97913447 0.52591558 129 33 5869
395 0.075 0.95 0.98527746 0.98196806 0.27687700 114 26 5884
396 0.075 0.96 0.98980747 0.98596084 0.14757674 91 18 5907
397 0.075 0.97 0.99377123 0.98969603 0.07259162 69 11 5929
398 0.075 0.98 0.99546999 0.99291602 0.19546748 47 8 5951
399 0.075 0.99 0.99773499 0.99639361 0.39863476 24 4 5974
400 0.075 1.00 1.00000000 1.00000000 0.00000000 0 0 5998
401 0.100 0.01 0.04750480 0.04598145 0.74362827 5422 1985 258
402 0.100 0.02 0.05950096 0.05963421 1.00000000 5341 1960 339
403 0.100 0.03 0.07005758 0.06942298 0.93394914 5287 1938 393
404 0.100 0.04 0.08397313 0.08037094 0.50920578 5231 1909 449
405 0.100 0.05 0.09596929 0.09222050 0.51746272 5164 1884 516
406 0.100 0.06 0.10940499 0.10419887 0.38562990 5099 1856 581
407 0.100 0.07 0.12140115 0.11424523 0.24589844 5046 1831 634
408 0.100 0.08 0.13579655 0.12660999 0.15101717 4980 1801 700
409 0.100 0.09 0.15211132 0.14026275 0.07439011 4908 1767 772
410 0.100 0.10 0.16362764 0.15108192 0.06666100 4848 1743 832
411 0.100 0.11 0.17370441 0.16280268 0.12322944 4778 1722 902
412 0.100 0.12 0.18666027 0.17709943 0.19252438 4694 1695 986
413 0.100 0.13 0.19577735 0.18907779 0.37860379 4620 1676 1060
414 0.100 0.14 0.21065259 0.20260175 0.29964523 4546 1645 1134
415 0.100 0.15 0.22264875 0.21586811 0.39615701 4468 1620 1212
416 0.100 0.16 0.23800384 0.22797527 0.21301457 4406 1588 1274
417 0.100 0.17 0.25431862 0.24252962 0.15039611 4327 1554 1353
418 0.100 0.18 0.26871401 0.25540958 0.10983316 4257 1524 1423
419 0.100 0.19 0.28310940 0.27009274 0.12457637 4173 1494 1507
420 0.100 0.20 0.29846449 0.28657908 0.16925202 4077 1462 1603
421 0.100 0.21 0.30758157 0.29752705 0.25187428 4011 1443 1669
422 0.100 0.22 0.32581574 0.31491499 0.22057180 3914 1405 1766
423 0.100 0.23 0.34021113 0.32843895 0.19000146 3839 1375 1841
424 0.100 0.24 0.35844530 0.34453890 0.12480723 3752 1337 1928
425 0.100 0.25 0.37188100 0.36128284 0.24978701 3650 1309 2030
426 0.100 0.26 0.38819578 0.37854199 0.30024359 3550 1275 2130
427 0.100 0.27 0.40451056 0.39657393 0.40105072 3444 1241 2236
428 0.100 0.28 0.42274472 0.41409067 0.36191400 3346 1203 2334
429 0.100 0.29 0.43618042 0.42928903 0.47323849 3256 1175 2424
430 0.100 0.30 0.45201536 0.44448738 0.43374293 3171 1142 2509
431 0.100 0.31 0.47120921 0.46032973 0.25457326 3088 1102 2592
432 0.100 0.32 0.48176583 0.47359608 0.39663183 3007 1080 2673
433 0.100 0.33 0.50239923 0.49330242 0.34439699 2897 1037 2783
434 0.100 0.34 0.51631478 0.50901597 0.45107561 2804 1008 2876
435 0.100 0.35 0.53071017 0.52357032 0.46090827 2721 978 2959
436 0.100 0.36 0.54174664 0.53464709 0.46295164 2658 955 3022
437 0.100 0.37 0.55902111 0.55229263 0.48615317 2557 919 3123
438 0.100 0.38 0.57629559 0.57109737 0.59285329 2447 883 3233
439 0.100 0.39 0.59548944 0.58861412 0.47171659 2351 843 3329
440 0.100 0.40 0.61036468 0.60497166 0.57370348 2255 812 3425
441 0.100 0.41 0.62284069 0.61862442 0.66216216 2175 786 3505
442 0.100 0.42 0.63963532 0.63369397 0.52765148 2093 751 3587
443 0.100 0.43 0.65547025 0.64837713 0.44364507 2012 718 3668
444 0.100 0.44 0.67274472 0.66138588 0.20983956 1947 682 3733
445 0.100 0.45 0.68857965 0.67928903 0.30069640 1841 649 3839
446 0.100 0.46 0.69817658 0.69178259 0.47688419 1764 629 3916
447 0.100 0.47 0.70873321 0.70376095 0.58014657 1693 607 3987
448 0.100 0.48 0.72168906 0.71470891 0.42563783 1635 580 4045
449 0.100 0.49 0.73224568 0.72501288 0.40322193 1577 558 4103
450 0.100 0.50 0.74472169 0.73634724 0.32445052 1515 532 4165
451 0.100 0.51 0.75383877 0.74510562 0.29826415 1466 513 4214
452 0.100 0.52 0.76631478 0.75643998 0.23089924 1404 487 4276
453 0.100 0.53 0.77543186 0.76738794 0.32420678 1338 468 4342
454 0.100 0.54 0.78214971 0.77460072 0.35050652 1296 454 4384
455 0.100 0.55 0.79222649 0.78554869 0.40250175 1232 433 4448
456 0.100 0.56 0.79606526 0.79224626 0.63774733 1188 425 4492
457 0.100 0.57 0.80182342 0.79894384 0.72520014 1148 413 4532
458 0.100 0.58 0.80950096 0.80757342 0.81926469 1097 397 4583
459 0.100 0.59 0.81669866 0.81543019 0.88747250 1051 382 4629
460 0.100 0.60 0.82725528 0.82290057 0.56509889 1015 360 4665
461 0.100 0.61 0.83205374 0.82972694 0.76697930 972 350 4708
462 0.100 0.62 0.83973129 0.83681092 0.69865496 933 334 4747
463 0.100 0.63 0.84932821 0.84312210 0.38126656 904 314 4776
464 0.100 0.64 0.85508637 0.84981968 0.45265630 864 302 4816
465 0.100 0.65 0.86180422 0.85793405 0.57890477 815 288 4865
466 0.100 0.66 0.86756238 0.86476043 0.68926445 774 276 4906
467 0.100 0.67 0.87188100 0.87107161 0.92773530 734 267 4946
468 0.100 0.68 0.87619962 0.87493560 0.86875908 713 258 4967
469 0.100 0.69 0.88339731 0.88060278 0.67412835 684 243 4996
470 0.100 0.70 0.89299424 0.88730036 0.35730173 652 223 5028
471 0.100 0.71 0.89827255 0.89167955 0.27525344 629 212 5051
472 0.100 0.72 0.90211132 0.89760433 0.45252322 591 204 5089
473 0.100 0.73 0.90451056 0.90069552 0.52346083 572 199 5108
474 0.100 0.74 0.90930902 0.90494590 0.45305374 549 189 5131
475 0.100 0.75 0.91362764 0.90868109 0.38318171 529 180 5151
476 0.100 0.76 0.91890595 0.91228748 0.22878674 512 169 5168
477 0.100 0.77 0.92562380 0.91834106 0.16986108 479 155 5201
478 0.100 0.78 0.93234165 0.92323545 0.07547742 455 141 5225
479 0.100 0.79 0.93522073 0.92658423 0.08575394 435 135 5245
480 0.100 0.80 0.93570058 0.93044822 0.29296646 406 134 5274
481 0.100 0.81 0.94001919 0.93379701 0.19908756 389 125 5291
482 0.100 0.82 0.94289827 0.93766100 0.26994020 365 119 5315
483 0.100 0.83 0.94721689 0.94268418 0.32428996 335 110 5345
484 0.100 0.84 0.95249520 0.94654817 0.17567454 316 99 5364
485 0.100 0.85 0.95633397 0.94938176 0.10220986 302 91 5378
486 0.100 0.86 0.95921305 0.95260175 0.10952182 283 85 5397
487 0.100 0.87 0.96305182 0.95646574 0.09693741 261 77 5419
488 0.100 0.88 0.96545106 0.95981453 0.14248663 240 72 5440
489 0.100 0.89 0.96880998 0.96277692 0.10240876 224 65 5456
490 0.100 0.90 0.97216891 0.96586811 0.07481222 207 58 5473
491 0.100 0.91 0.97456814 0.96857290 0.07830098 191 53 5489
492 0.100 0.92 0.97696737 0.97256569 0.17388046 165 48 5515
493 0.100 0.93 0.97984645 0.97617208 0.22940229 143 42 5537
494 0.100 0.94 0.98080614 0.97913447 0.59290912 122 40 5558
495 0.100 0.95 0.98416507 0.98196806 0.43246647 107 33 5573
496 0.100 0.96 0.98992322 0.98596084 0.09128122 88 21 5592
497 0.100 0.97 0.99424184 0.98969603 0.02286025 68 12 5612
498 0.100 0.98 0.99568138 0.99291602 0.10801938 46 9 5634
499 0.100 0.99 0.99808061 0.99639361 0.19759913 24 4 5656
500 0.100 1.00 1.00000000 1.00000000 0.00000000 0 0 5680
Dboth
1 20
2 26
3 31
4 41
5 46
6 53
7 62
8 72
9 79
10 87
11 92
12 97
13 103
14 110
15 119
16 125
17 135
18 141
19 146
20 157
21 162
22 171
23 180
24 187
25 191
26 199
27 206
28 212
29 216
30 225
31 233
32 238
33 245
34 250
35 257
36 263
37 271
38 275
39 284
40 293
41 299
42 310
43 313
44 321
45 331
46 332
47 339
48 342
49 352
50 357
51 363
52 370
53 376
54 376
55 379
56 383
57 386
58 391
59 394
60 397
61 397
62 402
63 403
64 407
65 408
66 411
67 415
68 417
69 419
70 425
71 428
72 429
73 429
74 429
75 430
76 433
77 434
78 440
79 442
80 442
81 444
82 447
83 448
84 450
85 451
86 453
87 454
88 455
89 456
90 457
91 457
92 457
93 460
94 461
95 465
96 467
97 468
98 468
99 469
100 470
101 37
102 50
103 57
104 73
105 84
106 98
107 114
108 132
109 146
110 158
111 167
112 178
113 188
114 201
115 213
116 224
117 240
118 256
119 267
120 282
121 289
122 302
123 313
124 327
125 337
126 353
127 367
128 378
129 388
130 408
131 427
132 435
133 453
134 465
135 479
136 490
137 507
138 521
139 539
140 553
141 564
142 584
143 596
144 610
145 624
146 629
147 640
148 650
149 662
150 676
151 685
152 696
153 705
154 710
155 719
156 723
157 728
158 734
159 742
160 755
161 757
162 766
163 770
164 777
165 782
166 789
167 795
168 801
169 810
170 824
171 829
172 833
173 835
174 838
175 840
176 845
177 849
178 858
179 860
180 860
181 863
182 866
183 870
184 876
185 881
186 885
187 886
188 889
189 893
190 896
191 898
192 899
193 902
194 904
195 910
196 916
197 918
198 920
199 921
200 923
201 64
202 82
203 92
204 113
205 130
206 149
207 166
208 189
209 211
210 229
211 243
212 261
213 273
214 295
215 314
216 333
217 356
218 381
219 400
220 422
221 432
222 459
223 475
224 501
225 523
226 543
227 559
228 586
229 603
230 628
231 658
232 670
233 701
234 720
235 740
236 754
237 780
238 803
239 833
240 854
241 872
242 901
243 921
244 943
245 962
246 974
247 986
248 1002
249 1015
250 1034
251 1050
252 1068
253 1080
254 1089
255 1104
256 1110
257 1120
258 1128
259 1139
260 1158
261 1162
262 1172
263 1181
264 1190
265 1197
266 1205
267 1213
268 1219
269 1230
270 1249
271 1255
272 1263
273 1267
274 1274
275 1280
276 1289
277 1296
278 1307
279 1310
280 1310
281 1315
282 1318
283 1322
284 1330
285 1335
286 1341
287 1346
288 1351
289 1357
290 1362
291 1365
292 1369
293 1373
294 1375
295 1381
296 1388
297 1393
298 1396
299 1398
300 1401
301 84
302 105
303 123
304 148
305 169
306 192
307 214
308 242
309 272
310 292
311 308
312 328
313 342
314 369
315 388
316 416
317 445
318 474
319 501
320 527
321 543
322 575
323 599
324 634
325 659
326 687
327 714
328 748
329 774
330 802
331 835
332 850
333 890
334 917
335 941
336 961
337 995
338 1026
339 1061
340 1088
341 1108
342 1139
343 1164
344 1195
345 1224
346 1242
347 1259
348 1282
349 1300
350 1322
351 1339
352 1362
353 1378
354 1389
355 1408
356 1415
357 1426
358 1440
359 1453
360 1474
361 1481
362 1492
363 1508
364 1519
365 1529
366 1538
367 1547
368 1553
369 1565
370 1585
371 1592
372 1600
373 1604
374 1613
375 1620
376 1630
377 1640
378 1652
379 1656
380 1656
381 1662
382 1666
383 1674
384 1683
385 1688
386 1694
387 1700
388 1705
389 1711
390 1717
391 1721
392 1726
393 1731
394 1733
395 1740
396 1748
397 1755
398 1758
399 1762
400 1766
401 99
402 124
403 146
404 175
405 200
406 228
407 253
408 283
409 317
410 341
411 362
412 389
413 408
414 439
415 464
416 496
417 530
418 560
419 590
420 622
421 641
422 679
423 709
424 747
425 775
426 809
427 843
428 881
429 909
430 942
431 982
432 1004
433 1047
434 1076
435 1106
436 1129
437 1165
438 1201
439 1241
440 1272
441 1298
442 1333
443 1366
444 1402
445 1435
446 1455
447 1477
448 1504
449 1526
450 1552
451 1571
452 1597
453 1616
454 1630
455 1651
456 1659
457 1671
458 1687
459 1702
460 1724
461 1734
462 1750
463 1770
464 1782
465 1796
466 1808
467 1817
468 1826
469 1841
470 1861
471 1872
472 1880
473 1885
474 1895
475 1904
476 1915
477 1929
478 1943
479 1949
480 1950
481 1959
482 1965
483 1974
484 1985
485 1993
486 1999
487 2007
488 2012
489 2019
490 2026
491 2031
492 2036
493 2042
494 2044
495 2051
496 2063
497 2072
498 2075
499 2080
500 2084enrichment.plotter(gene.hic.filt, "adj.P.Val", "min_FDR.H", "Minimum DE FDR of Genes Overlapping Hi-C Contacts", xmax=1, recip=TRUE) #FIGS19A/BWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.05602241 0.06053581 0.800652624 6957 337 450
2 0.010 0.02 0.09243697 0.10059248 0.663978705 6659 324 748
3 0.010 0.03 0.13165266 0.13459557 0.930337299 6409 310 998
4 0.010 0.04 0.15686275 0.15996909 0.928275481 6221 301 1186
5 0.010 0.05 0.17927171 0.18044822 1.000000000 6070 293 1337
6 0.010 0.06 0.20168067 0.20002576 0.990186154 5926 285 1481
7 0.010 0.07 0.21568627 0.21818650 0.958922928 5790 280 1617
8 0.010 0.08 0.24929972 0.23480165 0.550018420 5673 268 1734
9 0.010 0.09 0.26610644 0.25270479 0.593186399 5540 262 1867
10 0.010 0.10 0.27731092 0.26841834 0.743628272 5422 258 1985
11 0.010 0.11 0.28571429 0.28065430 0.874812750 5330 255 2077
12 0.010 0.12 0.29691877 0.29533745 0.993884589 5220 251 2187
13 0.010 0.13 0.30252101 0.30873261 0.840341856 5118 249 2289
14 0.010 0.14 0.31652661 0.32393096 0.803998305 5005 244 2402
15 0.010 0.15 0.33053221 0.33590933 0.870621812 4917 239 2490
16 0.010 0.16 0.33613445 0.34698609 0.700920468 4833 237 2574
17 0.010 0.17 0.35014006 0.35651726 0.840703138 4764 232 2643
18 0.010 0.18 0.36694678 0.36810922 1.000000000 4680 226 2727
19 0.010 0.19 0.39215686 0.37802679 0.611570946 4612 217 2795
20 0.010 0.20 0.40056022 0.39026275 0.724228311 4520 214 2887
21 0.010 0.21 0.41456583 0.40146832 0.644373263 4438 209 2969
22 0.010 0.22 0.43977591 0.41190108 0.298089950 4366 200 3041
23 0.010 0.23 0.44257703 0.42297785 0.476100034 4281 199 3126
24 0.010 0.24 0.44537815 0.43366821 0.687378789 4199 198 3208
25 0.010 0.25 0.44817927 0.44332818 0.893115650 4125 197 3282
26 0.010 0.26 0.45378151 0.45350335 1.000000000 4048 195 3359
27 0.010 0.27 0.45378151 0.46200412 0.791238616 3982 195 3425
28 0.010 0.28 0.46218487 0.47011850 0.800106597 3922 192 3485
29 0.010 0.29 0.47619048 0.48016486 0.920616777 3849 187 3558
30 0.010 0.30 0.49019608 0.49072643 1.000000000 3772 182 3635
31 0.010 0.31 0.49299720 0.49819681 0.883146496 3715 181 3692
32 0.010 0.32 0.50420168 0.50695518 0.958250913 3651 177 3756
33 0.010 0.33 0.51260504 0.51609995 0.935386393 3583 174 3824
34 0.010 0.34 0.51540616 0.52460072 0.762723416 3518 173 3889
35 0.010 0.35 0.52380952 0.53207110 0.790245079 3463 170 3944
36 0.010 0.36 0.52661064 0.54018547 0.636543613 3401 169 4006
37 0.010 0.37 0.53221289 0.54662545 0.613121642 3353 167 4054
38 0.010 0.38 0.54061625 0.55319423 0.663627911 3305 164 4102
39 0.010 0.39 0.54621849 0.56169500 0.583159212 3241 162 4166
40 0.010 0.40 0.55462185 0.56800618 0.639793731 3195 159 4212
41 0.010 0.41 0.55742297 0.57534776 0.517833707 3139 158 4268
42 0.010 0.42 0.57142857 0.58346213 0.676508459 3081 153 4326
43 0.010 0.43 0.57703081 0.59222050 0.587268901 3015 151 4392
44 0.010 0.44 0.58263305 0.59917568 0.550036866 2963 149 4444
45 0.010 0.45 0.58543417 0.60625966 0.441836082 2909 148 4498
46 0.010 0.46 0.58543417 0.61385884 0.282938643 2850 148 4557
47 0.010 0.47 0.59103641 0.61939722 0.282762145 2809 146 4598
48 0.010 0.48 0.59663866 0.62686759 0.248880733 2753 144 4654
49 0.010 0.49 0.60504202 0.63614116 0.232430702 2684 141 4723
50 0.010 0.50 0.61064426 0.64129315 0.238133543 2646 139 4761
51 0.010 0.51 0.62184874 0.64863472 0.303651985 2593 135 4814
52 0.010 0.52 0.63025210 0.65546110 0.332466932 2543 132 4864
53 0.010 0.53 0.63585434 0.66293148 0.293373423 2487 130 4920
54 0.010 0.54 0.63865546 0.67014426 0.215731319 2432 129 4975
55 0.010 0.55 0.64705882 0.67426584 0.286779085 2403 126 5004
56 0.010 0.56 0.65266106 0.67993302 0.283357140 2361 124 5046
57 0.010 0.57 0.65266106 0.68624420 0.179707412 2312 124 5095
58 0.010 0.58 0.65826331 0.69448738 0.143617490 2250 122 5157
59 0.010 0.59 0.66106443 0.70118496 0.101768730 2199 121 5208
60 0.010 0.60 0.66386555 0.70839773 0.066392156 2144 120 5263
61 0.010 0.61 0.66666667 0.71535291 0.042653784 2091 119 5316
62 0.010 0.62 0.68067227 0.72140649 0.089650330 2049 114 5358
63 0.010 0.63 0.69187675 0.72784647 0.132965198 2003 110 5404
64 0.010 0.64 0.69187675 0.73364245 0.077329863 1958 110 5449
65 0.010 0.65 0.69187675 0.73853684 0.046328280 1920 110 5487
66 0.010 0.66 0.70028011 0.74678001 0.044831808 1859 107 5548
67 0.010 0.67 0.70868347 0.75399279 0.048587641 1806 104 5601
68 0.010 0.68 0.72549020 0.76416795 0.089385709 1733 98 5674
69 0.010 0.69 0.73109244 0.77163833 0.071243900 1677 96 5730
70 0.010 0.70 0.73949580 0.77833591 0.081223075 1628 93 5779
71 0.010 0.71 0.73949580 0.78400309 0.042727168 1584 93 5823
72 0.010 0.72 0.74789916 0.78967027 0.055336084 1543 90 5864
73 0.010 0.73 0.75070028 0.79495106 0.040063042 1503 89 5904
74 0.010 0.74 0.76470588 0.80100464 0.090854612 1461 84 5946
75 0.010 0.75 0.76750700 0.80795981 0.055132162 1408 83 5999
76 0.010 0.76 0.77310924 0.81388460 0.050341870 1364 81 6043
77 0.010 0.77 0.78711485 0.82199897 0.090393507 1306 76 6101
78 0.010 0.78 0.78991597 0.82779495 0.061623926 1262 75 6145
79 0.010 0.79 0.80112045 0.83475013 0.093223905 1212 71 6195
80 0.010 0.80 0.80112045 0.84067491 0.043728176 1166 71 6241
81 0.010 0.81 0.82072829 0.85200927 0.103565734 1085 64 6322
82 0.010 0.82 0.82352941 0.85896445 0.058548005 1032 63 6375
83 0.010 0.83 0.83473389 0.86643483 0.084885770 978 59 6429
84 0.010 0.84 0.84593838 0.87467800 0.110186466 918 55 6489
85 0.010 0.85 0.84873950 0.88408037 0.040275437 846 54 6561
86 0.010 0.86 0.85714286 0.89283874 0.031969468 781 51 6626
87 0.010 0.87 0.86554622 0.90352911 0.016532461 701 48 6706
88 0.010 0.88 0.87675070 0.90945389 0.034846297 659 44 6748
89 0.010 0.89 0.88515406 0.91486347 0.049739964 620 41 6787
90 0.010 0.90 0.90196078 0.92168985 0.186928988 573 35 6834
91 0.010 0.91 0.91316527 0.92967543 0.252991424 515 31 6892
92 0.010 0.92 0.91316527 0.93547141 0.099766307 470 31 6937
93 0.010 0.93 0.91596639 0.94397218 0.025227369 405 30 7002
94 0.010 0.94 0.92436975 0.95324575 0.011815614 336 27 7071
95 0.010 0.95 0.93837535 0.96136012 0.030283544 278 22 7129
96 0.010 0.96 0.94677871 0.96780010 0.031549509 231 19 7176
97 0.010 0.97 0.96358543 0.97552808 0.186881383 177 13 7230
98 0.010 0.98 0.97478992 0.98286965 0.319374503 124 9 7283
99 0.010 0.99 0.98879552 0.99356002 0.415923533 46 4 7361
100 0.010 1.00 1.00000000 1.00000000 0.000000000 0 0 7407
101 0.025 0.01 0.05964215 0.06053581 1.000000000 6821 473 440
102 0.025 0.02 0.09542744 0.10059248 0.747760009 6528 455 733
103 0.025 0.03 0.13320080 0.13459557 0.978275449 6283 436 978
104 0.025 0.04 0.16302187 0.15996909 0.896371562 6101 421 1160
105 0.025 0.05 0.18091451 0.18044822 1.000000000 5951 412 1310
106 0.025 0.06 0.20477137 0.20002576 0.827817900 5811 400 1450
107 0.025 0.07 0.21868787 0.21818650 1.000000000 5677 393 1584
108 0.025 0.08 0.24453280 0.23480165 0.632625688 5561 380 1700
109 0.025 0.09 0.26441352 0.25270479 0.567447458 5432 370 1829
110 0.025 0.10 0.27634195 0.26841834 0.716860281 5316 364 1945
111 0.025 0.11 0.28429423 0.28065430 0.891372811 5225 360 2036
112 0.025 0.12 0.29423459 0.29533745 0.995585801 5116 355 2145
113 0.025 0.13 0.30616302 0.30873261 0.936957260 5018 349 2243
114 0.025 0.14 0.32007952 0.32393096 0.887392027 4907 342 2354
115 0.025 0.15 0.33200795 0.33590933 0.886481636 4820 336 2441
116 0.025 0.16 0.33598410 0.34698609 0.625838696 4736 334 2525
117 0.025 0.17 0.34990060 0.35651726 0.785434046 4669 327 2592
118 0.025 0.18 0.36580517 0.36810922 0.949771627 4587 319 2674
119 0.025 0.19 0.38767396 0.37802679 0.678975808 4521 308 2740
120 0.025 0.20 0.39761431 0.39026275 0.762461205 4431 303 2830
121 0.025 0.21 0.40755467 0.40146832 0.809616160 4349 298 2912
122 0.025 0.22 0.42942346 0.41190108 0.436086744 4279 287 2982
123 0.025 0.23 0.43339960 0.42297785 0.658078120 4195 285 3066
124 0.025 0.24 0.43737575 0.43366821 0.898954290 4114 283 3147
125 0.025 0.25 0.43936382 0.44332818 0.889713900 4040 282 3221
126 0.025 0.26 0.44930417 0.45350335 0.881308379 3966 277 3295
127 0.025 0.27 0.45129225 0.46200412 0.651233430 3901 276 3360
128 0.025 0.28 0.46123260 0.47011850 0.713840475 3843 271 3418
129 0.025 0.29 0.47514911 0.48016486 0.851906542 3772 264 3489
130 0.025 0.30 0.48707753 0.49072643 0.901979268 3696 258 3565
131 0.025 0.31 0.49105368 0.49819681 0.775479434 3640 256 3621
132 0.025 0.32 0.50298211 0.50695518 0.890090387 3578 250 3683
133 0.025 0.33 0.51292247 0.51609995 0.919290631 3512 245 3749
134 0.025 0.34 0.52087475 0.52460072 0.899044193 3450 241 3811
135 0.025 0.35 0.52683897 0.53207110 0.843842351 3395 238 3866
136 0.025 0.36 0.53479125 0.54018547 0.837762818 3336 234 3925
137 0.025 0.37 0.54075547 0.54662545 0.820306555 3289 231 3972
138 0.025 0.38 0.54870775 0.55319423 0.870585870 3242 227 4019
139 0.025 0.39 0.55467197 0.56169500 0.778099737 3179 224 4082
140 0.025 0.40 0.56262425 0.56800618 0.837234219 3134 220 4127
141 0.025 0.41 0.57057654 0.57534776 0.859334999 3081 216 4180
142 0.025 0.42 0.58250497 0.58346213 1.000000000 3024 210 4237
143 0.025 0.43 0.59045726 0.59222050 0.971042222 2960 206 4301
144 0.025 0.44 0.59443340 0.59917568 0.859210558 2908 204 4353
145 0.025 0.45 0.59840954 0.60625966 0.744848493 2855 202 4406
146 0.025 0.46 0.60437376 0.61385884 0.685869924 2799 199 4462
147 0.025 0.47 0.60834990 0.61939722 0.631090200 2758 197 4503
148 0.025 0.48 0.61232604 0.62686759 0.515928645 2702 195 4559
149 0.025 0.49 0.62027833 0.63614116 0.473534988 2634 191 4627
150 0.025 0.50 0.62425447 0.64129315 0.437855930 2596 189 4665
151 0.025 0.51 0.63419483 0.64863472 0.513635736 2544 184 4717
152 0.025 0.52 0.64015905 0.65546110 0.485016268 2494 181 4767
153 0.025 0.53 0.64612326 0.66293148 0.437833235 2439 178 4822
154 0.025 0.54 0.65208748 0.67014426 0.399984192 2386 175 4875
155 0.025 0.55 0.66003976 0.67426584 0.512596910 2358 171 4903
156 0.025 0.56 0.66600398 0.67993302 0.520194405 2317 168 4944
157 0.025 0.57 0.66799205 0.68624420 0.388382165 2269 167 4992
158 0.025 0.58 0.67594433 0.69448738 0.376935645 2209 163 5052
159 0.025 0.59 0.67793241 0.70118496 0.259431700 2158 162 5103
160 0.025 0.60 0.68190855 0.70839773 0.193284336 2104 160 5157
161 0.025 0.61 0.68787276 0.71535291 0.173439349 2053 157 5208
162 0.025 0.62 0.69781312 0.72140649 0.242366590 2011 152 5250
163 0.025 0.63 0.70775348 0.72784647 0.319637974 1966 147 5295
164 0.025 0.64 0.70775348 0.73364245 0.191530398 1921 147 5340
165 0.025 0.65 0.70974155 0.73853684 0.142310440 1884 146 5377
166 0.025 0.66 0.71570577 0.74678001 0.108665465 1823 143 5438
167 0.025 0.67 0.72365805 0.75399279 0.114119863 1771 139 5490
168 0.025 0.68 0.73956262 0.76416795 0.197089016 1700 131 5561
169 0.025 0.69 0.74353877 0.77163833 0.134262246 1644 129 5617
170 0.025 0.70 0.74950298 0.77833591 0.120100567 1595 126 5666
171 0.025 0.71 0.75149105 0.78400309 0.075691406 1552 125 5709
172 0.025 0.72 0.76143141 0.78967027 0.121049316 1513 120 5748
173 0.025 0.73 0.76341948 0.79495106 0.079406983 1473 119 5788
174 0.025 0.74 0.77335984 0.80100464 0.121598109 1431 114 5830
175 0.025 0.75 0.77534791 0.80795981 0.062669298 1378 113 5883
176 0.025 0.76 0.78131213 0.81388460 0.059879028 1335 110 5926
177 0.025 0.77 0.79125249 0.82199897 0.071252261 1277 105 5984
178 0.025 0.78 0.79324056 0.82779495 0.039261035 1233 104 6028
179 0.025 0.79 0.80516899 0.83475013 0.074253510 1185 98 6076
180 0.025 0.80 0.80914513 0.84067491 0.052990319 1141 96 6120
181 0.025 0.81 0.82703777 0.85200927 0.117348091 1062 87 6199
182 0.025 0.82 0.83101392 0.85896445 0.072472288 1010 85 6251
183 0.025 0.83 0.83896620 0.86643483 0.071096803 956 81 6305
184 0.025 0.84 0.84691849 0.87467800 0.060813822 896 77 6365
185 0.025 0.85 0.84890656 0.88408037 0.013281537 824 76 6437
186 0.025 0.86 0.85487078 0.89283874 0.005568515 759 73 6502
187 0.025 0.87 0.86481113 0.90352911 0.003043548 681 68 6580
188 0.025 0.88 0.87475149 0.90945389 0.006445505 640 63 6621
189 0.025 0.89 0.88469185 0.91486347 0.015324859 603 58 6658
190 0.025 0.90 0.89860835 0.92168985 0.056564786 557 51 6704
191 0.025 0.91 0.91451292 0.92967543 0.198762266 503 43 6758
192 0.025 0.92 0.92047714 0.93547141 0.186329337 461 40 6800
193 0.025 0.93 0.92445328 0.94397218 0.061746956 397 38 6864
194 0.025 0.94 0.93041750 0.95324575 0.016459029 328 35 6933
195 0.025 0.95 0.94433400 0.96136012 0.053716755 272 28 6989
196 0.025 0.96 0.95427435 0.96780010 0.099694256 227 23 7034
197 0.025 0.97 0.96819085 0.97552808 0.341046705 174 16 7087
198 0.025 0.98 0.97813121 0.98286965 0.503343775 122 11 7139
199 0.025 0.99 0.99005964 0.99356002 0.467435863 45 5 7216
200 0.025 1.00 1.00000000 1.00000000 0.000000000 0 0 7261
201 0.050 0.01 0.06424581 0.06053581 0.722834149 6624 670 424
202 0.050 0.02 0.10195531 0.10059248 0.950527352 6340 643 708
203 0.050 0.03 0.13547486 0.13459557 0.988118676 6100 619 948
204 0.050 0.04 0.16620112 0.15996909 0.671600828 5925 597 1123
205 0.050 0.05 0.18156425 0.18044822 0.975664616 5777 586 1271
206 0.050 0.06 0.20391061 0.20002576 0.822975727 5641 570 1407
207 0.050 0.07 0.22346369 0.21818650 0.755529424 5514 556 1534
208 0.050 0.08 0.24720670 0.23480165 0.437957152 5402 539 1646
209 0.050 0.09 0.26256983 0.25270479 0.553571566 5274 528 1774
210 0.050 0.10 0.27932961 0.26841834 0.517462717 5164 516 1884
211 0.050 0.11 0.29050279 0.28065430 0.567371353 5077 508 1971
212 0.050 0.12 0.30586592 0.29533745 0.545067059 4974 497 2074
213 0.050 0.13 0.31703911 0.30873261 0.643707139 4878 489 2170
214 0.050 0.14 0.32681564 0.32393096 0.895609587 4767 482 2281
215 0.050 0.15 0.33659218 0.33590933 1.000000000 4681 475 2367
216 0.050 0.16 0.34357542 0.34698609 0.872859514 4600 470 2448
217 0.050 0.17 0.36033520 0.35651726 0.854861769 4538 458 2510
218 0.050 0.18 0.37430168 0.36810922 0.749020878 4458 448 2590
219 0.050 0.19 0.39106145 0.37802679 0.474914770 4393 436 2655
220 0.050 0.20 0.39944134 0.39026275 0.625385818 4304 430 2744
221 0.050 0.21 0.40782123 0.40146832 0.745964463 4223 424 2825
222 0.050 0.22 0.42458101 0.41190108 0.494170694 4154 412 2894
223 0.050 0.23 0.42737430 0.42297785 0.833488584 4070 410 2978
224 0.050 0.24 0.43854749 0.43366821 0.812708152 3995 402 3053
225 0.050 0.25 0.44413408 0.44332818 0.995147740 3924 398 3124
226 0.050 0.26 0.45670391 0.45350335 0.887743736 3854 389 3194
227 0.050 0.27 0.45810056 0.46200412 0.856715309 3789 388 3259
228 0.050 0.28 0.46648045 0.47011850 0.868615836 3732 382 3316
229 0.050 0.29 0.47625698 0.48016486 0.856823301 3661 375 3387
230 0.050 0.30 0.48603352 0.49072643 0.822438043 3586 368 3462
231 0.050 0.31 0.49162011 0.49819681 0.741260912 3532 364 3516
232 0.050 0.32 0.50418994 0.50695518 0.907567440 3473 355 3575
233 0.050 0.33 0.52094972 0.51609995 0.815525762 3414 343 3634
234 0.050 0.34 0.52793296 0.52460072 0.882245165 3353 338 3695
235 0.050 0.35 0.53491620 0.53207110 0.903824747 3300 333 3748
236 0.050 0.36 0.54469274 0.54018547 0.830049072 3244 326 3804
237 0.050 0.37 0.55027933 0.54662545 0.867577032 3198 322 3850
238 0.050 0.38 0.55726257 0.55319423 0.849018663 3152 317 3896
239 0.050 0.39 0.56564246 0.56169500 0.854087455 3092 311 3956
240 0.050 0.40 0.57402235 0.56800618 0.763033792 3049 305 3999
241 0.050 0.41 0.58240223 0.57534776 0.717992887 2998 299 4050
242 0.050 0.42 0.59497207 0.58346213 0.537948432 2944 290 4104
243 0.050 0.43 0.60474860 0.59222050 0.498999451 2883 283 4165
244 0.050 0.44 0.61033520 0.59917568 0.548835772 2833 279 4215
245 0.050 0.45 0.61452514 0.60625966 0.663580278 2781 276 4267
246 0.050 0.46 0.61871508 0.61385884 0.810448317 2725 273 4323
247 0.050 0.47 0.62290503 0.61939722 0.870906463 2685 270 4363
248 0.050 0.48 0.63128492 0.62686759 0.829019091 2633 264 4415
249 0.050 0.49 0.63687151 0.63614116 0.998508634 2565 260 4483
250 0.050 0.50 0.64525140 0.64129315 0.848614327 2531 254 4517
251 0.050 0.51 0.65223464 0.64863472 0.864463030 2479 249 4569
252 0.050 0.52 0.65782123 0.65546110 0.921766003 2430 245 4618
253 0.050 0.53 0.66340782 0.66293148 1.000000000 2376 241 4672
254 0.050 0.54 0.66899441 0.67014426 0.978482950 2324 237 4724
255 0.050 0.55 0.67597765 0.67426584 0.951570441 2297 232 4751
256 0.050 0.56 0.68575419 0.67993302 0.757772907 2260 225 4788
257 0.050 0.57 0.69134078 0.68624420 0.790083594 2215 221 4833
258 0.050 0.58 0.70111732 0.69448738 0.717610991 2158 214 4890
259 0.050 0.59 0.70530726 0.70118496 0.833606892 2109 211 4939
260 0.050 0.60 0.70810056 0.70839773 1.000000000 2055 209 4993
261 0.050 0.61 0.71648045 0.71535291 0.978688546 2007 203 5041
262 0.050 0.62 0.72765363 0.72140649 0.728133786 1968 195 5080
263 0.050 0.63 0.73743017 0.72784647 0.575015673 1925 188 5123
264 0.050 0.64 0.74022346 0.73364245 0.708599184 1882 186 5166
265 0.050 0.65 0.74301676 0.73853684 0.809023919 1846 184 5202
266 0.050 0.66 0.74720670 0.74678001 1.000000000 1785 181 5263
267 0.050 0.67 0.75418994 0.75399279 1.000000000 1734 176 5314
268 0.050 0.68 0.76815642 0.76416795 0.827691283 1665 166 5383
269 0.050 0.69 0.77234637 0.77163833 0.999481459 1610 163 5438
270 0.050 0.70 0.77793296 0.77833591 1.000000000 1562 159 5486
271 0.050 0.71 0.78072626 0.78400309 0.860313090 1520 157 5528
272 0.050 0.72 0.78770950 0.78967027 0.930673317 1481 152 5567
273 0.050 0.73 0.78910615 0.79495106 0.720341065 1441 151 5607
274 0.050 0.74 0.79888268 0.80100464 0.920230023 1401 144 5647
275 0.050 0.75 0.80307263 0.80795981 0.765202276 1350 141 5698
276 0.050 0.76 0.80865922 0.81388460 0.743917412 1308 137 5740
277 0.050 0.77 0.81843575 0.82199897 0.833400154 1252 130 5796
278 0.050 0.78 0.82262570 0.82779495 0.739460815 1210 127 5838
279 0.050 0.79 0.83100559 0.83475013 0.817823806 1162 121 5886
280 0.050 0.80 0.83659218 0.84067491 0.795085580 1120 117 5928
281 0.050 0.81 0.85474860 0.85200927 0.871758575 1045 104 6003
282 0.050 0.82 0.85893855 0.85896445 1.000000000 994 101 6054
283 0.050 0.83 0.86731844 0.86643483 0.987795681 942 95 6106
284 0.050 0.84 0.87430168 0.87467800 1.000000000 883 90 6165
285 0.050 0.85 0.87709497 0.88408037 0.581251737 812 88 6236
286 0.050 0.86 0.88128492 0.89283874 0.324318121 747 85 6301
287 0.050 0.87 0.88966480 0.90352911 0.210416536 670 79 6378
288 0.050 0.88 0.89664804 0.90945389 0.236041217 629 74 6419
289 0.050 0.89 0.90363128 0.91486347 0.289131233 592 69 6456
290 0.050 0.90 0.91480447 0.92168985 0.517781613 547 61 6501
291 0.050 0.91 0.92597765 0.92967543 0.741816252 493 53 6555
292 0.050 0.92 0.93435754 0.93547141 0.962115185 454 47 6594
293 0.050 0.93 0.93854749 0.94397218 0.563816756 391 44 6657
294 0.050 0.94 0.94413408 0.95324575 0.263038933 323 40 6725
295 0.050 0.95 0.95530726 0.96136012 0.435250544 268 32 6780
296 0.050 0.96 0.96508380 0.96780010 0.748177841 225 25 6823
297 0.050 0.97 0.97625698 0.97552808 0.995564929 173 17 6875
298 0.050 0.98 0.98324022 0.98286965 1.000000000 121 12 6927
299 0.050 0.99 0.99162011 0.99356002 0.662897217 44 6 7004
300 0.050 1.00 1.00000000 1.00000000 0.000000000 0 0 7048
301 0.075 0.01 0.07188841 0.06053581 0.139929431 6429 865 403
302 0.075 0.02 0.11802575 0.10059248 0.067521852 6161 822 671
303 0.075 0.03 0.14914163 0.13459557 0.181578898 5926 793 906
304 0.075 0.04 0.17489270 0.15996909 0.201504106 5753 769 1079
305 0.075 0.05 0.19098712 0.18044822 0.397284237 5609 754 1223
306 0.075 0.06 0.21137339 0.20002576 0.379095251 5476 735 1356
307 0.075 0.07 0.23175966 0.21818650 0.304300611 5354 716 1478
308 0.075 0.08 0.25429185 0.23480165 0.145603799 5246 695 1586
309 0.075 0.09 0.27145923 0.25270479 0.172459617 5123 679 1709
310 0.075 0.10 0.28648069 0.26841834 0.198052761 5015 665 1817
311 0.075 0.11 0.29828326 0.28065430 0.215709160 4931 654 1901
312 0.075 0.12 0.31652361 0.29533745 0.140716932 4834 637 1998
313 0.075 0.13 0.32939914 0.30873261 0.156161118 4742 625 2090
314 0.075 0.14 0.33798283 0.32393096 0.347266725 4632 617 2200
315 0.075 0.15 0.34656652 0.33590933 0.485571145 4547 609 2285
316 0.075 0.16 0.35193133 0.34698609 0.763092156 4466 604 2366
317 0.075 0.17 0.36587983 0.35651726 0.548704082 4405 591 2427
318 0.075 0.18 0.37768240 0.36810922 0.542003686 4326 580 2506
319 0.075 0.19 0.39163090 0.37802679 0.380457125 4262 567 2570
320 0.075 0.20 0.39914163 0.39026275 0.577822969 4174 560 2658
321 0.075 0.21 0.40772532 0.40146832 0.704101299 4095 552 2737
322 0.075 0.22 0.42274678 0.41190108 0.495440992 4028 538 2804
323 0.075 0.23 0.42703863 0.42297785 0.816410779 3946 534 2886
324 0.075 0.24 0.43884120 0.43366821 0.760764983 3874 523 2958
325 0.075 0.25 0.44527897 0.44332818 0.926179408 3805 517 3027
326 0.075 0.26 0.45600858 0.45350335 0.897593689 3736 507 3096
327 0.075 0.27 0.46459227 0.46200412 0.893459259 3678 499 3154
328 0.075 0.28 0.47424893 0.47011850 0.814717083 3624 490 3208
329 0.075 0.29 0.48497854 0.48016486 0.780538573 3556 480 3276
330 0.075 0.30 0.49356223 0.49072643 0.881012355 3482 472 3350
331 0.075 0.31 0.50000000 0.49819681 0.934289275 3430 466 3402
332 0.075 0.32 0.50965665 0.50695518 0.887924719 3371 457 3461
333 0.075 0.33 0.52360515 0.51609995 0.649957261 3313 444 3519
334 0.075 0.34 0.53218884 0.52460072 0.645846544 3255 436 3577
335 0.075 0.35 0.53862661 0.53207110 0.694629408 3203 430 3629
336 0.075 0.36 0.54613734 0.54018547 0.723619612 3147 423 3685
337 0.075 0.37 0.55150215 0.54662545 0.776612682 3102 418 3730
338 0.075 0.38 0.56008584 0.55319423 0.677403380 3059 410 3773
339 0.075 0.39 0.56866953 0.56169500 0.672826495 3001 402 3831
340 0.075 0.40 0.57725322 0.56800618 0.567133374 2960 394 3872
341 0.075 0.41 0.58369099 0.57534776 0.607250431 2909 388 3923
342 0.075 0.42 0.59442060 0.58346213 0.491447486 2856 378 3976
343 0.075 0.43 0.60300429 0.59222050 0.497373280 2796 370 4036
344 0.075 0.44 0.61051502 0.59917568 0.473127349 2749 363 4083
345 0.075 0.45 0.61587983 0.60625966 0.545133346 2699 358 4133
346 0.075 0.46 0.62017167 0.61385884 0.699406976 2644 354 4188
347 0.075 0.47 0.62339056 0.61939722 0.816766117 2604 351 4228
348 0.075 0.48 0.63304721 0.62686759 0.704143919 2555 342 4277
349 0.075 0.49 0.63733906 0.63614116 0.964313823 2487 338 4345
350 0.075 0.50 0.64377682 0.64129315 0.894884650 2453 332 4379
351 0.075 0.51 0.65236052 0.64863472 0.827883187 2404 324 4428
352 0.075 0.52 0.65879828 0.65546110 0.847898112 2357 318 4475
353 0.075 0.53 0.66416309 0.66293148 0.961829819 2304 313 4528
354 0.075 0.54 0.67274678 0.67014426 0.886281253 2256 305 4576
355 0.075 0.55 0.67918455 0.67426584 0.760887383 2230 299 4602
356 0.075 0.56 0.68669528 0.67993302 0.664051132 2193 292 4639
357 0.075 0.57 0.69206009 0.68624420 0.711176432 2149 287 4683
358 0.075 0.58 0.70064378 0.69448738 0.691320578 2093 279 4739
359 0.075 0.59 0.70600858 0.70118496 0.760511715 2046 274 4786
360 0.075 0.60 0.71030043 0.70839773 0.922069096 1994 270 4838
361 0.075 0.61 0.71888412 0.71535291 0.829000555 1948 262 4884
362 0.075 0.62 0.73068670 0.72140649 0.525596286 1912 251 4920
363 0.075 0.63 0.73819742 0.72784647 0.472968814 1869 244 4963
364 0.075 0.64 0.74141631 0.73364245 0.594155915 1827 241 5005
365 0.075 0.65 0.74463519 0.73853684 0.680402330 1792 238 5040
366 0.075 0.66 0.75000000 0.74678001 0.840829362 1733 233 5099
367 0.075 0.67 0.75536481 0.75399279 0.949657176 1682 228 5150
368 0.075 0.68 0.76716738 0.76416795 0.850237993 1614 217 5218
369 0.075 0.69 0.77038627 0.77163833 0.955757850 1559 214 5273
370 0.075 0.70 0.77575107 0.77833591 0.872494232 1512 209 5320
371 0.075 0.71 0.77789700 0.78400309 0.659594562 1470 207 5362
372 0.075 0.72 0.78540773 0.78967027 0.766049634 1433 200 5399
373 0.075 0.73 0.78969957 0.79495106 0.703895818 1396 196 5436
374 0.075 0.74 0.79721030 0.80100464 0.790574809 1356 189 5476
375 0.075 0.75 0.80042918 0.80795981 0.563360335 1305 186 5527
376 0.075 0.76 0.80579399 0.81388460 0.527604395 1264 181 5568
377 0.075 0.77 0.81652361 0.82199897 0.674337480 1211 171 5621
378 0.075 0.78 0.82403433 0.82779495 0.781080667 1173 164 5659
379 0.075 0.79 0.83047210 0.83475013 0.743022925 1125 158 5707
380 0.075 0.80 0.83476395 0.84067491 0.632705611 1083 154 5749
381 0.075 0.81 0.84978541 0.85200927 0.877096778 1009 140 5823
382 0.075 0.82 0.85407725 0.85896445 0.684151563 959 136 5873
383 0.075 0.83 0.86266094 0.86643483 0.756779492 909 128 5923
384 0.075 0.84 0.87017167 0.87467800 0.696371731 852 121 5980
385 0.075 0.85 0.87446352 0.88408037 0.355947049 783 117 6049
386 0.075 0.86 0.87875536 0.89283874 0.154065821 719 113 6113
387 0.075 0.87 0.88841202 0.90352911 0.107999655 645 104 6187
388 0.075 0.88 0.89592275 0.90945389 0.140555562 606 97 6226
389 0.075 0.89 0.90236052 0.91486347 0.162887251 570 91 6262
390 0.075 0.90 0.91309013 0.92168985 0.328691164 527 81 6305
391 0.075 0.91 0.92381974 0.92967543 0.498390762 475 71 6357
392 0.075 0.92 0.93025751 0.93547141 0.535540018 436 65 6396
393 0.075 0.93 0.93669528 0.94397218 0.340157153 376 59 6456
394 0.075 0.94 0.94098712 0.95324575 0.070754028 308 55 6524
395 0.075 0.95 0.95171674 0.96136012 0.124108944 255 45 6577
396 0.075 0.96 0.96137339 0.96780010 0.277524044 214 36 6618
397 0.075 0.97 0.97317597 0.97552808 0.702143270 165 25 6667
398 0.075 0.98 0.98175966 0.98286965 0.885623480 116 17 6716
399 0.075 0.99 0.99034335 0.99356002 0.275517517 41 9 6791
400 0.075 1.00 1.00000000 1.00000000 0.000000000 0 0 6832
401 0.100 0.01 0.07416880 0.06053581 0.039535617 6208 1086 383
402 0.100 0.02 0.12105712 0.10059248 0.013272120 5952 1031 639
403 0.100 0.03 0.15260017 0.13459557 0.055548558 5725 994 866
404 0.100 0.04 0.17817562 0.15996909 0.071392693 5558 964 1033
405 0.100 0.05 0.19522592 0.18044822 0.165373091 5419 944 1172
406 0.100 0.06 0.21909633 0.20002576 0.083178631 5295 916 1296
407 0.100 0.07 0.23870418 0.21818650 0.070565846 5177 893 1414
408 0.100 0.08 0.26086957 0.23480165 0.024533853 5074 867 1517
409 0.100 0.09 0.27706735 0.25270479 0.040610689 4954 848 1637
410 0.100 0.10 0.29070759 0.26841834 0.066661004 4848 832 1743
411 0.100 0.11 0.30434783 0.28065430 0.054242628 4769 816 1822
412 0.100 0.12 0.31884058 0.29533745 0.060057790 4672 799 1919
413 0.100 0.13 0.33162830 0.30873261 0.070609396 4583 784 2008
414 0.100 0.14 0.34271100 0.32393096 0.144875453 4478 771 2113
415 0.100 0.15 0.35038363 0.33590933 0.268889349 4394 762 2197
416 0.100 0.16 0.35720375 0.34698609 0.444499765 4316 754 2275
417 0.100 0.17 0.37169650 0.35651726 0.252228939 4259 737 2332
418 0.100 0.18 0.38277920 0.36810922 0.272284180 4182 724 2409
419 0.100 0.19 0.39386189 0.37802679 0.237506400 4118 711 2473
420 0.100 0.20 0.39982950 0.39026275 0.486101219 4030 704 2561
421 0.100 0.21 0.41261722 0.40146832 0.416155367 3958 689 2633
422 0.100 0.22 0.42796249 0.41190108 0.237660400 3895 671 2696
423 0.100 0.23 0.43307758 0.42297785 0.466703686 3815 665 2776
424 0.100 0.24 0.44757033 0.43366821 0.312119745 3749 648 2842
425 0.100 0.25 0.45609548 0.44332818 0.355781273 3684 638 2907
426 0.100 0.26 0.46632566 0.45350335 0.354662490 3617 626 2974
427 0.100 0.27 0.47485081 0.46200412 0.354413415 3561 616 3030
428 0.100 0.28 0.48422847 0.47011850 0.308143831 3509 605 3082
429 0.100 0.29 0.49360614 0.48016486 0.332869047 3442 594 3149
430 0.100 0.30 0.50298380 0.49072643 0.379008780 3371 583 3220
431 0.100 0.31 0.51150895 0.49819681 0.338065225 3323 573 3268
432 0.100 0.32 0.52088662 0.50695518 0.315317544 3266 562 3325
433 0.100 0.33 0.53452685 0.51609995 0.180591811 3211 546 3380
434 0.100 0.34 0.54305200 0.52460072 0.179700441 3155 536 3436
435 0.100 0.35 0.54816709 0.53207110 0.243066891 3103 530 3488
436 0.100 0.36 0.55498721 0.54018547 0.283630239 3048 522 3543
437 0.100 0.37 0.56010230 0.54662545 0.329818316 3004 516 3587
438 0.100 0.38 0.56947997 0.55319423 0.235706288 2964 505 3627
439 0.100 0.39 0.57630009 0.56169500 0.288132051 2906 497 3685
440 0.100 0.40 0.58397272 0.56800618 0.243548127 2866 488 3725
441 0.100 0.41 0.59079284 0.57534776 0.258704688 2817 480 3774
442 0.100 0.42 0.59931799 0.58346213 0.244658478 2764 470 3827
443 0.100 0.43 0.60784314 0.59222050 0.250357802 2706 460 3885
444 0.100 0.44 0.61466326 0.59917568 0.253280432 2660 452 3931
445 0.100 0.45 0.61892583 0.60625966 0.351731030 2610 447 3981
446 0.100 0.46 0.62318841 0.61385884 0.496651652 2556 442 4035
447 0.100 0.47 0.62574595 0.61939722 0.650247777 2516 439 4075
448 0.100 0.48 0.63597613 0.62686759 0.504571413 2470 427 4121
449 0.100 0.49 0.64109122 0.63614116 0.726696539 2404 421 4187
450 0.100 0.50 0.64705882 0.64129315 0.679005629 2371 414 4220
451 0.100 0.51 0.65643649 0.64863472 0.565774123 2325 403 4266
452 0.100 0.52 0.66410912 0.65546110 0.520149694 2281 394 4310
453 0.100 0.53 0.67263427 0.66293148 0.465713137 2233 384 4358
454 0.100 0.54 0.67945439 0.67014426 0.482441851 2185 376 4406
455 0.100 0.55 0.68456948 0.67426584 0.433363575 2159 370 4432
456 0.100 0.56 0.69224211 0.67993302 0.343713656 2124 361 4467
457 0.100 0.57 0.69735720 0.68624420 0.391940584 2081 355 4510
458 0.100 0.58 0.70502984 0.69448738 0.414288709 2026 346 4565
459 0.100 0.59 0.71099744 0.70118496 0.445918882 1981 339 4610
460 0.100 0.60 0.71526002 0.70839773 0.598623766 1930 334 4661
461 0.100 0.61 0.72293265 0.71535291 0.555674903 1885 325 4706
462 0.100 0.62 0.73401535 0.72140649 0.312429470 1851 312 4740
463 0.100 0.63 0.74254049 0.72784647 0.233402276 1811 302 4780
464 0.100 0.64 0.74680307 0.73364245 0.284248148 1771 297 4820
465 0.100 0.65 0.75021313 0.73853684 0.341272978 1737 293 4854
466 0.100 0.66 0.75532822 0.74678001 0.487510179 1679 287 4912
467 0.100 0.67 0.76214834 0.75399279 0.504700978 1631 279 4960
468 0.100 0.68 0.77408355 0.76416795 0.406022251 1566 265 5025
469 0.100 0.69 0.77749361 0.77163833 0.630694509 1512 261 5079
470 0.100 0.70 0.78346121 0.77833591 0.674100145 1467 254 5124
471 0.100 0.71 0.78772379 0.78400309 0.766018060 1428 249 5163
472 0.100 0.72 0.79369139 0.78967027 0.742996782 1391 242 5200
473 0.100 0.73 0.79795396 0.79495106 0.812477064 1355 237 5236
474 0.100 0.74 0.80562660 0.80100464 0.696059848 1317 228 5274
475 0.100 0.75 0.80818414 0.80795981 1.000000000 1266 225 5325
476 0.100 0.76 0.81500426 0.81388460 0.947197311 1228 217 5363
477 0.100 0.77 0.82438193 0.82199897 0.849192603 1176 206 5415
478 0.100 0.78 0.83120205 0.82779495 0.769159633 1139 198 5452
479 0.100 0.79 0.83631714 0.83475013 0.909101885 1091 192 5500
480 0.100 0.80 0.83972720 0.84067491 0.957760801 1049 188 5542
481 0.100 0.81 0.85421995 0.85200927 0.851818667 978 171 5613
482 0.100 0.82 0.85763001 0.85896445 0.922732253 928 167 5663
483 0.100 0.83 0.86445013 0.86643483 0.864781130 878 159 5713
484 0.100 0.84 0.87212276 0.87467800 0.811083253 823 150 5768
485 0.100 0.85 0.87809037 0.88408037 0.518253735 757 143 5834
486 0.100 0.86 0.88235294 0.89283874 0.226702732 694 138 5897
487 0.100 0.87 0.89343564 0.90352911 0.223542653 624 125 5967
488 0.100 0.88 0.90110827 0.90945389 0.304965352 587 116 6004
489 0.100 0.89 0.90707587 0.91486347 0.326851232 552 109 6039
490 0.100 0.90 0.91645354 0.92168985 0.505714396 510 98 6081
491 0.100 0.91 0.92497869 0.92967543 0.534710614 458 88 6133
492 0.100 0.92 0.93009378 0.93547141 0.453785969 419 82 6172
493 0.100 0.93 0.93520887 0.94397218 0.177800701 359 76 6232
494 0.100 0.94 0.94373402 0.95324575 0.109655092 297 66 6294
495 0.100 0.95 0.95481671 0.96136012 0.238083633 247 53 6344
496 0.100 0.96 0.96419437 0.96780010 0.503176101 208 42 6383
497 0.100 0.97 0.97442455 0.97552808 0.870567064 160 30 6431
498 0.100 0.98 0.98294970 0.98286965 1.000000000 113 20 6478
499 0.100 0.99 0.99232737 0.99356002 0.707856473 41 9 6550
500 0.100 1.00 1.00000000 1.00000000 0.000000000 0 0 6591
Dboth
1 20
2 33
3 47
4 56
5 64
6 72
7 77
8 89
9 95
10 99
11 102
12 106
13 108
14 113
15 118
16 120
17 125
18 131
19 140
20 143
21 148
22 157
23 158
24 159
25 160
26 162
27 162
28 165
29 170
30 175
31 176
32 180
33 183
34 184
35 187
36 188
37 190
38 193
39 195
40 198
41 199
42 204
43 206
44 208
45 209
46 209
47 211
48 213
49 216
50 218
51 222
52 225
53 227
54 228
55 231
56 233
57 233
58 235
59 236
60 237
61 238
62 243
63 247
64 247
65 247
66 250
67 253
68 259
69 261
70 264
71 264
72 267
73 268
74 273
75 274
76 276
77 281
78 282
79 286
80 286
81 293
82 294
83 298
84 302
85 303
86 306
87 309
88 313
89 316
90 322
91 326
92 326
93 327
94 330
95 335
96 338
97 344
98 348
99 353
100 357
101 30
102 48
103 67
104 82
105 91
106 103
107 110
108 123
109 133
110 139
111 143
112 148
113 154
114 161
115 167
116 169
117 176
118 184
119 195
120 200
121 205
122 216
123 218
124 220
125 221
126 226
127 227
128 232
129 239
130 245
131 247
132 253
133 258
134 262
135 265
136 269
137 272
138 276
139 279
140 283
141 287
142 293
143 297
144 299
145 301
146 304
147 306
148 308
149 312
150 314
151 319
152 322
153 325
154 328
155 332
156 335
157 336
158 340
159 341
160 343
161 346
162 351
163 356
164 356
165 357
166 360
167 364
168 372
169 374
170 377
171 378
172 383
173 384
174 389
175 390
176 393
177 398
178 399
179 405
180 407
181 416
182 418
183 422
184 426
185 427
186 430
187 435
188 440
189 445
190 452
191 460
192 463
193 465
194 468
195 475
196 480
197 487
198 492
199 498
200 503
201 46
202 73
203 97
204 119
205 130
206 146
207 160
208 177
209 188
210 200
211 208
212 219
213 227
214 234
215 241
216 246
217 258
218 268
219 280
220 286
221 292
222 304
223 306
224 314
225 318
226 327
227 328
228 334
229 341
230 348
231 352
232 361
233 373
234 378
235 383
236 390
237 394
238 399
239 405
240 411
241 417
242 426
243 433
244 437
245 440
246 443
247 446
248 452
249 456
250 462
251 467
252 471
253 475
254 479
255 484
256 491
257 495
258 502
259 505
260 507
261 513
262 521
263 528
264 530
265 532
266 535
267 540
268 550
269 553
270 557
271 559
272 564
273 565
274 572
275 575
276 579
277 586
278 589
279 595
280 599
281 612
282 615
283 621
284 626
285 628
286 631
287 637
288 642
289 647
290 655
291 663
292 669
293 672
294 676
295 684
296 691
297 699
298 704
299 710
300 716
301 67
302 110
303 139
304 163
305 178
306 197
307 216
308 237
309 253
310 267
311 278
312 295
313 307
314 315
315 323
316 328
317 341
318 352
319 365
320 372
321 380
322 394
323 398
324 409
325 415
326 425
327 433
328 442
329 452
330 460
331 466
332 475
333 488
334 496
335 502
336 509
337 514
338 522
339 530
340 538
341 544
342 554
343 562
344 569
345 574
346 578
347 581
348 590
349 594
350 600
351 608
352 614
353 619
354 627
355 633
356 640
357 645
358 653
359 658
360 662
361 670
362 681
363 688
364 691
365 694
366 699
367 704
368 715
369 718
370 723
371 725
372 732
373 736
374 743
375 746
376 751
377 761
378 768
379 774
380 778
381 792
382 796
383 804
384 811
385 815
386 819
387 828
388 835
389 841
390 851
391 861
392 867
393 873
394 877
395 887
396 896
397 907
398 915
399 923
400 932
401 87
402 142
403 179
404 209
405 229
406 257
407 280
408 306
409 325
410 341
411 357
412 374
413 389
414 402
415 411
416 419
417 436
418 449
419 462
420 469
421 484
422 502
423 508
424 525
425 535
426 547
427 557
428 568
429 579
430 590
431 600
432 611
433 627
434 637
435 643
436 651
437 657
438 668
439 676
440 685
441 693
442 703
443 713
444 721
445 726
446 731
447 734
448 746
449 752
450 759
451 770
452 779
453 789
454 797
455 803
456 812
457 818
458 827
459 834
460 839
461 848
462 861
463 871
464 876
465 880
466 886
467 894
468 908
469 912
470 919
471 924
472 931
473 936
474 945
475 948
476 956
477 967
478 975
479 981
480 985
481 1002
482 1006
483 1014
484 1023
485 1030
486 1035
487 1048
488 1057
489 1064
490 1075
491 1085
492 1091
493 1097
494 1107
495 1120
496 1131
497 1143
498 1153
499 1164
500 1173enrichment.plotter(gene.hic.filt, "min_FDR.C", "adj.P.Val", "Minimum FDR of Hi-C Contacts Overlapping Gene, Chimp")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.03719912 0.04602134 0.41639016 6981 440 341
2 0.010 0.02 0.05470460 0.06029053 0.67753993 6878 432 444
3 0.010 0.03 0.06345733 0.06877491 0.71307165 6816 428 506
4 0.010 0.04 0.07658643 0.08073017 0.80516417 6729 422 593
5 0.010 0.05 0.08533917 0.09114282 0.71842910 6652 418 670
6 0.010 0.06 0.10284464 0.10309808 1.00000000 6567 410 755
7 0.010 0.07 0.11816193 0.11261088 0.75605305 6500 403 822
8 0.010 0.08 0.13566740 0.12366628 0.46536133 6422 395 900
9 0.010 0.09 0.15098468 0.13793547 0.44491130 6318 388 1004
10 0.010 0.10 0.17286652 0.14924798 0.16366582 6240 378 1082
11 0.010 0.11 0.18161926 0.15953207 0.20649139 6164 374 1158
12 0.010 0.12 0.19474836 0.17380126 0.24832927 6059 368 1263
13 0.010 0.13 0.19912473 0.18524232 0.46825115 5972 366 1350
14 0.010 0.14 0.21006565 0.19822599 0.55256566 5876 361 1446
15 0.010 0.15 0.22757112 0.21262373 0.45564427 5772 353 1550
16 0.010 0.16 0.23632385 0.22355058 0.53677763 5691 349 1631
17 0.010 0.17 0.25601751 0.24051935 0.45772189 5568 340 1754
18 0.010 0.18 0.26914661 0.25388867 0.47332973 5470 334 1852
19 0.010 0.19 0.27789934 0.26828641 0.67181507 5362 330 1960
20 0.010 0.20 0.29321663 0.28486952 0.72327476 5240 323 2082
21 0.010 0.21 0.30415755 0.29605348 0.73509898 5158 318 2164
22 0.010 0.22 0.32822757 0.31302224 0.50252673 5037 307 2285
23 0.010 0.23 0.35010941 0.32536316 0.26597090 4951 297 2371
24 0.010 0.24 0.37417943 0.34156061 0.14298807 4836 286 2486
25 0.010 0.25 0.38512035 0.35878648 0.24625179 4707 281 2615
26 0.010 0.26 0.40043764 0.37652655 0.29942638 4576 274 2746
27 0.010 0.27 0.42013129 0.39400951 0.25906713 4449 265 2873
28 0.010 0.28 0.43326039 0.41136393 0.35159652 4320 259 3002
29 0.010 0.29 0.44201313 0.42666152 0.52530646 4205 255 3117
30 0.010 0.30 0.46170678 0.44375884 0.45477419 4081 246 3241
31 0.010 0.31 0.47264770 0.45879933 0.57276175 3969 241 3353
32 0.010 0.32 0.48140044 0.47229721 0.72371427 3868 237 3454
33 0.010 0.33 0.49890591 0.49157989 0.78356615 3726 229 3596
34 0.010 0.34 0.50765864 0.50803445 1.00000000 3602 225 3720
35 0.010 0.35 0.51641138 0.52230364 0.83237068 3495 221 3827
36 0.010 0.36 0.53391685 0.53451600 1.00000000 3408 213 3914
37 0.010 0.37 0.55798687 0.54968505 0.74956060 3301 202 4021
38 0.010 0.38 0.56673961 0.56742512 1.00000000 3167 198 4155
39 0.010 0.39 0.59080963 0.58645070 0.88385923 3030 187 4292
40 0.010 0.40 0.60612691 0.60264816 0.91448948 2911 180 4411
41 0.010 0.41 0.61925602 0.61588893 0.91798432 2814 174 4508
42 0.010 0.42 0.63894967 0.63092943 0.75180079 2706 165 4616
43 0.010 0.43 0.64332604 0.64622702 0.93363786 2589 163 4733
44 0.010 0.44 0.65645514 0.65972490 0.91940643 2490 157 4832
45 0.010 0.45 0.67177243 0.67630801 0.87124764 2368 150 4954
46 0.010 0.46 0.67614880 0.68903458 0.57458454 2271 148 5051
47 0.010 0.47 0.68927790 0.70111840 0.60496292 2183 142 5139
48 0.010 0.48 0.70240700 0.71294511 0.64552123 2097 136 5225
49 0.010 0.49 0.72866521 0.72310066 0.82576289 2030 124 5292
50 0.010 0.50 0.73522976 0.73415606 1.00000000 1947 121 5375
51 0.010 0.51 0.74398249 0.74238334 0.97969782 1887 117 5435
52 0.010 0.52 0.76148796 0.75408150 0.74670064 1804 109 5518
53 0.010 0.53 0.76805252 0.76385139 0.87193928 1731 106 5591
54 0.010 0.54 0.76805252 0.77143592 0.90437926 1672 106 5650
55 0.010 0.55 0.77242888 0.78133436 0.67710768 1597 104 5725
56 0.010 0.56 0.78118162 0.78891888 0.71981431 1542 100 5780
57 0.010 0.57 0.78555799 0.79637486 0.59471808 1486 98 5836
58 0.010 0.58 0.80087527 0.80460213 0.88367846 1429 91 5893
59 0.010 0.59 0.80525164 0.81218666 0.74175482 1372 89 5950
60 0.010 0.60 0.81400438 0.81951408 0.80027936 1319 85 6003
61 0.010 0.61 0.81400438 0.82529888 0.55389831 1274 85 6048
62 0.010 0.62 0.82932166 0.83185499 0.93242404 1230 78 6092
63 0.010 0.63 0.83150985 0.83879676 0.71057101 1177 77 6145
64 0.010 0.64 0.84682713 0.84560998 0.99401196 1131 70 6191
65 0.010 0.65 0.85557987 0.85229464 0.89176115 1083 66 6239
66 0.010 0.66 0.85995624 0.85936496 1.00000000 1030 64 6292
67 0.010 0.67 0.86870897 0.86502121 0.86717112 990 60 6332
68 0.010 0.68 0.87308534 0.86913485 0.85195524 960 58 6362
69 0.010 0.69 0.87527352 0.87401980 0.99154232 923 57 6399
70 0.010 0.70 0.89059081 0.88031881 0.53325817 881 50 6441
71 0.010 0.71 0.89277899 0.88520375 0.65415798 844 49 6478
72 0.010 0.72 0.89934354 0.89137421 0.62636358 799 46 6523
73 0.010 0.73 0.89934354 0.89535930 0.83518651 768 46 6554
74 0.010 0.74 0.90371991 0.89973004 0.83176661 736 44 6586
75 0.010 0.75 0.90590810 0.90448644 0.98040851 700 43 6622
76 0.010 0.76 0.91028446 0.90847153 0.95619447 671 41 6651
77 0.010 0.77 0.91028446 0.91399923 0.83681809 628 41 6694
78 0.010 0.78 0.91903720 0.91862707 1.00000000 596 37 6726
79 0.010 0.79 0.92341357 0.92248361 1.00000000 568 35 6754
80 0.010 0.80 0.92560175 0.92646870 1.00000000 538 34 6784
81 0.010 0.81 0.92778993 0.92929682 0.97169289 517 33 6805
82 0.010 0.82 0.93435449 0.93366757 1.00000000 486 30 6836
83 0.010 0.83 0.94091904 0.93906672 0.94431784 447 27 6875
84 0.010 0.84 0.94310722 0.94215195 1.00000000 424 26 6898
85 0.010 0.85 0.94748359 0.94575138 0.95050261 398 24 6924
86 0.010 0.86 0.95404814 0.95037923 0.79389239 365 21 6957
87 0.010 0.87 0.95623632 0.95410721 0.91321112 337 20 6985
88 0.010 0.88 0.96061269 0.95834940 0.89739744 306 18 7016
89 0.010 0.89 0.96280088 0.96143463 0.97515567 283 17 7039
90 0.010 0.90 0.96717724 0.96490551 0.88786089 258 15 7064
91 0.010 0.91 0.97155361 0.96734799 0.69966413 241 13 7081
92 0.010 0.92 0.97155361 0.97159018 1.00000000 208 13 7114
93 0.010 0.93 0.97592998 0.97544672 1.00000000 180 11 7142
94 0.010 0.94 0.97592998 0.97827484 0.85005538 158 11 7164
95 0.010 0.95 0.98468271 0.98213138 0.80848152 132 7 7190
96 0.010 0.96 0.98905908 0.98624502 0.74488621 102 5 7220
97 0.010 0.97 0.99124726 0.99010156 0.99083340 73 4 7249
98 0.010 0.98 0.99124726 0.99305823 0.84911228 50 4 7272
99 0.010 0.99 0.99781182 0.99652912 0.94366508 26 1 7296
100 0.010 1.00 1.00000000 1.00000000 0.00000000 0 0 7322
101 0.025 0.01 0.03913043 0.04602134 0.32781229 6537 884 322
102 0.025 0.02 0.05652174 0.06029053 0.66160468 6442 868 417
103 0.025 0.03 0.06304348 0.06877491 0.50785313 6382 862 477
104 0.025 0.04 0.07608696 0.08073017 0.62688337 6301 850 558
105 0.025 0.05 0.08695652 0.09114282 0.68265658 6230 840 629
106 0.025 0.06 0.10217391 0.10309808 0.96774296 6151 826 708
107 0.025 0.07 0.11521739 0.11261088 0.83303776 6089 814 770
108 0.025 0.08 0.13152174 0.12366628 0.47308799 6018 799 841
109 0.025 0.09 0.14782609 0.13793547 0.38125784 5922 784 937
110 0.025 0.10 0.16521739 0.14924798 0.16200280 5850 768 1009
111 0.025 0.11 0.17500000 0.15953207 0.18798773 5779 759 1080
112 0.025 0.12 0.19021739 0.17380126 0.17604726 5682 745 1177
113 0.025 0.13 0.19565217 0.18524232 0.41201708 5598 740 1261
114 0.025 0.14 0.20978261 0.19822599 0.37221093 5510 727 1349
115 0.025 0.15 0.22500000 0.21262373 0.35022769 5412 713 1447
116 0.025 0.16 0.23152174 0.22355058 0.56469463 5333 707 1526
117 0.025 0.17 0.25108696 0.24051935 0.44869172 5219 689 1640
118 0.025 0.18 0.26739130 0.25388867 0.33615708 5130 674 1729
119 0.025 0.19 0.28152174 0.26828641 0.35481229 5031 661 1828
120 0.025 0.20 0.29565217 0.28486952 0.46369146 4915 648 1944
121 0.025 0.21 0.30434783 0.29605348 0.58339689 4836 640 2023
122 0.025 0.22 0.31956522 0.31302224 0.67601381 4718 626 2141
123 0.025 0.23 0.33586957 0.32536316 0.49214601 4637 611 2222
124 0.025 0.24 0.35434783 0.34156061 0.40430099 4528 594 2331
125 0.025 0.25 0.36847826 0.35878648 0.53783272 4407 581 2452
126 0.025 0.26 0.38586957 0.37652655 0.55743919 4285 565 2574
127 0.025 0.27 0.40108696 0.39400951 0.66579041 4163 551 2696
128 0.025 0.28 0.41630435 0.41136393 0.77286554 4042 537 2817
129 0.025 0.29 0.42608696 0.42666152 0.99837992 3932 528 2927
130 0.025 0.30 0.44347826 0.44375884 1.00000000 3815 512 3044
131 0.025 0.31 0.46304348 0.45879933 0.81041430 3716 494 3143
132 0.025 0.32 0.46956522 0.47229721 0.88739359 3617 488 3242
133 0.025 0.33 0.49021739 0.49157989 0.95779620 3486 469 3373
134 0.025 0.34 0.50217391 0.50803445 0.73118883 3369 458 3490
135 0.025 0.35 0.50978261 0.52230364 0.43859878 3265 451 3594
136 0.025 0.36 0.52500000 0.53451600 0.56121225 3184 437 3675
137 0.025 0.37 0.54565217 0.54968505 0.82077505 3085 418 3774
138 0.025 0.38 0.55326087 0.56742512 0.37450864 2954 411 3905
139 0.025 0.39 0.57934783 0.58645070 0.66702173 2830 387 4029
140 0.025 0.40 0.59673913 0.60264816 0.72320656 2720 371 4139
141 0.025 0.41 0.60978261 0.61588893 0.71180054 2629 359 4230
142 0.025 0.42 0.63260870 0.63092943 0.93939605 2533 338 4326
143 0.025 0.43 0.64347826 0.64622702 0.88156759 2424 328 4435
144 0.025 0.44 0.65652174 0.65972490 0.85611214 2331 316 4528
145 0.025 0.45 0.66956522 0.67630801 0.66866048 2214 304 4645
146 0.025 0.46 0.67500000 0.68903458 0.34647540 2120 299 4739
147 0.025 0.47 0.68478261 0.70111840 0.26512452 2035 290 4824
148 0.025 0.48 0.70000000 0.71294511 0.37588142 1957 276 4902
149 0.025 0.49 0.71630435 0.72310066 0.65171620 1893 261 4966
150 0.025 0.50 0.72826087 0.73415606 0.69557495 1818 250 5041
151 0.025 0.51 0.73586957 0.74238334 0.65922651 1761 243 5098
152 0.025 0.52 0.74891304 0.75408150 0.72865073 1682 231 5177
153 0.025 0.53 0.75434783 0.76385139 0.49558018 1611 226 5248
154 0.025 0.54 0.75978261 0.77143592 0.39275518 1557 221 5302
155 0.025 0.55 0.76956522 0.78133436 0.38034493 1489 212 5370
156 0.025 0.56 0.77500000 0.78891888 0.28971578 1435 207 5424
157 0.025 0.57 0.77826087 0.79637486 0.15871600 1380 204 5479
158 0.025 0.58 0.78804348 0.80460213 0.19199912 1325 195 5534
159 0.025 0.59 0.79565217 0.81218666 0.18598721 1273 188 5586
160 0.025 0.60 0.81086957 0.81951408 0.49624962 1230 174 5629
161 0.025 0.61 0.81304348 0.82529888 0.31909317 1187 172 5672
162 0.025 0.62 0.82500000 0.83185499 0.58566998 1147 161 5712
163 0.025 0.63 0.82826087 0.83879676 0.38006880 1096 158 5763
164 0.025 0.64 0.83913043 0.84560998 0.59564353 1053 148 5806
165 0.025 0.65 0.84673913 0.85229464 0.64817755 1008 141 5851
166 0.025 0.66 0.85434783 0.85936496 0.67764913 960 134 5899
167 0.025 0.67 0.85869565 0.86502121 0.58465896 920 130 5939
168 0.025 0.68 0.86739130 0.86913485 0.90849177 896 122 5963
169 0.025 0.69 0.87173913 0.87401980 0.86571282 862 118 5997
170 0.025 0.70 0.88478261 0.88031881 0.69643710 825 106 6034
171 0.025 0.71 0.88804348 0.88520375 0.81601017 790 103 6069
172 0.025 0.72 0.89347826 0.89137421 0.87130670 747 98 6112
173 0.025 0.73 0.89673913 0.89535930 0.92966982 719 95 6140
174 0.025 0.74 0.90108696 0.89973004 0.93029024 689 91 6170
175 0.025 0.75 0.90326087 0.90448644 0.94024599 654 89 6205
176 0.025 0.76 0.90869565 0.90847153 1.00000000 628 84 6231
177 0.025 0.77 0.91521739 0.91399923 0.93804094 591 78 6268
178 0.025 0.78 0.92065217 0.91862707 0.86104309 560 73 6299
179 0.025 0.79 0.92282609 0.92248361 1.00000000 532 71 6327
180 0.025 0.80 0.92391304 0.92646870 0.80334281 502 70 6357
181 0.025 0.81 0.92500000 0.92929682 0.63622388 481 69 6378
182 0.025 0.82 0.93043478 0.93366757 0.72704042 452 64 6407
183 0.025 0.83 0.93586957 0.93906672 0.72008749 415 59 6444
184 0.025 0.84 0.93913043 0.94215195 0.73169720 394 56 6465
185 0.025 0.85 0.94565217 0.94575138 1.00000000 372 50 6487
186 0.025 0.86 0.95108696 0.95037923 0.98050815 341 45 6518
187 0.025 0.87 0.95217391 0.95410721 0.83012405 313 44 6546
188 0.025 0.88 0.95760870 0.95834940 0.97456167 285 39 6574
189 0.025 0.89 0.95978261 0.96143463 0.85247597 263 37 6596
190 0.025 0.90 0.96521739 0.96490551 1.00000000 241 32 6618
191 0.025 0.91 0.96956522 0.96734799 0.76096982 226 28 6633
192 0.025 0.92 0.97282609 0.97159018 0.89290843 196 25 6663
193 0.025 0.93 0.97608696 0.97544672 0.98388659 169 22 6690
194 0.025 0.94 0.97717391 0.97827484 0.90169915 148 21 6711
195 0.025 0.95 0.98478261 0.98213138 0.60729229 125 14 6734
196 0.025 0.96 0.99021739 0.98624502 0.34163041 98 9 6761
197 0.025 0.97 0.99239130 0.99010156 0.56882123 70 7 6789
198 0.025 0.98 0.99456522 0.99305823 0.70777222 49 5 6810
199 0.025 0.99 0.99782609 0.99652912 0.67898675 25 2 6834
200 0.025 1.00 1.00000000 1.00000000 0.00000000 0 0 6859
201 0.050 0.01 0.04581246 0.04602134 1.00000000 6088 1333 294
202 0.050 0.02 0.06227631 0.06029053 0.77777856 6000 1310 382
203 0.050 0.03 0.06871868 0.06877491 1.00000000 5943 1301 439
204 0.050 0.04 0.08231926 0.08073017 0.85205836 5869 1282 513
205 0.050 0.05 0.09234073 0.09114282 0.90413875 5802 1268 580
206 0.050 0.06 0.10522548 0.10309808 0.81023578 5727 1250 655
207 0.050 0.07 0.11596278 0.11261088 0.69592474 5668 1235 714
208 0.050 0.08 0.13171081 0.12366628 0.33529126 5604 1213 778
209 0.050 0.09 0.14745884 0.13793547 0.27272794 5515 1191 867
210 0.050 0.10 0.16392269 0.14924798 0.09732703 5450 1168 932
211 0.050 0.11 0.17465999 0.15953207 0.09601724 5385 1153 997
212 0.050 0.12 0.19112384 0.17380126 0.06469081 5297 1130 1085
213 0.050 0.13 0.19613457 0.18524232 0.26316783 5215 1123 1167
214 0.050 0.14 0.21188261 0.19822599 0.16865859 5136 1101 1246
215 0.050 0.15 0.22691482 0.21262373 0.15996501 5045 1080 1337
216 0.050 0.16 0.23622047 0.22355058 0.22267235 4973 1067 1409
217 0.050 0.17 0.25411596 0.24051935 0.20118405 4866 1042 1516
218 0.050 0.18 0.27129563 0.25388867 0.10600020 4786 1018 1596
219 0.050 0.19 0.28489621 0.26828641 0.13012344 4693 999 1689
220 0.050 0.20 0.29849678 0.28486952 0.22507125 4583 980 1799
221 0.050 0.21 0.30708661 0.29605348 0.33457088 4508 968 1874
222 0.050 0.22 0.32498210 0.31302224 0.30187815 4401 943 1981
223 0.050 0.23 0.33858268 0.32536316 0.25729324 4324 924 2058
224 0.050 0.24 0.36148890 0.34156061 0.08858673 4230 892 2152
225 0.050 0.25 0.37652112 0.35878648 0.13492415 4117 871 2265
226 0.050 0.26 0.39155333 0.37652655 0.21155020 4000 850 2382
227 0.050 0.27 0.40300644 0.39400951 0.46566032 3880 834 2502
228 0.050 0.28 0.42233357 0.41136393 0.37353028 3772 807 2610
229 0.050 0.29 0.43235505 0.42666152 0.65620226 3667 793 2715
230 0.050 0.30 0.44881890 0.44375884 0.69613225 3557 770 2825
231 0.050 0.31 0.46886185 0.45879933 0.42159692 3468 742 2914
232 0.050 0.32 0.47745168 0.47229721 0.69175899 3375 730 3007
233 0.050 0.33 0.49964209 0.49157989 0.52482554 3256 699 3126
234 0.050 0.34 0.51610594 0.50803445 0.52433089 3151 676 3231
235 0.050 0.35 0.52684324 0.52230364 0.72974976 3055 661 3327
236 0.050 0.36 0.54187545 0.53451600 0.56244205 2981 640 3401
237 0.050 0.37 0.56335004 0.54968505 0.26972533 2893 610 3489
238 0.050 0.38 0.57337151 0.56742512 0.64159576 2769 596 3613
239 0.050 0.39 0.59914102 0.58645070 0.30143717 2657 560 3725
240 0.050 0.40 0.61417323 0.60264816 0.34635464 2552 539 3830
241 0.050 0.41 0.62705798 0.61588893 0.35902732 2467 521 3915
242 0.050 0.42 0.64495347 0.63092943 0.24254697 2375 496 4007
243 0.050 0.43 0.65783822 0.64622702 0.33145117 2274 478 4108
244 0.050 0.44 0.67215462 0.65972490 0.29308662 2189 458 4193
245 0.050 0.45 0.68217609 0.67630801 0.62698905 2074 444 4308
246 0.050 0.46 0.69005011 0.68903458 0.95325128 1986 433 4396
247 0.050 0.47 0.69935576 0.70111840 0.89923541 1905 420 4477
248 0.050 0.48 0.71224052 0.71294511 0.97477263 1831 402 4551
249 0.050 0.49 0.72512527 0.72310066 0.87784469 1770 384 4612
250 0.050 0.50 0.73586256 0.73415606 0.89975855 1699 369 4683
251 0.050 0.51 0.74373658 0.74238334 0.92517458 1646 358 4736
252 0.050 0.52 0.75590551 0.75408150 0.88827406 1572 341 4810
253 0.050 0.53 0.76234789 0.76385139 0.91137416 1505 332 4877
254 0.050 0.54 0.76950608 0.77143592 0.87723499 1456 322 4926
255 0.050 0.55 0.77952756 0.78133436 0.88498964 1393 308 4989
256 0.050 0.56 0.78668576 0.78891888 0.84960393 1344 298 5038
257 0.050 0.57 0.79169649 0.79637486 0.65796284 1293 291 5089
258 0.050 0.58 0.79957051 0.80460213 0.62668364 1240 280 5142
259 0.050 0.59 0.80529707 0.81218666 0.49012780 1189 272 5193
260 0.050 0.60 0.82176092 0.81951408 0.83938997 1155 249 5227
261 0.050 0.61 0.82605583 0.82529888 0.96540994 1116 243 5266
262 0.050 0.62 0.83536149 0.83185499 0.72828994 1078 230 5304
263 0.050 0.63 0.84037223 0.83879676 0.89132096 1031 223 5351
264 0.050 0.64 0.84896206 0.84560998 0.73238747 990 211 5392
265 0.050 0.65 0.85468862 0.85229464 0.81281215 946 203 5436
266 0.050 0.66 0.86041518 0.85936496 0.93450663 899 195 5483
267 0.050 0.67 0.86542591 0.86502121 0.99549138 862 188 5520
268 0.050 0.68 0.87115247 0.86913485 0.83907578 838 180 5544
269 0.050 0.69 0.87544739 0.87401980 0.89417585 806 174 5576
270 0.050 0.70 0.88833214 0.88031881 0.33043625 775 156 5607
271 0.050 0.71 0.89119542 0.88520375 0.46583051 741 152 5641
272 0.050 0.72 0.89549034 0.89137421 0.61821131 699 146 5683
273 0.050 0.73 0.89835361 0.89535930 0.72227321 672 142 5710
274 0.050 0.74 0.90264853 0.89973004 0.72499875 644 136 5738
275 0.050 0.75 0.90622763 0.90448644 0.84601580 612 131 5770
276 0.050 0.76 0.91195419 0.90847153 0.65475917 589 123 5793
277 0.050 0.77 0.91982820 0.91399923 0.42067679 557 112 5825
278 0.050 0.78 0.92483894 0.91862707 0.37694955 528 105 5854
279 0.050 0.79 0.92770222 0.92248361 0.45320988 502 101 5880
280 0.050 0.80 0.92841804 0.92646870 0.80134695 472 100 5910
281 0.050 0.81 0.92984968 0.92929682 0.97496417 452 98 5930
282 0.050 0.82 0.93486042 0.93366757 0.88988874 425 91 5957
283 0.050 0.83 0.93915533 0.93906672 1.00000000 389 85 5993
284 0.050 0.84 0.94273443 0.94215195 0.96833631 370 80 6012
285 0.050 0.85 0.94917681 0.94575138 0.57627253 351 71 6031
286 0.050 0.86 0.95490336 0.95037923 0.42855275 323 63 6059
287 0.050 0.87 0.95705082 0.95410721 0.61011800 297 60 6085
288 0.050 0.88 0.96206156 0.95834940 0.48843860 271 53 6111
289 0.050 0.89 0.96492484 0.96143463 0.50205955 251 49 6131
290 0.050 0.90 0.96921976 0.96490551 0.37498108 230 43 6152
291 0.050 0.91 0.97208304 0.96734799 0.30948257 215 39 6167
292 0.050 0.92 0.97637795 0.97159018 0.27121778 188 33 6194
293 0.050 0.93 0.97995705 0.97544672 0.26820649 163 28 6219
294 0.050 0.94 0.98138869 0.97827484 0.43534375 143 26 6239
295 0.050 0.95 0.98711525 0.98213138 0.14959493 121 18 6261
296 0.050 0.96 0.99212598 0.98624502 0.05037509 96 11 6286
297 0.050 0.97 0.99427344 0.99010156 0.11188484 69 8 6313
298 0.050 0.98 0.99570508 0.99305823 0.25528324 48 6 6334
299 0.050 0.99 0.99856836 0.99652912 0.23812048 25 2 6357
300 0.050 1.00 1.00000000 1.00000000 0.00000000 0 0 6382
301 0.075 0.01 0.04745763 0.04602134 0.79209553 5735 1686 274
302 0.075 0.02 0.06384181 0.06029053 0.51093910 5653 1657 356
303 0.075 0.03 0.07175141 0.06877491 0.61035110 5601 1643 408
304 0.075 0.04 0.08361582 0.08073017 0.64737367 5529 1622 480
305 0.075 0.05 0.09265537 0.09114282 0.83789961 5464 1606 545
306 0.075 0.06 0.10621469 0.10309808 0.65549869 5395 1582 614
307 0.075 0.07 0.11751412 0.11261088 0.48411265 5341 1562 668
308 0.075 0.08 0.13276836 0.12366628 0.19968981 5282 1535 727
309 0.075 0.09 0.14971751 0.13793547 0.11041666 5201 1505 808
310 0.075 0.10 0.16384181 0.14924798 0.05453962 5138 1480 871
311 0.075 0.11 0.17231638 0.15953207 0.10219235 5073 1465 936
312 0.075 0.12 0.18757062 0.17380126 0.08843940 4989 1438 1020
313 0.075 0.13 0.19209040 0.18524232 0.41852763 4908 1430 1101
314 0.075 0.14 0.20734463 0.19822599 0.28870210 4834 1403 1175
315 0.075 0.15 0.21920904 0.21262373 0.46089733 4743 1382 1266
316 0.075 0.16 0.23107345 0.22355058 0.40547307 4679 1361 1330
317 0.075 0.17 0.24915254 0.24051935 0.34964870 4579 1329 1430
318 0.075 0.18 0.26440678 0.25388867 0.26027563 4502 1302 1507
319 0.075 0.19 0.27966102 0.26828641 0.23077182 4417 1275 1592
320 0.075 0.20 0.29322034 0.28486952 0.39217024 4312 1251 1697
321 0.075 0.21 0.30451977 0.29605348 0.39082614 4245 1231 1764
322 0.075 0.22 0.32316384 0.31302224 0.30881307 4146 1198 1863
323 0.075 0.23 0.33841808 0.32536316 0.19190162 4077 1171 1932
324 0.075 0.24 0.36214689 0.34156061 0.04042020 3993 1129 2016
325 0.075 0.25 0.37570621 0.35878648 0.09683637 3883 1105 2126
326 0.075 0.26 0.38983051 0.37652655 0.19827783 3770 1080 2239
327 0.075 0.27 0.40395480 0.39400951 0.34384508 3659 1055 2350
328 0.075 0.28 0.42259887 0.41136393 0.28668421 3557 1022 2452
329 0.075 0.29 0.43502825 0.42666152 0.43396854 3460 1000 2549
330 0.075 0.30 0.45141243 0.44375884 0.47758589 3356 971 2653
331 0.075 0.31 0.47118644 0.45879933 0.24490727 3274 936 2735
332 0.075 0.32 0.47909605 0.47229721 0.53209446 3183 922 2826
333 0.075 0.33 0.50282486 0.49157989 0.29387546 3075 880 2934
334 0.075 0.34 0.51864407 0.50803445 0.32275579 2975 852 3034
335 0.075 0.35 0.53050847 0.52230364 0.44772478 2885 831 3124
336 0.075 0.36 0.54632768 0.53451600 0.26855185 2818 803 3191
337 0.075 0.37 0.56723164 0.54968505 0.09670833 2737 766 3272
338 0.075 0.38 0.57740113 0.56742512 0.34897536 2617 748 3392
339 0.075 0.39 0.60112994 0.58645070 0.16170193 2511 706 3498
340 0.075 0.40 0.61751412 0.60264816 0.15370761 2414 677 3595
341 0.075 0.41 0.62768362 0.61588893 0.25721847 2329 659 3680
342 0.075 0.42 0.64293785 0.63092943 0.24475308 2239 632 3770
343 0.075 0.43 0.65649718 0.64622702 0.31735885 2144 608 3865
344 0.075 0.44 0.67175141 0.65972490 0.23542473 2066 581 3943
345 0.075 0.45 0.68361582 0.67630801 0.47229730 1958 560 4051
346 0.075 0.46 0.69322034 0.68903458 0.68647366 1876 543 4133
347 0.075 0.47 0.70338983 0.70111840 0.83524301 1800 525 4209
348 0.075 0.48 0.71694915 0.71294511 0.69373809 1732 501 4277
349 0.075 0.49 0.73107345 0.72310066 0.41069032 1678 476 4331
350 0.075 0.50 0.74067797 0.73415606 0.49900378 1609 459 4400
351 0.075 0.51 0.74745763 0.74238334 0.59993110 1557 447 4452
352 0.075 0.52 0.76214689 0.75408150 0.38696411 1492 421 4517
353 0.075 0.53 0.76949153 0.76385139 0.54594788 1429 408 4580
354 0.075 0.54 0.77683616 0.77143592 0.55961812 1383 395 4626
355 0.075 0.55 0.78644068 0.78133436 0.57640859 1323 378 4686
356 0.075 0.56 0.79322034 0.78891888 0.63733061 1276 366 4733
357 0.075 0.57 0.79943503 0.79637486 0.74126081 1229 355 4780
358 0.075 0.58 0.80847458 0.80460213 0.66472624 1181 339 4828
359 0.075 0.59 0.81468927 0.81218666 0.78554281 1133 328 4876
360 0.075 0.60 0.82881356 0.81951408 0.26173435 1101 303 4908
361 0.075 0.61 0.83446328 0.82529888 0.26284316 1066 293 4943
362 0.075 0.62 0.84237288 0.83185499 0.19018033 1029 279 4980
363 0.075 0.63 0.84915254 0.83879676 0.18975470 987 267 5022
364 0.075 0.64 0.85649718 0.84560998 0.16004497 947 254 5062
365 0.075 0.65 0.86214689 0.85229464 0.19667322 905 244 5104
366 0.075 0.66 0.86779661 0.85936496 0.26182707 860 234 5149
367 0.075 0.67 0.87231638 0.86502121 0.32590572 824 226 5185
368 0.075 0.68 0.87683616 0.86913485 0.29234275 800 218 5209
369 0.075 0.69 0.88248588 0.87401980 0.23778665 772 208 5237
370 0.075 0.70 0.89322034 0.88031881 0.06274743 742 189 5267
371 0.075 0.71 0.89717514 0.88520375 0.07921885 711 182 5298
372 0.075 0.72 0.90056497 0.89137421 0.17056493 669 176 5340
373 0.075 0.73 0.90282486 0.89535930 0.26129594 642 172 5367
374 0.075 0.74 0.90734463 0.89973004 0.24259883 616 164 5393
375 0.075 0.75 0.91129944 0.90448644 0.28752912 586 157 5423
376 0.075 0.76 0.91581921 0.90847153 0.24086087 563 149 5446
377 0.075 0.77 0.92429379 0.91399923 0.08737430 535 134 5474
378 0.075 0.78 0.92881356 0.91862707 0.08291851 507 126 5502
379 0.075 0.79 0.93163842 0.92248361 0.11223827 482 121 5527
380 0.075 0.80 0.93220339 0.92646870 0.31734596 452 120 5557
381 0.075 0.81 0.93389831 0.92929682 0.41992356 433 117 5576
382 0.075 0.82 0.93785311 0.93366757 0.45280558 406 110 5603
383 0.075 0.83 0.94350282 0.93906672 0.40586797 374 100 5635
384 0.075 0.84 0.94689266 0.94215195 0.36065264 356 94 5653
385 0.075 0.85 0.95254237 0.94575138 0.16899097 338 84 5671
386 0.075 0.86 0.95706215 0.95037923 0.15829285 310 76 5699
387 0.075 0.87 0.95932203 0.95410721 0.25918809 285 72 5724
388 0.075 0.88 0.96384181 0.95834940 0.21193497 260 64 5749
389 0.075 0.89 0.96610169 0.96143463 0.27572417 240 60 5769
390 0.075 0.90 0.97005650 0.96490551 0.20535996 220 53 5789
391 0.075 0.91 0.97288136 0.96734799 0.15728169 206 48 5803
392 0.075 0.92 0.97683616 0.97159018 0.15269525 180 41 5829
393 0.075 0.93 0.98079096 0.97544672 0.11743258 157 34 5852
394 0.075 0.94 0.98192090 0.97827484 0.26940831 137 32 5872
395 0.075 0.95 0.98644068 0.98213138 0.14565484 115 24 5894
396 0.075 0.96 0.99096045 0.98624502 0.06847458 91 16 5918
397 0.075 0.97 0.99378531 0.99010156 0.10004857 66 11 5943
398 0.075 0.98 0.99491525 0.99305823 0.36399814 45 9 5964
399 0.075 0.99 0.99830508 0.99652912 0.22414616 24 3 5985
400 0.075 1.00 1.00000000 1.00000000 0.00000000 0 0 6009
401 0.100 0.01 0.04780115 0.04602134 0.69404773 5429 1992 258
402 0.100 0.02 0.06309751 0.06029053 0.56385322 5350 1960 337
403 0.100 0.03 0.07170172 0.06877491 0.56993840 5302 1942 385
404 0.100 0.04 0.08365201 0.08073017 0.59832472 5234 1917 453
405 0.100 0.05 0.09273423 0.09114282 0.80153540 5172 1898 515
406 0.100 0.06 0.10611855 0.10309808 0.62462900 5107 1870 580
407 0.100 0.07 0.11663480 0.11261088 0.52185598 5055 1848 632
408 0.100 0.08 0.13145315 0.12366628 0.22001328 5000 1817 687
409 0.100 0.09 0.14866157 0.13793547 0.10376822 4925 1781 762
410 0.100 0.10 0.16252390 0.14924798 0.05033181 4866 1752 821
411 0.100 0.11 0.17112811 0.15953207 0.09708898 4804 1734 883
412 0.100 0.12 0.18785851 0.17380126 0.05109556 4728 1699 959
413 0.100 0.13 0.19407266 0.18524232 0.23681716 4652 1686 1035
414 0.100 0.14 0.20889101 0.19822599 0.16181739 4582 1655 1105
415 0.100 0.15 0.22179732 0.21262373 0.24276802 4497 1628 1190
416 0.100 0.16 0.23470363 0.22355058 0.16111365 4439 1601 1248
417 0.100 0.17 0.25191205 0.24051935 0.16271351 4343 1565 1344
418 0.100 0.18 0.26529637 0.25388867 0.16984159 4267 1537 1420
419 0.100 0.19 0.28107075 0.26828641 0.12986023 4188 1504 1499
420 0.100 0.20 0.29541109 0.28486952 0.22206993 4089 1474 1598
421 0.100 0.21 0.30544933 0.29605348 0.28327947 4023 1453 1664
422 0.100 0.22 0.32456979 0.31302224 0.19205824 3931 1413 1756
423 0.100 0.23 0.34130019 0.32536316 0.07307415 3870 1378 1817
424 0.100 0.24 0.36424474 0.34156061 0.01134750 3792 1330 1895
425 0.100 0.25 0.37715105 0.35878648 0.04322820 3685 1303 2002
426 0.100 0.26 0.39244742 0.37652655 0.08338422 3579 1271 2108
427 0.100 0.27 0.40917782 0.39400951 0.10217805 3478 1236 2209
428 0.100 0.28 0.42782027 0.41136393 0.07790617 3382 1197 2305
429 0.100 0.29 0.43929254 0.42666152 0.18015419 3287 1173 2400
430 0.100 0.30 0.45506692 0.44375884 0.23333525 3187 1140 2500
431 0.100 0.31 0.47418738 0.45879933 0.10388975 3110 1100 2577
432 0.100 0.32 0.48374761 0.47229721 0.22962750 3025 1080 2662
433 0.100 0.33 0.50621415 0.49157989 0.12348158 2922 1033 2765
434 0.100 0.34 0.51959847 0.50803445 0.22559435 2822 1005 2865
435 0.100 0.35 0.53202677 0.52230364 0.30977736 2737 979 2950
436 0.100 0.36 0.54636711 0.53451600 0.21301597 2672 949 3015
437 0.100 0.37 0.56453155 0.54968505 0.11627889 2592 911 3095
438 0.100 0.38 0.57600382 0.56742512 0.36787312 2478 887 3209
439 0.100 0.39 0.59847036 0.58645070 0.20066824 2377 840 3310
440 0.100 0.40 0.61472275 0.60264816 0.19572995 2285 806 3402
441 0.100 0.41 0.62619503 0.61588893 0.26820810 2206 782 3481
442 0.100 0.42 0.64053537 0.63092943 0.29909594 2119 752 3568
443 0.100 0.43 0.65535373 0.64622702 0.32005652 2031 721 3656
444 0.100 0.44 0.67160612 0.65972490 0.18869885 1960 687 3727
445 0.100 0.45 0.68307839 0.67630801 0.45522222 1855 663 3832
446 0.100 0.46 0.69263862 0.68903458 0.69736569 1776 643 3911
447 0.100 0.47 0.70315488 0.70111840 0.83363022 1704 621 3983
448 0.100 0.48 0.71749522 0.71294511 0.61020852 1642 591 4045
449 0.100 0.49 0.73087954 0.72310066 0.36738932 1591 563 4096
450 0.100 0.50 0.74139579 0.73415606 0.39661108 1527 541 4160
451 0.100 0.51 0.74808795 0.74238334 0.50377799 1477 527 4210
452 0.100 0.52 0.76195029 0.75408150 0.34324055 1415 498 4272
453 0.100 0.53 0.76912046 0.76385139 0.52637824 1354 483 4333
454 0.100 0.54 0.77629063 0.77143592 0.55652485 1310 468 4377
455 0.100 0.55 0.78489484 0.78133436 0.66730060 1251 450 4436
456 0.100 0.56 0.79110899 0.78891888 0.79813324 1205 437 4482
457 0.100 0.57 0.79636711 0.79637486 1.00000000 1158 426 4529
458 0.100 0.58 0.80544933 0.80460213 0.93460514 1113 407 4574
459 0.100 0.59 0.81214149 0.81218666 1.00000000 1068 393 4619
460 0.100 0.60 0.82456979 0.81951408 0.50288069 1037 367 4650
461 0.100 0.61 0.82982792 0.82529888 0.54559312 1003 356 4684
462 0.100 0.62 0.83747610 0.83185499 0.44140894 968 340 4719
463 0.100 0.63 0.84560229 0.83879676 0.33944614 931 323 4756
464 0.100 0.64 0.85277247 0.84560998 0.30535500 893 308 4794
465 0.100 0.65 0.85946463 0.85229464 0.29603789 855 294 4832
466 0.100 0.66 0.86567878 0.85936496 0.34991382 813 281 4874
467 0.100 0.67 0.86998088 0.86502121 0.45989303 778 272 4909
468 0.100 0.68 0.87476099 0.86913485 0.39283718 756 262 4931
469 0.100 0.69 0.88097514 0.87401980 0.27892621 731 249 4956
470 0.100 0.70 0.89005736 0.88031881 0.11745046 701 230 4986
471 0.100 0.71 0.89531549 0.88520375 0.09757324 674 219 5013
472 0.100 0.72 0.89866157 0.89137421 0.22563051 633 212 5054
473 0.100 0.73 0.90105163 0.89535930 0.34056771 607 207 5080
474 0.100 0.74 0.90487572 0.89973004 0.38218960 581 199 5106
475 0.100 0.75 0.90917782 0.90448644 0.41775317 553 190 5134
476 0.100 0.76 0.91347992 0.90847153 0.37628074 531 181 5156
477 0.100 0.77 0.92256214 0.91399923 0.11224190 507 162 5180
478 0.100 0.78 0.92782027 0.91862707 0.07978513 482 151 5205
479 0.100 0.79 0.93068834 0.92248361 0.11104968 458 145 5229
480 0.100 0.80 0.93164436 0.92646870 0.31164729 429 143 5258
481 0.100 0.81 0.93403442 0.92929682 0.34782522 412 138 5275
482 0.100 0.82 0.93881453 0.93366757 0.29143588 388 128 5299
483 0.100 0.83 0.94502868 0.93906672 0.20061137 359 115 5328
484 0.100 0.84 0.94837476 0.94215195 0.17033910 342 108 5345
485 0.100 0.85 0.95411090 0.94575138 0.05513731 326 96 5361
486 0.100 0.86 0.95841300 0.95037923 0.05484508 299 87 5388
487 0.100 0.87 0.96128107 0.95410721 0.07625655 276 81 5411
488 0.100 0.88 0.96510516 0.95834940 0.08100971 251 73 5436
489 0.100 0.89 0.96749522 0.96143463 0.10581920 232 68 5455
490 0.100 0.90 0.97131931 0.96490551 0.07265561 213 60 5474
491 0.100 0.91 0.97418738 0.96734799 0.04695978 200 54 5487
492 0.100 0.92 0.97753346 0.97159018 0.06626192 174 47 5513
493 0.100 0.93 0.98135755 0.97544672 0.04993685 152 39 5535
494 0.100 0.94 0.98231358 0.97827484 0.16324118 132 37 5555
495 0.100 0.95 0.98613767 0.98213138 0.12819855 110 29 5577
496 0.100 0.96 0.99091778 0.98624502 0.04171606 88 19 5599
497 0.100 0.97 0.99378585 0.99010156 0.06264913 64 13 5623
498 0.100 0.98 0.99474187 0.99305823 0.35197885 43 11 5644
499 0.100 0.99 0.99808795 0.99652912 0.22995331 23 4 5664
500 0.100 1.00 1.00000000 1.00000000 0.00000000 0 0 5687
Dboth
1 17
2 25
3 29
4 35
5 39
6 47
7 54
8 62
9 69
10 79
11 83
12 89
13 91
14 96
15 104
16 108
17 117
18 123
19 127
20 134
21 139
22 150
23 160
24 171
25 176
26 183
27 192
28 198
29 202
30 211
31 216
32 220
33 228
34 232
35 236
36 244
37 255
38 259
39 270
40 277
41 283
42 292
43 294
44 300
45 307
46 309
47 315
48 321
49 333
50 336
51 340
52 348
53 351
54 351
55 353
56 357
57 359
58 366
59 368
60 372
61 372
62 379
63 380
64 387
65 391
66 393
67 397
68 399
69 400
70 407
71 408
72 411
73 411
74 413
75 414
76 416
77 416
78 420
79 422
80 423
81 424
82 427
83 430
84 431
85 433
86 436
87 437
88 439
89 440
90 442
91 444
92 444
93 446
94 446
95 450
96 452
97 453
98 453
99 456
100 457
101 36
102 52
103 58
104 70
105 80
106 94
107 106
108 121
109 136
110 152
111 161
112 175
113 180
114 193
115 207
116 213
117 231
118 246
119 259
120 272
121 280
122 294
123 309
124 326
125 339
126 355
127 369
128 383
129 392
130 408
131 426
132 432
133 451
134 462
135 469
136 483
137 502
138 509
139 533
140 549
141 561
142 582
143 592
144 604
145 616
146 621
147 630
148 644
149 659
150 670
151 677
152 689
153 694
154 699
155 708
156 713
157 716
158 725
159 732
160 746
161 748
162 759
163 762
164 772
165 779
166 786
167 790
168 798
169 802
170 814
171 817
172 822
173 825
174 829
175 831
176 836
177 842
178 847
179 849
180 850
181 851
182 856
183 861
184 864
185 870
186 875
187 876
188 881
189 883
190 888
191 892
192 895
193 898
194 899
195 906
196 911
197 913
198 915
199 918
200 920
201 64
202 87
203 96
204 115
205 129
206 147
207 162
208 184
209 206
210 229
211 244
212 267
213 274
214 296
215 317
216 330
217 355
218 379
219 398
220 417
221 429
222 454
223 473
224 505
225 526
226 547
227 563
228 590
229 604
230 627
231 655
232 667
233 698
234 721
235 736
236 757
237 787
238 801
239 837
240 858
241 876
242 901
243 919
244 939
245 953
246 964
247 977
248 995
249 1013
250 1028
251 1039
252 1056
253 1065
254 1075
255 1089
256 1099
257 1106
258 1117
259 1125
260 1148
261 1154
262 1167
263 1174
264 1186
265 1194
266 1202
267 1209
268 1217
269 1223
270 1241
271 1245
272 1251
273 1255
274 1261
275 1266
276 1274
277 1285
278 1292
279 1296
280 1297
281 1299
282 1306
283 1312
284 1317
285 1326
286 1334
287 1337
288 1344
289 1348
290 1354
291 1358
292 1364
293 1369
294 1371
295 1379
296 1386
297 1389
298 1391
299 1395
300 1397
301 84
302 113
303 127
304 148
305 164
306 188
307 208
308 235
309 265
310 290
311 305
312 332
313 340
314 367
315 388
316 409
317 441
318 468
319 495
320 519
321 539
322 572
323 599
324 641
325 665
326 690
327 715
328 748
329 770
330 799
331 834
332 848
333 890
334 918
335 939
336 967
337 1004
338 1022
339 1064
340 1093
341 1111
342 1138
343 1162
344 1189
345 1210
346 1227
347 1245
348 1269
349 1294
350 1311
351 1323
352 1349
353 1362
354 1375
355 1392
356 1404
357 1415
358 1431
359 1442
360 1467
361 1477
362 1491
363 1503
364 1516
365 1526
366 1536
367 1544
368 1552
369 1562
370 1581
371 1588
372 1594
373 1598
374 1606
375 1613
376 1621
377 1636
378 1644
379 1649
380 1650
381 1653
382 1660
383 1670
384 1676
385 1686
386 1694
387 1698
388 1706
389 1710
390 1717
391 1722
392 1729
393 1736
394 1738
395 1746
396 1754
397 1759
398 1761
399 1767
400 1770
401 100
402 132
403 150
404 175
405 194
406 222
407 244
408 275
409 311
410 340
411 358
412 393
413 406
414 437
415 464
416 491
417 527
418 555
419 588
420 618
421 639
422 679
423 714
424 762
425 789
426 821
427 856
428 895
429 919
430 952
431 992
432 1012
433 1059
434 1087
435 1113
436 1143
437 1181
438 1205
439 1252
440 1286
441 1310
442 1340
443 1371
444 1405
445 1429
446 1449
447 1471
448 1501
449 1529
450 1551
451 1565
452 1594
453 1609
454 1624
455 1642
456 1655
457 1666
458 1685
459 1699
460 1725
461 1736
462 1752
463 1769
464 1784
465 1798
466 1811
467 1820
468 1830
469 1843
470 1862
471 1873
472 1880
473 1885
474 1893
475 1902
476 1911
477 1930
478 1941
479 1947
480 1949
481 1954
482 1964
483 1977
484 1984
485 1996
486 2005
487 2011
488 2019
489 2024
490 2032
491 2038
492 2045
493 2053
494 2055
495 2063
496 2073
497 2079
498 2081
499 2088
500 2092#enrichment.plotter(h_US, "USFDR", "adj.P.Val", "FDR for Closest Upstream Hi-C Contact Overlapping Gene, Human") #These two are ugly, and can't be run anyway until next chunk is complete to create their DFs. It's OK without them.
#enrichment.plotter(c_US, "USFDR", "adj.P.Val", "FDR for Closest Upstream Hi-C Contact Overlapping Gene, Chimp")
enrichment.plotter(gene.hic.filt, "max_B_FDR.H", "adj.P.Val", "FDR for Hi-C Contact Overlapping Gene w/ Strongest Effect Size, Human")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.04255319 0.04482226 0.896341593 6966 450 328
2 0.010 0.02 0.05531915 0.05873261 0.823132279 6864 444 430
3 0.010 0.03 0.06595745 0.06774858 0.948388489 6799 439 495
4 0.010 0.04 0.08297872 0.07792375 0.739109091 6728 431 566
5 0.010 0.05 0.09361702 0.08861412 0.756539910 6650 426 644
6 0.010 0.06 0.10638298 0.09994848 0.688779537 6568 420 726
7 0.010 0.07 0.12127660 0.10935085 0.436282525 6502 413 792
8 0.010 0.08 0.14042553 0.12017002 0.186783684 6427 404 867
9 0.010 0.09 0.15319149 0.13227718 0.190016651 6339 398 955
10 0.010 0.10 0.16595745 0.14013395 0.110625351 6284 392 1010
11 0.010 0.11 0.17446809 0.15108192 0.163272439 6203 388 1091
12 0.010 0.12 0.19148936 0.16280268 0.094223648 6120 380 1174
13 0.010 0.13 0.20212766 0.17349304 0.103414230 6042 375 1252
14 0.010 0.14 0.21489362 0.18495621 0.096241734 5959 369 1335
15 0.010 0.15 0.22978723 0.19706337 0.075037993 5872 362 1422
16 0.010 0.16 0.24468085 0.20852653 0.053363753 5790 355 1504
17 0.010 0.17 0.26170213 0.22230809 0.039216814 5691 347 1603
18 0.010 0.18 0.27659574 0.23402885 0.028340342 5607 340 1687
19 0.010 0.19 0.28510638 0.24729521 0.056768894 5508 336 1786
20 0.010 0.20 0.31063830 0.26313756 0.018335106 5397 324 1897
21 0.010 0.21 0.31702128 0.27228233 0.028193632 5329 321 1965
22 0.010 0.22 0.33829787 0.28851108 0.016157245 5213 311 2081
23 0.010 0.23 0.35744681 0.30203503 0.008107401 5117 302 2177
24 0.010 0.24 0.37021277 0.31736218 0.012824793 5004 296 2290
25 0.010 0.25 0.37659574 0.33191654 0.038297090 4894 293 2400
26 0.010 0.26 0.39787234 0.35097888 0.031733005 4756 283 2538
27 0.010 0.27 0.41489362 0.36810922 0.033975104 4631 275 2663
28 0.010 0.28 0.42340426 0.38446677 0.081618865 4508 271 2786
29 0.010 0.29 0.42978723 0.39966512 0.184546153 4393 268 2901
30 0.010 0.30 0.45106383 0.41486347 0.110691080 4285 258 3009
31 0.010 0.31 0.46382979 0.42967543 0.134878441 4176 252 3118
32 0.010 0.32 0.47234043 0.44165379 0.182116831 4087 248 3207
33 0.010 0.33 0.48723404 0.46097372 0.258227010 3944 241 3350
34 0.010 0.34 0.49787234 0.47578568 0.346426915 3834 236 3460
35 0.010 0.35 0.50638298 0.48969603 0.484530376 3730 232 3564
36 0.010 0.36 0.51914894 0.50167439 0.462873952 3643 226 3651
37 0.010 0.37 0.54042553 0.52073673 0.404343606 3505 216 3789
38 0.010 0.38 0.55319149 0.53941267 0.568291880 3366 210 3928
39 0.010 0.39 0.57021277 0.55692942 0.582177184 3238 202 4056
40 0.010 0.40 0.58510638 0.57264297 0.606260207 3123 195 4171
41 0.010 0.41 0.60212766 0.58629572 0.502409757 3025 187 4269
42 0.010 0.42 0.62340426 0.60226687 0.358950831 2911 177 4383
43 0.010 0.43 0.62765957 0.61720762 0.665736782 2797 175 4497
44 0.010 0.44 0.64468085 0.63008758 0.530781211 2705 167 4589
45 0.010 0.45 0.66595745 0.64799073 0.428591225 2576 157 4718
46 0.010 0.46 0.67021277 0.66164348 0.722737688 2472 155 4822
47 0.010 0.47 0.68723404 0.67555384 0.612010811 2372 147 4922
48 0.010 0.48 0.69787234 0.68650180 0.619242304 2292 142 5002
49 0.010 0.49 0.71914894 0.69577537 0.278096782 2230 132 5064
50 0.010 0.50 0.73191489 0.70852653 0.271860903 2137 126 5157
51 0.010 0.51 0.75319149 0.71818650 0.091513096 2072 116 5222
52 0.010 0.52 0.77021277 0.73042246 0.050933914 1985 108 5309
53 0.010 0.53 0.77659574 0.74227202 0.088970215 1896 105 5398
54 0.010 0.54 0.78085106 0.75128800 0.140305286 1828 103 5466
55 0.010 0.55 0.78723404 0.76339516 0.230675497 1737 100 5557
56 0.010 0.56 0.80425532 0.77241113 0.100582484 1675 92 5619
57 0.010 0.57 0.80638298 0.77988150 0.169681007 1618 91 5676
58 0.010 0.58 0.82127660 0.78967027 0.093688267 1549 84 5745
59 0.010 0.59 0.82553191 0.79778465 0.137295513 1488 82 5806
60 0.010 0.60 0.83191489 0.80654302 0.168689782 1423 79 5871
61 0.010 0.61 0.83191489 0.81465739 0.351266577 1360 79 5934
62 0.010 0.62 0.84468085 0.82264297 0.219380183 1304 73 5990
63 0.010 0.63 0.84893617 0.82934055 0.270557576 1254 71 6040
64 0.010 0.64 0.85957447 0.83719732 0.196612013 1198 66 6096
65 0.010 0.65 0.86170213 0.84621329 0.371114856 1129 65 6165
66 0.010 0.66 0.86808511 0.85394127 0.407445078 1072 62 6222
67 0.010 0.67 0.87446809 0.86038125 0.400665983 1025 59 6269
68 0.010 0.68 0.88085106 0.86450283 0.317857745 996 56 6298
69 0.010 0.69 0.88510638 0.87107161 0.386641950 947 54 6347
70 0.010 0.70 0.89361702 0.87815559 0.324875631 896 50 6398
71 0.010 0.71 0.90000000 0.88330757 0.276238359 859 47 6435
72 0.010 0.72 0.90638298 0.89116435 0.309346013 801 44 6493
73 0.010 0.73 0.90638298 0.89528594 0.463598044 769 44 6525
74 0.010 0.74 0.90638298 0.90043792 0.715380192 729 44 6565
75 0.010 0.75 0.91063830 0.90443071 0.695553513 700 42 6594
76 0.010 0.76 0.91702128 0.90803709 0.539843942 675 39 6619
77 0.010 0.77 0.91914894 0.91447707 0.772910246 626 38 6668
78 0.010 0.78 0.93617021 0.92014426 0.216984862 590 30 6704
79 0.010 0.79 0.94042553 0.92387944 0.191612567 563 28 6731
80 0.010 0.80 0.94042553 0.92787223 0.320526913 532 28 6762
81 0.010 0.81 0.94468085 0.93134982 0.277869297 507 26 6787
82 0.010 0.82 0.95106383 0.93572901 0.193050135 476 23 6818
83 0.010 0.83 0.95319149 0.94062339 0.276237487 439 22 6855
84 0.010 0.84 0.95744681 0.94474498 0.254571528 409 20 6885
85 0.010 0.85 0.95957447 0.94757857 0.272594404 388 19 6906
86 0.010 0.86 0.96382979 0.95105616 0.224761047 363 17 6931
87 0.010 0.87 0.96595745 0.95492014 0.282294973 334 16 6960
88 0.010 0.88 0.96808511 0.95878413 0.354022806 305 15 6989
89 0.010 0.89 0.97021277 0.96251932 0.434974134 277 14 7017
90 0.010 0.90 0.97234043 0.96548171 0.477705507 255 13 7039
91 0.010 0.91 0.97234043 0.96805770 0.682222700 235 13 7059
92 0.010 0.92 0.97234043 0.97217929 1.000000000 203 13 7091
93 0.010 0.93 0.97659574 0.97591448 1.000000000 176 11 7118
94 0.010 0.94 0.97872340 0.97887687 1.000000000 154 10 7140
95 0.010 0.95 0.98723404 0.98183926 0.468178083 135 6 7159
96 0.010 0.96 0.99361702 0.98596084 0.210103389 106 3 7188
97 0.010 0.97 0.99574468 0.98969603 0.269553541 78 2 7216
98 0.010 0.98 0.99574468 0.99291602 0.637876460 53 2 7241
99 0.010 0.99 0.99787234 0.99639361 0.876970946 27 1 7267
100 0.010 1.00 1.00000000 1.00000000 0.000000000 0 0 7294
101 0.025 0.01 0.03900325 0.04482226 0.409099524 6529 887 312
102 0.025 0.02 0.05308776 0.05873261 0.482388519 6434 874 407
103 0.025 0.03 0.06067172 0.06774858 0.399993514 6371 867 470
104 0.025 0.04 0.07583965 0.07792375 0.852261448 6306 853 535
105 0.025 0.05 0.08667389 0.08861412 0.873451994 6233 843 608
106 0.025 0.06 0.10075840 0.09994848 0.976910867 6158 830 683
107 0.025 0.07 0.11592633 0.10935085 0.531472350 6099 816 742
108 0.025 0.08 0.13326111 0.12017002 0.211616544 6031 800 810
109 0.025 0.09 0.14517876 0.13227718 0.237694465 5948 789 893
110 0.025 0.10 0.15492958 0.14013395 0.183842024 5896 780 945
111 0.025 0.11 0.16251354 0.15108192 0.325033015 5818 773 1023
112 0.025 0.12 0.17551463 0.16280268 0.286001027 5739 761 1102
113 0.025 0.13 0.18526544 0.17349304 0.337105911 5665 752 1176
114 0.025 0.14 0.19609967 0.18495621 0.376823241 5586 742 1255
115 0.025 0.15 0.20910076 0.19706337 0.349607245 5504 730 1337
116 0.025 0.16 0.22318527 0.20852653 0.260725688 5428 717 1413
117 0.025 0.17 0.23943662 0.22230809 0.196663204 5336 702 1505
118 0.025 0.18 0.25568797 0.23402885 0.106462902 5260 687 1581
119 0.025 0.19 0.26652221 0.24729521 0.160996761 5167 677 1674
120 0.025 0.20 0.28494041 0.26313756 0.118114569 5061 660 1780
121 0.025 0.21 0.29144095 0.27228233 0.175854126 4996 654 1845
122 0.025 0.22 0.30660888 0.28851108 0.209790687 4884 640 1957
123 0.025 0.23 0.31852654 0.30203503 0.260873330 4790 629 2051
124 0.025 0.24 0.33152763 0.31736218 0.343464171 4683 617 2158
125 0.025 0.25 0.34127844 0.33191654 0.544367096 4579 608 2262
126 0.025 0.26 0.35752979 0.35097888 0.683638483 4446 593 2395
127 0.025 0.27 0.37594800 0.36810922 0.624352281 4330 576 2511
128 0.025 0.28 0.38569881 0.38446677 0.963367147 4212 567 2629
129 0.025 0.29 0.39219935 0.39966512 0.647305202 4100 561 2741
130 0.025 0.30 0.41386782 0.41486347 0.976211057 4002 541 2839
131 0.025 0.31 0.43011918 0.42967543 1.000000000 3902 526 2939
132 0.025 0.32 0.43661972 0.44165379 0.769678017 3815 520 3026
133 0.025 0.33 0.45612134 0.46097372 0.779562828 3683 502 3158
134 0.025 0.34 0.47128927 0.47578568 0.797724799 3582 488 3259
135 0.025 0.35 0.48320693 0.48969603 0.700190424 3485 477 3356
136 0.025 0.36 0.49404117 0.50167439 0.646201946 3402 467 3439
137 0.025 0.37 0.51354280 0.52073673 0.666484675 3272 449 3569
138 0.025 0.38 0.53087757 0.53941267 0.603735039 3143 433 3698
139 0.025 0.39 0.54712893 0.55692942 0.546339017 3022 418 3819
140 0.025 0.40 0.55796316 0.57264297 0.354970893 2910 408 3931
141 0.025 0.41 0.56988082 0.58629572 0.296881201 2815 397 4026
142 0.025 0.42 0.59046587 0.60226687 0.456532878 2710 378 4131
143 0.025 0.43 0.60238353 0.61720762 0.341596994 2605 367 4236
144 0.025 0.44 0.61755146 0.63008758 0.421336332 2519 353 4322
145 0.025 0.45 0.63705309 0.64799073 0.481116816 2398 335 4443
146 0.025 0.46 0.64355363 0.66164348 0.229995117 2298 329 4543
147 0.025 0.47 0.65872156 0.67555384 0.260077391 2204 315 4637
148 0.025 0.48 0.66955580 0.68650180 0.252430287 2129 305 4712
149 0.025 0.49 0.68147346 0.69577537 0.333042506 2068 294 4773
150 0.025 0.50 0.69664139 0.70852653 0.419155444 1983 280 4858
151 0.025 0.51 0.71397616 0.71818650 0.791834371 1924 264 4917
152 0.025 0.52 0.72914410 0.73042246 0.957150034 1843 250 4998
153 0.025 0.53 0.73781148 0.74227202 0.771826574 1759 242 5082
154 0.025 0.54 0.74431203 0.75128800 0.629976481 1695 236 5146
155 0.025 0.55 0.75406284 0.76339516 0.503210422 1610 227 5231
156 0.025 0.56 0.76489707 0.77241113 0.590422712 1550 217 5291
157 0.025 0.57 0.76923077 0.77988150 0.429715417 1496 213 5345
158 0.025 0.58 0.78006501 0.78967027 0.471650832 1430 203 5411
159 0.025 0.59 0.78656555 0.79778465 0.389570171 1373 197 5468
160 0.025 0.60 0.80173348 0.80654302 0.726570337 1319 183 5522
161 0.025 0.61 0.80390033 0.81465739 0.394842672 1258 181 5583
162 0.025 0.62 0.81690141 0.82264297 0.659502593 1208 169 5633
163 0.025 0.63 0.82123510 0.82934055 0.515232924 1160 165 5681
164 0.025 0.64 0.82990249 0.83719732 0.553830766 1107 157 5734
165 0.025 0.65 0.83531961 0.84621329 0.353009029 1042 152 5799
166 0.025 0.66 0.84398700 0.85394127 0.388352838 990 144 5851
167 0.025 0.67 0.84832069 0.86038125 0.282079055 944 140 5897
168 0.025 0.68 0.85482124 0.86450283 0.387410213 918 134 5923
169 0.025 0.69 0.86457205 0.87107161 0.565018524 876 125 5965
170 0.025 0.70 0.87757313 0.87815559 0.996783158 833 113 6008
171 0.025 0.71 0.88190683 0.88330757 0.930989466 797 109 6044
172 0.025 0.72 0.89165764 0.89116435 1.000000000 745 100 6096
173 0.025 0.73 0.89599133 0.89528594 0.986195216 717 96 6124
174 0.025 0.74 0.90032503 0.90043792 1.000000000 681 92 6160
175 0.025 0.75 0.90465872 0.90443071 1.000000000 654 88 6187
176 0.025 0.76 0.91007584 0.90803709 0.866842335 631 83 6210
177 0.025 0.77 0.91440953 0.91447707 1.000000000 585 79 6256
178 0.025 0.78 0.92849404 0.92014426 0.351191485 554 66 6287
179 0.025 0.79 0.93066089 0.92387944 0.446336191 527 64 6314
180 0.025 0.80 0.93066089 0.92787223 0.778623659 496 64 6345
181 0.025 0.81 0.93391116 0.93134982 0.796013081 472 61 6369
182 0.025 0.82 0.93716143 0.93572901 0.906420740 441 58 6400
183 0.025 0.83 0.94149512 0.94062339 0.963950075 407 54 6434
184 0.025 0.84 0.94691224 0.94474498 0.817880036 380 49 6461
185 0.025 0.85 0.95232936 0.94757857 0.541043219 363 44 6478
186 0.025 0.86 0.95774648 0.95105616 0.356331723 341 39 6500
187 0.025 0.87 0.95882990 0.95492014 0.599307648 312 38 6529
188 0.025 0.88 0.96316360 0.95878413 0.532076641 286 34 6555
189 0.025 0.89 0.96749729 0.96251932 0.449679957 261 30 6580
190 0.025 0.90 0.96966414 0.96548171 0.518624628 240 28 6601
191 0.025 0.91 0.97183099 0.96805770 0.551983594 222 26 6619
192 0.025 0.92 0.97399783 0.97217929 0.801596533 192 24 6649
193 0.025 0.93 0.97616468 0.97591448 1.000000000 165 22 6676
194 0.025 0.94 0.97833153 0.97887687 0.999348421 144 20 6697
195 0.025 0.95 0.98483207 0.98183926 0.552446875 127 14 6714
196 0.025 0.96 0.99241603 0.98596084 0.103785697 102 7 6739
197 0.025 0.97 0.99458288 0.98969603 0.163733245 75 5 6766
198 0.025 0.98 0.99674973 0.99291602 0.203934252 52 3 6789
199 0.025 0.99 0.99783315 0.99639361 0.627847272 26 2 6815
200 0.025 1.00 1.00000000 1.00000000 0.000000000 0 0 6841
201 0.050 0.01 0.04425410 0.04482226 0.966327190 6077 1339 286
202 0.050 0.02 0.05781585 0.05873261 0.921572702 5988 1320 375
203 0.050 0.03 0.06495360 0.06774858 0.688338729 5928 1310 435
204 0.050 0.04 0.07851535 0.07792375 0.971120818 5868 1291 495
205 0.050 0.05 0.08993576 0.08861412 0.888374972 5801 1275 562
206 0.050 0.06 0.10206995 0.09994848 0.807812574 5730 1258 633
207 0.050 0.07 0.11277659 0.10935085 0.684319152 5672 1243 691
208 0.050 0.08 0.12705211 0.12017002 0.406701595 5608 1223 755
209 0.050 0.09 0.13990007 0.13227718 0.375221760 5532 1205 831
210 0.050 0.10 0.15060671 0.14013395 0.228246173 5486 1190 877
211 0.050 0.11 0.15845824 0.15108192 0.417715951 5412 1179 951
212 0.050 0.12 0.16987866 0.16280268 0.451760629 5337 1163 1026
213 0.050 0.13 0.17773019 0.17349304 0.671806719 5265 1152 1098
214 0.050 0.14 0.19057816 0.18495621 0.575020636 5194 1134 1169
215 0.050 0.15 0.20342612 0.19706337 0.532459233 5118 1116 1245
216 0.050 0.16 0.21841542 0.20852653 0.331997299 5050 1095 1313
217 0.050 0.17 0.23483226 0.22230809 0.226324013 4966 1072 1397
218 0.050 0.18 0.25053533 0.23402885 0.114778942 4897 1050 1466
219 0.050 0.19 0.26195575 0.24729521 0.170453614 4810 1034 1553
220 0.050 0.20 0.27980014 0.26313756 0.125760321 4712 1009 1651
221 0.050 0.21 0.28551035 0.27228233 0.231884339 4649 1001 1714
222 0.050 0.22 0.30549607 0.28851108 0.129158364 4551 973 1812
223 0.050 0.23 0.31977159 0.30203503 0.117572273 4466 953 1897
224 0.050 0.24 0.33618844 0.31736218 0.100874705 4370 930 1993
225 0.050 0.25 0.34903640 0.33191654 0.141071775 4275 912 2088
226 0.050 0.26 0.36259814 0.35097888 0.329241204 4146 893 2217
227 0.050 0.27 0.37758744 0.36810922 0.434243345 4034 872 2329
228 0.050 0.28 0.39614561 0.38446677 0.335913883 3933 846 2430
229 0.050 0.29 0.40613847 0.39966512 0.605657996 3829 832 2534
230 0.050 0.30 0.42326909 0.41486347 0.499405726 3735 808 2628
231 0.050 0.31 0.44182727 0.42967543 0.324557714 3646 782 2717
232 0.050 0.32 0.44825125 0.44165379 0.603347795 3562 773 2801
233 0.050 0.33 0.47037830 0.46097372 0.452979889 3443 742 2920
234 0.050 0.34 0.48608137 0.47578568 0.410610278 3350 720 3013
235 0.050 0.35 0.49607423 0.48969603 0.618472018 3256 706 3107
236 0.050 0.36 0.50606709 0.50167439 0.738583879 3177 692 3186
237 0.050 0.37 0.52391149 0.52073673 0.815594794 3054 667 3309
238 0.050 0.38 0.54104211 0.53941267 0.915933371 2933 643 3430
239 0.050 0.39 0.56031406 0.55692942 0.801034511 2824 616 3539
240 0.050 0.40 0.57316203 0.57264297 0.989185781 2720 598 3643
241 0.050 0.41 0.58529622 0.58629572 0.956976033 2631 581 3732
242 0.050 0.42 0.60599572 0.60226687 0.775755918 2536 552 3827
243 0.050 0.43 0.61955746 0.61720762 0.865385126 2439 533 3924
244 0.050 0.44 0.63740186 0.63008758 0.551284609 2364 508 3999
245 0.050 0.45 0.65453248 0.64799073 0.592355307 2249 484 4114
246 0.050 0.46 0.66381156 0.66164348 0.874244741 2156 471 4207
247 0.050 0.47 0.67523198 0.67555384 1.000000000 2064 455 4299
248 0.050 0.48 0.68593862 0.68650180 0.985330771 1994 440 4369
249 0.050 0.49 0.69450393 0.69577537 0.934496785 1934 428 4429
250 0.050 0.50 0.70735189 0.70852653 0.940691747 1853 410 4510
251 0.050 0.51 0.72376874 0.71818650 0.631066064 1801 387 4562
252 0.050 0.52 0.73947181 0.73042246 0.417983692 1728 365 4635
253 0.050 0.53 0.74803712 0.74227202 0.609185687 1648 353 4715
254 0.050 0.54 0.75588865 0.75128800 0.684807998 1589 342 4774
255 0.050 0.55 0.76659529 0.76339516 0.782084178 1510 327 4853
256 0.050 0.56 0.77658815 0.77241113 0.706387409 1454 313 4909
257 0.050 0.57 0.78229836 0.77988150 0.837131026 1404 305 4959
258 0.050 0.58 0.79086367 0.78967027 0.932368868 1340 293 5023
259 0.050 0.59 0.79800143 0.79778465 1.000000000 1287 283 5076
260 0.050 0.60 0.81227695 0.80654302 0.573558797 1239 263 5124
261 0.050 0.61 0.81655960 0.81465739 0.869394767 1182 257 5181
262 0.050 0.62 0.82655246 0.82264297 0.700573912 1134 243 5229
263 0.050 0.63 0.83297645 0.82934055 0.718574470 1091 234 5272
264 0.050 0.64 0.83940043 0.83719732 0.836196197 1039 225 5324
265 0.050 0.65 0.84511064 0.84621329 0.931884283 977 217 5386
266 0.050 0.66 0.85153462 0.85394127 0.810353751 926 208 5437
267 0.050 0.67 0.85581727 0.86038125 0.615756024 882 202 5481
268 0.050 0.68 0.86009993 0.86450283 0.624999984 856 196 5507
269 0.050 0.69 0.86866524 0.87107161 0.800378927 817 184 5546
270 0.050 0.70 0.88079943 0.87815559 0.772529750 779 167 5584
271 0.050 0.71 0.88436831 0.88330757 0.927776670 744 162 5619
272 0.050 0.72 0.89436117 0.89116435 0.706152102 697 148 5666
273 0.050 0.73 0.89793005 0.89528594 0.757430693 670 143 5693
274 0.050 0.74 0.90364026 0.90043792 0.694375973 638 135 5725
275 0.050 0.75 0.90935046 0.90443071 0.521077483 615 127 5748
276 0.050 0.76 0.91577445 0.90803709 0.290976530 596 118 5767
277 0.050 0.77 0.92005710 0.91447707 0.439990622 552 112 5811
278 0.050 0.78 0.93076374 0.92014426 0.117504554 523 97 5840
279 0.050 0.79 0.93290507 0.92387944 0.176523384 497 94 5866
280 0.050 0.80 0.93290507 0.92787223 0.454871776 466 94 5897
281 0.050 0.81 0.93647395 0.93134982 0.435680516 444 89 5919
282 0.050 0.82 0.93932905 0.93572901 0.584526705 414 85 5949
283 0.050 0.83 0.94218415 0.94062339 0.833184745 380 81 5983
284 0.050 0.84 0.94718059 0.94474498 0.706790747 355 74 6008
285 0.050 0.85 0.95074946 0.94757857 0.601650421 338 69 6025
286 0.050 0.86 0.95574590 0.95105616 0.406350283 318 62 6045
287 0.050 0.87 0.95931478 0.95492014 0.421034795 293 57 6070
288 0.050 0.88 0.96359743 0.95878413 0.353988942 269 51 6094
289 0.050 0.89 0.96859386 0.96251932 0.213267798 247 44 6116
290 0.050 0.90 0.97144897 0.96548171 0.203853699 228 40 6135
291 0.050 0.91 0.97359029 0.96805770 0.223629915 211 37 6152
292 0.050 0.92 0.97715917 0.97217929 0.245136702 184 32 6179
293 0.050 0.93 0.97930050 0.97591448 0.413989201 158 29 6205
294 0.050 0.94 0.98072805 0.97887687 0.667443456 137 27 6226
295 0.050 0.95 0.98501071 0.98183926 0.383495345 120 21 6243
296 0.050 0.96 0.99072091 0.98596084 0.121771567 96 13 6267
297 0.050 0.97 0.99428979 0.98969603 0.082794877 72 8 6291
298 0.050 0.98 0.99643112 0.99291602 0.119480340 50 5 6313
299 0.050 0.99 0.99785867 0.99639361 0.444664563 25 3 6338
300 0.050 1.00 1.00000000 1.00000000 0.000000000 0 0 6363
301 0.075 0.01 0.04643262 0.04482226 0.759082704 5732 1684 266
302 0.075 0.02 0.05832390 0.05873261 0.979625428 5645 1663 353
303 0.075 0.03 0.06681767 0.06774858 0.901916804 5590 1648 408
304 0.075 0.04 0.07927520 0.07792375 0.848874834 5533 1626 465
305 0.075 0.05 0.09173273 0.08861412 0.633329594 5472 1604 526
306 0.075 0.06 0.10362401 0.09994848 0.588659280 5405 1583 593
307 0.075 0.07 0.11494904 0.10935085 0.415479190 5352 1563 646
308 0.075 0.08 0.12910532 0.12017002 0.203294077 5293 1538 705
309 0.075 0.09 0.14326161 0.13227718 0.130988656 5224 1513 774
310 0.075 0.10 0.15288788 0.14013395 0.085854981 5180 1496 818
311 0.075 0.11 0.16081540 0.15108192 0.207069320 5109 1482 889
312 0.075 0.12 0.17044168 0.16280268 0.340777499 5035 1465 963
313 0.075 0.13 0.17780294 0.17349304 0.611154546 4965 1452 1033
314 0.075 0.14 0.18969422 0.18495621 0.583287639 4897 1431 1101
315 0.075 0.15 0.19988675 0.19706337 0.760115330 4821 1413 1177
316 0.075 0.16 0.21460929 0.20852653 0.494889534 4758 1387 1240
317 0.075 0.17 0.23159683 0.22230809 0.300416862 4681 1357 1317
318 0.075 0.18 0.24631937 0.23402885 0.175116189 4616 1331 1382
319 0.075 0.19 0.25934315 0.24729521 0.192313271 4536 1308 1462
320 0.075 0.20 0.27633069 0.26313756 0.160983965 4443 1278 1555
321 0.075 0.21 0.28425821 0.27228233 0.209146431 4386 1264 1612
322 0.075 0.22 0.30407701 0.28851108 0.106794262 4295 1229 1703
323 0.075 0.23 0.31879955 0.30203503 0.086114388 4216 1203 1782
324 0.075 0.24 0.33578709 0.31736218 0.062384350 4127 1173 1871
325 0.075 0.25 0.34824462 0.33191654 0.103295114 4036 1151 1962
326 0.075 0.26 0.36353341 0.35097888 0.218957831 3915 1124 2083
327 0.075 0.27 0.38052095 0.36810922 0.229222144 3812 1094 2186
328 0.075 0.28 0.39920725 0.38446677 0.155341091 3718 1061 2280
329 0.075 0.29 0.41223103 0.39966512 0.230562734 3623 1038 2375
330 0.075 0.30 0.42808607 0.41486347 0.209241180 3533 1010 2465
331 0.075 0.31 0.44507361 0.42967543 0.144326426 3448 980 2550
332 0.075 0.32 0.45243488 0.44165379 0.312132046 3368 967 2630
333 0.075 0.33 0.47451869 0.46097372 0.203364419 3257 928 2741
334 0.075 0.34 0.49150623 0.47578568 0.139430368 3172 898 2826
335 0.075 0.35 0.50226501 0.48969603 0.239968357 3083 879 2915
336 0.075 0.36 0.51302378 0.50167439 0.289963702 3009 860 2989
337 0.075 0.37 0.53114383 0.52073673 0.332583360 2893 828 3105
338 0.075 0.38 0.54869762 0.53941267 0.387877476 2779 797 3219
339 0.075 0.39 0.56625142 0.55692942 0.384307703 2674 766 3324
340 0.075 0.40 0.57927520 0.57264297 0.539456670 2575 743 3423
341 0.075 0.41 0.59003398 0.58629572 0.737304561 2488 724 3510
342 0.075 0.42 0.60872027 0.60226687 0.546664212 2397 691 3601
343 0.075 0.43 0.62287656 0.61720762 0.596270570 2306 666 3692
344 0.075 0.44 0.64269536 0.63008758 0.222252304 2241 631 3757
345 0.075 0.45 0.66138165 0.64799073 0.189449349 2135 598 3863
346 0.075 0.46 0.67157418 0.66164348 0.329617127 2047 580 3951
347 0.075 0.47 0.68403171 0.67555384 0.402653638 1961 558 4037
348 0.075 0.48 0.69762174 0.68650180 0.264054799 1900 534 4098
349 0.075 0.49 0.70724802 0.69577537 0.244900266 1845 517 4153
350 0.075 0.50 0.71970555 0.70852653 0.251647286 1768 495 4230
351 0.075 0.51 0.73499434 0.71818650 0.079057550 1720 468 4278
352 0.075 0.52 0.74971687 0.73042246 0.040519576 1651 442 4347
353 0.075 0.53 0.75877690 0.74227202 0.076187157 1575 426 4423
354 0.075 0.54 0.76727067 0.75128800 0.082478568 1520 411 4478
355 0.075 0.55 0.77802945 0.76339516 0.106421118 1445 392 4553
356 0.075 0.56 0.78652322 0.77241113 0.114801366 1390 377 4608
357 0.075 0.57 0.79161948 0.77988150 0.186218616 1341 368 4657
358 0.075 0.58 0.80181200 0.78967027 0.164159232 1283 350 4715
359 0.075 0.59 0.80804077 0.79778465 0.235162975 1231 339 4767
360 0.075 0.60 0.82106455 0.80654302 0.084813165 1186 316 4812
361 0.075 0.61 0.82616082 0.81465739 0.167405685 1132 307 4866
362 0.075 0.62 0.83465459 0.82264297 0.142084802 1085 292 4913
363 0.075 0.63 0.84314836 0.82934055 0.085647115 1048 277 4950
364 0.075 0.64 0.84994337 0.83719732 0.106521981 999 265 4999
365 0.075 0.65 0.85617214 0.84621329 0.199707064 940 254 5058
366 0.075 0.66 0.86183465 0.85394127 0.302876995 890 244 5108
367 0.075 0.67 0.86636467 0.86038125 0.431663679 848 236 5150
368 0.075 0.68 0.87032843 0.86450283 0.438774080 823 229 5175
369 0.075 0.69 0.87768969 0.87107161 0.366096011 785 216 5213
370 0.075 0.70 0.88901472 0.87815559 0.122140180 750 196 5248
371 0.075 0.71 0.89241223 0.88330757 0.188941703 716 190 5282
372 0.075 0.72 0.90033975 0.89116435 0.172202954 669 176 5329
373 0.075 0.73 0.90317101 0.89528594 0.235200002 642 171 5356
374 0.075 0.74 0.90883352 0.90043792 0.195171337 612 161 5386
375 0.075 0.75 0.91392978 0.90443071 0.133939295 590 152 5408
376 0.075 0.76 0.91902605 0.90803709 0.076507529 571 143 5427
377 0.075 0.77 0.92412231 0.91447707 0.109467041 530 134 5468
378 0.075 0.78 0.93318233 0.92014426 0.024465346 502 118 5496
379 0.075 0.79 0.93544734 0.92387944 0.041895903 477 114 5521
380 0.075 0.80 0.93544734 0.92787223 0.177762971 446 114 5552
381 0.075 0.81 0.93884485 0.93134982 0.172673292 425 108 5573
382 0.075 0.82 0.94167610 0.93572901 0.269478002 396 103 5602
383 0.075 0.83 0.94620612 0.94062339 0.283645102 366 95 5632
384 0.075 0.84 0.95073613 0.94474498 0.232291726 342 87 5656
385 0.075 0.85 0.95356738 0.94757857 0.220952636 325 82 5673
386 0.075 0.86 0.95753114 0.95105616 0.170014377 305 75 5693
387 0.075 0.87 0.96149490 0.95492014 0.147099926 282 68 5716
388 0.075 0.88 0.96489241 0.95878413 0.161202921 258 62 5740
389 0.075 0.89 0.96885617 0.96251932 0.127540049 236 55 5762
390 0.075 0.90 0.97168743 0.96548171 0.120868891 218 50 5780
391 0.075 0.91 0.97395243 0.96805770 0.127067718 202 46 5796
392 0.075 0.92 0.97734994 0.97217929 0.155342609 176 40 5822
393 0.075 0.93 0.97961495 0.97591448 0.286553750 151 36 5847
394 0.075 0.94 0.98074745 0.97887687 0.597617318 130 34 5868
395 0.075 0.95 0.98471121 0.98183926 0.353958287 114 27 5884
396 0.075 0.96 0.98980747 0.98596084 0.147576736 91 18 5907
397 0.075 0.97 0.99377123 0.98969603 0.072591625 69 11 5929
398 0.075 0.98 0.99546999 0.99291602 0.195467480 47 8 5951
399 0.075 0.99 0.99773499 0.99639361 0.398634760 24 4 5974
400 0.075 1.00 1.00000000 1.00000000 0.000000000 0 0 5998
401 0.100 0.01 0.04654511 0.04482226 0.702079771 5429 1987 251
402 0.100 0.02 0.05854127 0.05873261 1.000000000 5346 1962 334
403 0.100 0.03 0.06765835 0.06774858 1.000000000 5295 1943 385
404 0.100 0.04 0.08013436 0.07792375 0.694771422 5242 1917 438
405 0.100 0.05 0.09261036 0.08861412 0.480518339 5185 1891 495
406 0.100 0.06 0.10508637 0.09994848 0.383432678 5123 1865 557
407 0.100 0.07 0.11516315 0.10935085 0.340589585 5071 1844 609
408 0.100 0.08 0.12859885 0.12017002 0.178901527 5015 1816 665
409 0.100 0.09 0.14251440 0.13227718 0.115267785 4950 1787 730
410 0.100 0.10 0.15211132 0.14013395 0.071122232 4909 1767 771
411 0.100 0.11 0.15978887 0.15108192 0.207001123 4840 1751 840
412 0.100 0.12 0.17034549 0.16280268 0.291075486 4771 1729 909
413 0.100 0.13 0.17898273 0.17349304 0.459340643 4706 1711 974
414 0.100 0.14 0.18953935 0.18495621 0.550480190 4639 1689 1041
415 0.100 0.15 0.20009597 0.19706337 0.707876643 4567 1667 1113
416 0.100 0.16 0.21353167 0.20852653 0.531289716 4506 1639 1174
417 0.100 0.17 0.23032630 0.22230809 0.318068453 4434 1604 1246
418 0.100 0.18 0.24328215 0.23402885 0.255862777 4370 1577 1310
419 0.100 0.19 0.25575816 0.24729521 0.309033178 4293 1551 1387
420 0.100 0.20 0.27255278 0.26313756 0.266084868 4205 1516 1475
421 0.100 0.21 0.28023033 0.27228233 0.355375331 4150 1500 1530
422 0.100 0.22 0.29942418 0.28851108 0.208637450 4064 1460 1616
423 0.100 0.23 0.31525912 0.30203503 0.131213010 3992 1427 1688
424 0.100 0.24 0.33061420 0.31736218 0.135678322 3905 1395 1775
425 0.100 0.25 0.34309021 0.33191654 0.215255190 3818 1369 1862
426 0.100 0.26 0.35892514 0.35097888 0.388809117 3703 1336 1977
427 0.100 0.27 0.37763916 0.36810922 0.303915120 3609 1297 2071
428 0.100 0.28 0.39491363 0.38446677 0.262780129 3518 1261 2162
429 0.100 0.29 0.40834933 0.39966512 0.357520132 3428 1233 2252
430 0.100 0.30 0.42418426 0.41486347 0.325262132 3343 1200 2337
431 0.100 0.31 0.44193858 0.42967543 0.194870261 3265 1163 2415
432 0.100 0.32 0.45105566 0.44165379 0.324761680 3191 1144 2489
433 0.100 0.33 0.47120921 0.46097372 0.284510666 3083 1102 2597
434 0.100 0.34 0.48752399 0.47578568 0.219132771 3002 1068 2678
435 0.100 0.35 0.49904031 0.48969603 0.331025595 2918 1044 2762
436 0.100 0.36 0.50959693 0.50167439 0.412167797 2847 1022 2833
437 0.100 0.37 0.52639155 0.52073673 0.562919245 2734 987 2946
438 0.100 0.38 0.54318618 0.53941267 0.705156056 2624 952 3056
439 0.100 0.39 0.56142035 0.55692942 0.647855346 2526 914 3154
440 0.100 0.40 0.57485605 0.57264297 0.831418090 2432 886 3248
441 0.100 0.41 0.58685221 0.58629572 0.972633119 2351 861 3329
442 0.100 0.42 0.60508637 0.60226687 0.778477457 2265 823 3415
443 0.100 0.43 0.62092131 0.61720762 0.702881101 2182 790 3498
444 0.100 0.44 0.64011516 0.63008758 0.279231513 2122 750 3558
445 0.100 0.45 0.65786948 0.64799073 0.281408567 2020 713 3660
446 0.100 0.46 0.66794626 0.66164348 0.494035859 1935 692 3745
447 0.100 0.47 0.68138196 0.67555384 0.524078627 1855 664 3825
448 0.100 0.48 0.69433781 0.68650180 0.382164833 1797 637 3883
449 0.100 0.49 0.70441459 0.69577537 0.329867909 1746 616 3934
450 0.100 0.50 0.71737044 0.70852653 0.312252197 1674 589 4006
451 0.100 0.51 0.73128599 0.71818650 0.127106462 1628 560 4052
452 0.100 0.52 0.74520154 0.73042246 0.080334368 1562 531 4118
453 0.100 0.53 0.75431862 0.74227202 0.149661019 1489 512 4191
454 0.100 0.54 0.76295585 0.75128800 0.158238288 1437 494 4243
455 0.100 0.55 0.77303263 0.76339516 0.237931555 1364 473 4316
456 0.100 0.56 0.78119002 0.77241113 0.277043993 1311 456 4369
457 0.100 0.57 0.78598848 0.77988150 0.449781403 1263 446 4417
458 0.100 0.58 0.79606526 0.78967027 0.420198248 1208 425 4472
459 0.100 0.59 0.80230326 0.79778465 0.569652697 1158 412 4522
460 0.100 0.60 0.81477927 0.80654302 0.279944690 1116 386 4564
461 0.100 0.61 0.82053743 0.81465739 0.438520676 1065 374 4615
462 0.100 0.62 0.82965451 0.82264297 0.344049727 1022 355 4658
463 0.100 0.63 0.83877159 0.82934055 0.192257282 989 336 4691
464 0.100 0.64 0.84548944 0.83719732 0.244386764 942 322 4738
465 0.100 0.65 0.85316699 0.84621329 0.320559060 888 306 4792
466 0.100 0.66 0.85940499 0.85394127 0.429847361 841 293 4839
467 0.100 0.67 0.86324376 0.86038125 0.686315674 799 285 4881
468 0.100 0.68 0.86804223 0.86450283 0.606879359 777 275 4903
469 0.100 0.69 0.87523992 0.87107161 0.531545376 741 260 4939
470 0.100 0.70 0.88483685 0.87815559 0.293255968 706 240 4974
471 0.100 0.71 0.89011516 0.88330757 0.274912020 677 229 5003
472 0.100 0.72 0.89731286 0.89116435 0.311252627 631 214 5049
473 0.100 0.73 0.90019194 0.89528594 0.416007974 605 208 5075
474 0.100 0.74 0.90547025 0.90043792 0.392953026 576 197 5104
475 0.100 0.75 0.91074856 0.90443071 0.269860743 556 186 5124
476 0.100 0.76 0.91554702 0.90803709 0.179353930 538 176 5142
477 0.100 0.77 0.92130518 0.91447707 0.208627561 500 164 5180
478 0.100 0.78 0.92994242 0.92014426 0.059839129 474 146 5206
479 0.100 0.79 0.93234165 0.92387944 0.097960607 450 141 5230
480 0.100 0.80 0.93282150 0.92787223 0.331255218 420 140 5260
481 0.100 0.81 0.93714012 0.93134982 0.241376171 402 131 5278
482 0.100 0.82 0.94049904 0.93572901 0.324170028 375 124 5305
483 0.100 0.83 0.94481766 0.94062339 0.371829668 346 115 5334
484 0.100 0.84 0.95009597 0.94474498 0.232497635 325 104 5355
485 0.100 0.85 0.95393474 0.94757857 0.143010207 311 96 5369
486 0.100 0.86 0.95777351 0.95105616 0.109069382 292 88 5388
487 0.100 0.87 0.96161228 0.95492014 0.096957890 270 80 5410
488 0.100 0.88 0.96497121 0.95878413 0.110323844 247 73 5433
489 0.100 0.89 0.96880998 0.96251932 0.089080574 226 65 5454
490 0.100 0.90 0.97168906 0.96548171 0.081046156 209 59 5471
491 0.100 0.91 0.97408829 0.96805770 0.078820935 194 54 5486
492 0.100 0.92 0.97696737 0.97217929 0.139935350 168 48 5512
493 0.100 0.93 0.97936660 0.97591448 0.263462954 144 43 5536
494 0.100 0.94 0.98032630 0.97887687 0.653479756 123 41 5557
495 0.100 0.95 0.98368522 0.98183926 0.520919610 107 34 5573
496 0.100 0.96 0.98992322 0.98596084 0.091281221 88 21 5592
497 0.100 0.97 0.99424184 0.98969603 0.022860247 68 12 5612
498 0.100 0.98 0.99568138 0.99291602 0.108019375 46 9 5634
499 0.100 0.99 0.99808061 0.99639361 0.197599131 24 4 5656
500 0.100 1.00 1.00000000 1.00000000 0.000000000 0 0 5680
Dboth
1 20
2 26
3 31
4 39
5 44
6 50
7 57
8 66
9 72
10 78
11 82
12 90
13 95
14 101
15 108
16 115
17 123
18 130
19 134
20 146
21 149
22 159
23 168
24 174
25 177
26 187
27 195
28 199
29 202
30 212
31 218
32 222
33 229
34 234
35 238
36 244
37 254
38 260
39 268
40 275
41 283
42 293
43 295
44 303
45 313
46 315
47 323
48 328
49 338
50 344
51 354
52 362
53 365
54 367
55 370
56 378
57 379
58 386
59 388
60 391
61 391
62 397
63 399
64 404
65 405
66 408
67 411
68 414
69 416
70 420
71 423
72 426
73 426
74 426
75 428
76 431
77 432
78 440
79 442
80 442
81 444
82 447
83 448
84 450
85 451
86 453
87 454
88 455
89 456
90 457
91 457
92 457
93 459
94 460
95 464
96 467
97 468
98 468
99 469
100 470
101 36
102 49
103 56
104 70
105 80
106 93
107 107
108 123
109 134
110 143
111 150
112 162
113 171
114 181
115 193
116 206
117 221
118 236
119 246
120 263
121 269
122 283
123 294
124 306
125 315
126 330
127 347
128 356
129 362
130 382
131 397
132 403
133 421
134 435
135 446
136 456
137 474
138 490
139 505
140 515
141 526
142 545
143 556
144 570
145 588
146 594
147 608
148 618
149 629
150 643
151 659
152 673
153 681
154 687
155 696
156 706
157 710
158 720
159 726
160 740
161 742
162 754
163 758
164 766
165 771
166 779
167 783
168 789
169 798
170 810
171 814
172 823
173 827
174 831
175 835
176 840
177 844
178 857
179 859
180 859
181 862
182 865
183 869
184 874
185 879
186 884
187 885
188 889
189 893
190 895
191 897
192 899
193 901
194 903
195 909
196 916
197 918
198 920
199 921
200 923
201 62
202 81
203 91
204 110
205 126
206 143
207 158
208 178
209 196
210 211
211 222
212 238
213 249
214 267
215 285
216 306
217 329
218 351
219 367
220 392
221 400
222 428
223 448
224 471
225 489
226 508
227 529
228 555
229 569
230 593
231 619
232 628
233 659
234 681
235 695
236 709
237 734
238 758
239 785
240 803
241 820
242 849
243 868
244 893
245 917
246 930
247 946
248 961
249 973
250 991
251 1014
252 1036
253 1048
254 1059
255 1074
256 1088
257 1096
258 1108
259 1118
260 1138
261 1144
262 1158
263 1167
264 1176
265 1184
266 1193
267 1199
268 1205
269 1217
270 1234
271 1239
272 1253
273 1258
274 1266
275 1274
276 1283
277 1289
278 1304
279 1307
280 1307
281 1312
282 1316
283 1320
284 1327
285 1332
286 1339
287 1344
288 1350
289 1357
290 1361
291 1364
292 1369
293 1372
294 1374
295 1380
296 1388
297 1393
298 1396
299 1398
300 1401
301 82
302 103
303 118
304 140
305 162
306 183
307 203
308 228
309 253
310 270
311 284
312 301
313 314
314 335
315 353
316 379
317 409
318 435
319 458
320 488
321 502
322 537
323 563
324 593
325 615
326 642
327 672
328 705
329 728
330 756
331 786
332 799
333 838
334 868
335 887
336 906
337 938
338 969
339 1000
340 1023
341 1042
342 1075
343 1100
344 1135
345 1168
346 1186
347 1208
348 1232
349 1249
350 1271
351 1298
352 1324
353 1340
354 1355
355 1374
356 1389
357 1398
358 1416
359 1427
360 1450
361 1459
362 1474
363 1489
364 1501
365 1512
366 1522
367 1530
368 1537
369 1550
370 1570
371 1576
372 1590
373 1595
374 1605
375 1614
376 1623
377 1632
378 1648
379 1652
380 1652
381 1658
382 1663
383 1671
384 1679
385 1684
386 1691
387 1698
388 1704
389 1711
390 1716
391 1720
392 1726
393 1730
394 1732
395 1739
396 1748
397 1755
398 1758
399 1762
400 1766
401 97
402 122
403 141
404 167
405 193
406 219
407 240
408 268
409 297
410 317
411 333
412 355
413 373
414 395
415 417
416 445
417 480
418 507
419 533
420 568
421 584
422 624
423 657
424 689
425 715
426 748
427 787
428 823
429 851
430 884
431 921
432 940
433 982
434 1016
435 1040
436 1062
437 1097
438 1132
439 1170
440 1198
441 1223
442 1261
443 1294
444 1334
445 1371
446 1392
447 1420
448 1447
449 1468
450 1495
451 1524
452 1553
453 1572
454 1590
455 1611
456 1628
457 1638
458 1659
459 1672
460 1698
461 1710
462 1729
463 1748
464 1762
465 1778
466 1791
467 1799
468 1809
469 1824
470 1844
471 1855
472 1870
473 1876
474 1887
475 1898
476 1908
477 1920
478 1938
479 1943
480 1944
481 1953
482 1960
483 1969
484 1980
485 1988
486 1996
487 2004
488 2011
489 2019
490 2025
491 2030
492 2036
493 2041
494 2043
495 2050
496 2063
497 2072
498 2075
499 2080
500 2084enrichment.plotter(gene.hic.filt, "max_B_FDR.C", "adj.P.Val", "FDR for Hi-C Contact Overlapping Gene w/ Strongest Effect Size, Chimp")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.03719912 0.04422162 0.52516767 6995 440 327
2 0.010 0.02 0.05470460 0.05913356 0.75539549 6887 432 435
3 0.010 0.03 0.06345733 0.06710374 0.82215353 6829 428 493
4 0.010 0.04 0.07439825 0.07828770 0.81863830 6747 423 575
5 0.010 0.05 0.08533917 0.08792904 0.90734845 6677 418 645
6 0.010 0.06 0.10065646 0.09949865 0.99625785 6594 411 728
7 0.010 0.07 0.11159737 0.10824013 0.87247993 6531 406 791
8 0.010 0.08 0.12910284 0.11813858 0.50043561 6462 398 860
9 0.010 0.09 0.14004376 0.13022239 0.56773142 6373 393 949
10 0.010 0.10 0.15754923 0.13909243 0.26890554 6312 385 1010
11 0.010 0.11 0.16630197 0.14899087 0.31560692 6239 381 1083
12 0.010 0.12 0.18161926 0.16068903 0.23398121 6155 374 1167
13 0.010 0.13 0.18818381 0.17058748 0.33369699 6081 371 1241
14 0.010 0.14 0.19912473 0.18151433 0.34507532 6001 366 1321
15 0.010 0.15 0.21225383 0.19449801 0.35364437 5906 360 1416
16 0.010 0.16 0.22319475 0.20439645 0.33335477 5834 355 1488
17 0.010 0.17 0.24070022 0.21943695 0.28290000 5725 347 1597
18 0.010 0.18 0.25382932 0.23113511 0.25888750 5640 341 1682
19 0.010 0.19 0.26039387 0.24424733 0.44012473 5541 338 1781
20 0.010 0.20 0.27133479 0.26005913 0.60905084 5423 333 1899
21 0.010 0.21 0.28008753 0.26905772 0.62153662 5357 329 1965
22 0.010 0.22 0.30634573 0.28474097 0.31661032 5247 317 2075
23 0.010 0.23 0.32603939 0.29708189 0.17909888 5160 308 2162
24 0.010 0.24 0.34573304 0.31263659 0.12822041 5048 299 2274
25 0.010 0.25 0.35448578 0.32754853 0.22499999 4936 295 2386
26 0.010 0.26 0.37199125 0.34657411 0.26006844 4796 287 2526
27 0.010 0.27 0.38730853 0.36264301 0.27999131 4678 280 2644
28 0.010 0.28 0.39824945 0.37871192 0.40213675 4558 275 2764
29 0.010 0.29 0.40481400 0.39375241 0.65305224 4444 272 2878
30 0.010 0.30 0.42450766 0.41046407 0.56188102 4323 263 2999
31 0.010 0.31 0.43107221 0.42434760 0.80179689 4218 260 3104
32 0.010 0.32 0.43982495 0.43733128 0.95042867 4121 256 3201
33 0.010 0.33 0.45733042 0.45532845 0.96795761 3989 248 3333
34 0.010 0.34 0.47045952 0.47062604 1.00000000 3876 242 3446
35 0.010 0.35 0.47702407 0.48412392 0.79116154 3774 239 3548
36 0.010 0.36 0.49452954 0.49697905 0.95236822 3682 231 3640
37 0.010 0.37 0.52078775 0.51394781 0.80002605 3562 219 3760
38 0.010 0.38 0.53172867 0.53194498 1.00000000 3427 214 3895
39 0.010 0.39 0.55579869 0.55084201 0.86413893 3291 203 4031
40 0.010 0.40 0.56892779 0.56588250 0.93087511 3180 197 4142
41 0.010 0.41 0.58643326 0.58015169 0.81684628 3077 189 4245
42 0.010 0.42 0.60393873 0.59634915 0.77050182 2959 181 4363
43 0.010 0.43 0.60831510 0.61164674 0.91942245 2842 179 4480
44 0.010 0.44 0.61925602 0.62630158 0.78633928 2733 174 4589
45 0.010 0.45 0.63894967 0.64378455 0.86334013 2606 165 4716
46 0.010 0.46 0.64770241 0.65702532 0.70248906 2507 161 4815
47 0.010 0.47 0.66301969 0.67026610 0.77306952 2411 154 4911
48 0.010 0.48 0.67614880 0.68222137 0.81374252 2324 148 4998
49 0.010 0.49 0.70021882 0.69070575 0.68815641 2269 137 5053
50 0.010 0.50 0.70897155 0.70407507 0.85436421 2169 133 5153
51 0.010 0.51 0.72647702 0.71358786 0.56536448 2103 125 5219
52 0.010 0.52 0.74398249 0.72695719 0.43074140 2007 117 5315
53 0.010 0.53 0.74835886 0.73736984 0.62028012 1928 115 5394
54 0.010 0.54 0.75273523 0.74701118 0.81446068 1855 113 5467
55 0.010 0.55 0.76367615 0.75870935 0.84191857 1769 108 5553
56 0.010 0.56 0.77680525 0.76809359 0.69084945 1702 102 5620
57 0.010 0.57 0.77899344 0.77657797 0.94427335 1637 101 5685
58 0.010 0.58 0.79431072 0.78621931 0.70685701 1569 94 5753
59 0.010 0.59 0.79649891 0.79431804 0.95275707 1507 93 5815
60 0.010 0.60 0.80525164 0.80331662 0.96281915 1441 89 5881
61 0.010 0.61 0.80525164 0.81090114 0.79769382 1382 89 5940
62 0.010 0.62 0.82056893 0.81822856 0.94323365 1332 82 5990
63 0.010 0.63 0.82275711 0.82568454 0.91520092 1275 81 6047
64 0.010 0.64 0.83588621 0.83404037 0.96448722 1216 75 6106
65 0.010 0.65 0.84682713 0.84188199 0.81608930 1160 70 6162
66 0.010 0.66 0.85120350 0.84985217 0.98733983 1100 68 6222
67 0.010 0.67 0.85995624 0.85576552 0.84600803 1058 64 6264
68 0.010 0.68 0.86652079 0.86013626 0.73680041 1027 61 6295
69 0.010 0.69 0.86870897 0.86592107 0.91277866 983 60 6339
70 0.010 0.70 0.88183807 0.87273428 0.59640912 936 54 6386
71 0.010 0.71 0.88402626 0.87787633 0.73367825 897 53 6425
72 0.010 0.72 0.89277899 0.88546086 0.66672881 842 49 6480
73 0.010 0.73 0.89277899 0.88996015 0.90334035 807 49 6515
74 0.010 0.74 0.89715536 0.89535930 0.95969778 767 47 6555
75 0.010 0.75 0.90153173 0.90037280 0.99619395 730 45 6592
76 0.010 0.76 0.90590810 0.90461499 0.98808913 699 43 6623
77 0.010 0.77 0.90590810 0.91027124 0.80100717 655 43 6667
78 0.010 0.78 0.91903720 0.91567039 0.85698408 619 37 6703
79 0.010 0.79 0.92341357 0.92016969 0.86125535 586 35 6736
80 0.010 0.80 0.92560175 0.92428333 0.98509224 555 34 6767
81 0.010 0.81 0.92778993 0.92736856 1.00000000 532 33 6790
82 0.010 0.82 0.93435449 0.93238205 0.93856019 496 30 6826
83 0.010 0.83 0.94091904 0.93778121 0.85210646 457 27 6865
84 0.010 0.84 0.94310722 0.94099499 0.92414924 433 26 6889
85 0.010 0.85 0.94748359 0.94433732 0.84364699 409 24 6913
86 0.010 0.86 0.95404814 0.94935082 0.71729648 373 21 6949
87 0.010 0.87 0.95623632 0.95269315 0.79934346 348 20 6974
88 0.010 0.88 0.96061269 0.95732099 0.81066927 314 18 7008
89 0.010 0.89 0.96280088 0.96117753 0.95186042 285 17 7037
90 0.010 0.90 0.96717724 0.96451986 0.85228231 261 15 7061
91 0.010 0.91 0.97155361 0.96696233 0.66636957 244 13 7078
92 0.010 0.92 0.97155361 0.97120453 1.00000000 211 13 7111
93 0.010 0.93 0.97592998 0.97531816 1.00000000 181 11 7141
94 0.010 0.94 0.97592998 0.97814629 0.86569485 159 11 7163
95 0.010 0.95 0.98468271 0.98213138 0.80848152 132 7 7190
96 0.010 0.96 0.98905908 0.98624502 0.74488621 102 5 7220
97 0.010 0.97 0.99124726 0.99010156 0.99083340 73 4 7249
98 0.010 0.98 0.99124726 0.99305823 0.84911228 50 4 7272
99 0.010 0.99 0.99781182 0.99652912 0.94366508 26 1 7296
100 0.010 1.00 1.00000000 1.00000000 0.00000000 0 0 7322
101 0.025 0.01 0.03804348 0.04422162 0.37598698 6550 885 309
102 0.025 0.02 0.05543478 0.05913356 0.66566779 6450 869 409
103 0.025 0.03 0.06195652 0.06710374 0.55227442 6394 863 465
104 0.025 0.04 0.07391304 0.07828770 0.64501779 6318 852 541
105 0.025 0.05 0.08478261 0.08792904 0.76654233 6253 842 606
106 0.025 0.06 0.09891304 0.09949865 0.99637265 6176 829 683
107 0.025 0.07 0.10978261 0.10824013 0.91727608 6118 819 741
108 0.025 0.08 0.12500000 0.11813858 0.52720807 6055 805 804
109 0.025 0.09 0.13695652 0.13022239 0.55239478 5972 794 887
110 0.025 0.10 0.15000000 0.13909243 0.33332115 5915 782 944
111 0.025 0.11 0.15869565 0.14899087 0.40593624 5846 774 1013
112 0.025 0.12 0.17173913 0.16068903 0.35541788 5767 762 1092
113 0.025 0.13 0.17826087 0.17058748 0.54035193 5696 756 1163
114 0.025 0.14 0.19130435 0.18151433 0.43840107 5623 744 1236
115 0.025 0.15 0.20543478 0.19449801 0.39633793 5535 731 1324
116 0.025 0.16 0.21521739 0.20439645 0.41037180 5467 722 1392
117 0.025 0.17 0.23260870 0.21943695 0.32431857 5366 706 1493
118 0.025 0.18 0.24456522 0.23113511 0.32343130 5286 695 1573
119 0.025 0.19 0.25869565 0.24424733 0.29583349 5197 682 1662
120 0.025 0.20 0.27173913 0.26005913 0.41218766 5086 670 1773
121 0.025 0.21 0.27826087 0.26905772 0.52819897 5022 664 1837
122 0.025 0.22 0.29456522 0.28474097 0.50650958 4915 649 1944
123 0.025 0.23 0.30978261 0.29708189 0.39014748 4833 635 2026
124 0.025 0.24 0.32500000 0.31263659 0.41015493 4726 621 2133
125 0.025 0.25 0.33695652 0.32754853 0.54178526 4621 610 2238
126 0.025 0.26 0.35108696 0.34657411 0.78759659 4486 597 2373
127 0.025 0.27 0.36630435 0.36264301 0.83407057 4375 583 2484
128 0.025 0.28 0.38043478 0.37871192 0.93739994 4263 570 2596
129 0.025 0.29 0.38804348 0.39375241 0.73272333 4153 563 2706
130 0.025 0.30 0.40652174 0.41046407 0.82339152 4040 546 2819
131 0.025 0.31 0.42173913 0.42434760 0.89264398 3946 532 2913
132 0.025 0.32 0.42934783 0.43733128 0.62805380 3852 525 3007
133 0.025 0.33 0.44673913 0.45532845 0.60175691 3728 509 3131
134 0.025 0.34 0.45978261 0.47062604 0.50505095 3621 497 3238
135 0.025 0.35 0.46630435 0.48412392 0.26414070 3522 491 3337
136 0.025 0.36 0.47934783 0.49697905 0.26961638 3434 479 3425
137 0.025 0.37 0.50000000 0.51394781 0.38632432 3321 460 3538
138 0.025 0.38 0.50978261 0.53194498 0.16165910 3190 451 3669
139 0.025 0.39 0.53369565 0.55084201 0.28094849 3065 429 3794
140 0.025 0.40 0.54673913 0.56588250 0.22544134 2960 417 3899
141 0.025 0.41 0.56086957 0.58015169 0.22003278 2862 404 3997
142 0.025 0.42 0.58260870 0.59634915 0.38492740 2756 384 4103
143 0.025 0.43 0.59347826 0.61164674 0.24275478 2647 374 4212
144 0.025 0.44 0.60760870 0.62630158 0.22558413 2546 361 4313
145 0.025 0.45 0.62717391 0.64378455 0.27846607 2428 343 4431
146 0.025 0.46 0.63478261 0.65702532 0.13979664 2332 336 4527
147 0.025 0.47 0.64673913 0.67026610 0.11429114 2240 325 4619
148 0.025 0.48 0.65978261 0.68222137 0.12876858 2159 313 4700
149 0.025 0.49 0.67500000 0.69070575 0.28930978 2107 299 4752
150 0.025 0.50 0.68804348 0.70407507 0.27306418 2015 287 4844
151 0.025 0.51 0.70434783 0.71358786 0.53435319 1956 272 4903
152 0.025 0.52 0.71956522 0.72695719 0.61951619 1866 258 4993
153 0.025 0.53 0.72608696 0.73736984 0.43052210 1791 252 5068
154 0.025 0.54 0.73695652 0.74701118 0.47974213 1726 242 5133
155 0.025 0.55 0.75108696 0.75870935 0.59304999 1648 229 5211
156 0.025 0.56 0.75869565 0.76809359 0.49797549 1582 222 5277
157 0.025 0.57 0.76086957 0.77657797 0.23959190 1518 220 5341
158 0.025 0.58 0.77391304 0.78621931 0.35403759 1455 208 5404
159 0.025 0.59 0.77934783 0.79431804 0.24894447 1397 203 5462
160 0.025 0.60 0.79565217 0.80331662 0.56280615 1342 188 5517
161 0.025 0.61 0.79782609 0.81090114 0.30126723 1285 186 5574
162 0.025 0.62 0.81195652 0.81822856 0.63136113 1241 173 5618
163 0.025 0.63 0.81413043 0.82568454 0.34851147 1185 171 5674
164 0.025 0.64 0.82391304 0.83404037 0.40535811 1129 162 5730
165 0.025 0.65 0.83369565 0.84188199 0.49862715 1077 153 5782
166 0.025 0.66 0.84239130 0.84985217 0.53163472 1023 145 5836
167 0.025 0.67 0.84673913 0.85576552 0.43542902 981 141 5878
168 0.025 0.68 0.85434783 0.86013626 0.62522216 954 134 5905
169 0.025 0.69 0.85978261 0.86592107 0.59583361 914 129 5945
170 0.025 0.70 0.87391304 0.87273428 0.95090243 874 116 5985
171 0.025 0.71 0.87608696 0.87787633 0.90217738 836 114 6023
172 0.025 0.72 0.88369565 0.88546086 0.90137951 784 107 6075
173 0.025 0.73 0.88804348 0.88996015 0.88728431 753 103 6106
174 0.025 0.74 0.89347826 0.89535930 0.88774906 716 98 6143
175 0.025 0.75 0.89782609 0.90037280 0.82894780 681 94 6178
176 0.025 0.76 0.90326087 0.90461499 0.92896910 653 89 6206
177 0.025 0.77 0.90869565 0.91027124 0.90713390 614 84 6245
178 0.025 0.78 0.91956522 0.91567039 0.69685498 582 74 6277
179 0.025 0.79 0.92173913 0.92016969 0.90268074 549 72 6310
180 0.025 0.80 0.92282609 0.92428333 0.91116180 518 71 6341
181 0.025 0.81 0.92391304 0.92736856 0.71702494 495 70 6364
182 0.025 0.82 0.92934783 0.93238205 0.74864551 461 65 6398
183 0.025 0.83 0.93478261 0.93778121 0.74267600 424 60 6435
184 0.025 0.84 0.93804348 0.94099499 0.74132231 402 57 6457
185 0.025 0.85 0.94456522 0.94433732 1.00000000 382 51 6477
186 0.025 0.86 0.95000000 0.94935082 0.98757645 348 46 6511
187 0.025 0.87 0.95108696 0.95269315 0.87154416 323 45 6536
188 0.025 0.88 0.95760870 0.95732099 1.00000000 293 39 6566
189 0.025 0.89 0.95978261 0.96117753 0.88678291 265 37 6594
190 0.025 0.90 0.96413043 0.96451986 1.00000000 243 33 6616
191 0.025 0.91 0.96847826 0.96696233 0.86049447 228 29 6631
192 0.025 0.92 0.97282609 0.97120453 0.83504298 199 25 6660
193 0.025 0.93 0.97608696 0.97531816 0.96258615 170 22 6689
194 0.025 0.94 0.97717391 0.97814629 0.92450686 149 21 6710
195 0.025 0.95 0.98478261 0.98213138 0.60729229 125 14 6734
196 0.025 0.96 0.99021739 0.98624502 0.34163041 98 9 6761
197 0.025 0.97 0.99239130 0.99010156 0.56882123 70 7 6789
198 0.025 0.98 0.99456522 0.99305823 0.70777222 49 5 6810
199 0.025 0.99 0.99782609 0.99652912 0.67898675 25 2 6834
200 0.025 1.00 1.00000000 1.00000000 0.00000000 0 0 6859
201 0.050 0.01 0.04294918 0.04422162 0.85435587 6098 1337 284
202 0.050 0.02 0.06156049 0.05913356 0.71737996 6008 1311 374
203 0.050 0.03 0.06800286 0.06710374 0.92887484 5955 1302 427
204 0.050 0.04 0.08088762 0.07828770 0.73053868 5886 1284 496
205 0.050 0.05 0.09090909 0.08792904 0.70240006 5825 1270 557
206 0.050 0.06 0.10236220 0.09949865 0.72977690 5751 1254 631
207 0.050 0.07 0.11166786 0.10824013 0.68346965 5696 1241 686
208 0.050 0.08 0.12598425 0.11813858 0.33842728 5639 1221 743
209 0.050 0.09 0.13815319 0.13022239 0.35313273 5562 1204 820
210 0.050 0.10 0.15175376 0.13909243 0.14233249 5512 1185 870
211 0.050 0.11 0.16105941 0.14899087 0.17474629 5448 1172 934
212 0.050 0.12 0.17394417 0.16068903 0.14728643 5375 1154 1007
213 0.050 0.13 0.17967072 0.17058748 0.33846388 5306 1146 1076
214 0.050 0.14 0.19398712 0.18151433 0.19463166 5241 1126 1141
215 0.050 0.15 0.20758769 0.19449801 0.18439884 5159 1107 1223
216 0.050 0.16 0.21975662 0.20439645 0.12474480 5099 1090 1283
217 0.050 0.17 0.23693629 0.21943695 0.08743056 5006 1066 1376
218 0.050 0.18 0.24982105 0.23113511 0.07280195 4933 1048 1449
219 0.050 0.19 0.26342162 0.24424733 0.07072677 4850 1029 1532
220 0.050 0.20 0.27702219 0.26005913 0.11828051 4746 1010 1636
221 0.050 0.21 0.28274875 0.26905772 0.21473522 4684 1002 1698
222 0.050 0.22 0.30207588 0.28474097 0.12058168 4589 975 1793
223 0.050 0.23 0.31710809 0.29708189 0.07572286 4514 954 1868
224 0.050 0.24 0.33643522 0.31263659 0.03692414 4420 927 1962
225 0.050 0.25 0.34788833 0.32754853 0.07893279 4320 911 2062
226 0.050 0.26 0.36148890 0.34657411 0.20684991 4191 892 2191
227 0.050 0.27 0.37365784 0.36264301 0.36034529 4083 875 2299
228 0.050 0.28 0.39298497 0.37871192 0.23650306 3985 848 2397
229 0.050 0.29 0.40157480 0.39375241 0.52840576 3880 836 2502
230 0.050 0.30 0.41803865 0.41046407 0.54492837 3773 813 2609
231 0.050 0.31 0.43450251 0.42434760 0.41337854 3688 790 2694
232 0.050 0.32 0.44452398 0.43733128 0.56965554 3601 776 2781
233 0.050 0.33 0.46456693 0.45532845 0.46181833 3489 748 2893
234 0.050 0.34 0.48246242 0.47062604 0.34264280 3395 723 2987
235 0.050 0.35 0.48962062 0.48412392 0.67133427 3300 713 3082
236 0.050 0.36 0.50393701 0.49697905 0.58595159 3220 693 3162
237 0.050 0.37 0.52397996 0.51394781 0.42444961 3116 665 3266
238 0.050 0.38 0.53543307 0.53194498 0.79574125 2992 649 3390
239 0.050 0.39 0.55977094 0.55084201 0.47705241 2879 615 3503
240 0.050 0.40 0.57337151 0.56588250 0.55270897 2781 596 3601
241 0.050 0.41 0.58697208 0.58015169 0.58896517 2689 577 3693
242 0.050 0.42 0.60486757 0.59634915 0.49249187 2588 552 3794
243 0.050 0.43 0.61775233 0.61164674 0.62651164 2487 534 3895
244 0.050 0.44 0.63493200 0.62630158 0.48042901 2397 510 3985
245 0.050 0.45 0.64924839 0.64378455 0.65995373 2281 490 4101
246 0.050 0.46 0.65783822 0.65702532 0.96845078 2190 478 4192
247 0.050 0.47 0.66714388 0.67026610 0.80828393 2100 465 4282
248 0.050 0.48 0.67859699 0.68222137 0.77220607 2023 449 4359
249 0.050 0.49 0.69076593 0.69070575 1.00000000 1974 432 4408
250 0.050 0.50 0.70150322 0.70407507 0.84136606 1885 417 4497
251 0.050 0.51 0.71510379 0.71358786 0.91581979 1830 398 4552
252 0.050 0.52 0.73013601 0.72695719 0.79387939 1747 377 4635
253 0.050 0.53 0.73729420 0.73736984 1.00000000 1676 367 4706
254 0.050 0.54 0.74946314 0.74701118 0.84244197 1618 350 4764
255 0.050 0.55 0.76306371 0.75870935 0.69991698 1546 331 4836
256 0.050 0.56 0.77236936 0.76809359 0.70167448 1486 318 4896
257 0.050 0.57 0.77594846 0.77657797 0.97853473 1425 313 4957
258 0.050 0.58 0.78525412 0.78621931 0.95125993 1363 300 5019
259 0.050 0.59 0.79169649 0.79431804 0.81724020 1309 291 5073
260 0.050 0.60 0.80887616 0.80331662 0.58919675 1263 267 5119
261 0.050 0.61 0.81460272 0.81090114 0.72457463 1212 259 5170
262 0.050 0.62 0.82605583 0.81822856 0.42416435 1171 243 5211
263 0.050 0.63 0.83035075 0.82568454 0.63934712 1119 237 5263
264 0.050 0.64 0.83822477 0.83404037 0.67126528 1065 226 5317
265 0.050 0.65 0.84538296 0.84188199 0.72222793 1014 216 5368
266 0.050 0.66 0.85110952 0.84985217 0.91724694 960 208 5422
267 0.050 0.67 0.85612026 0.85576552 1.00000000 921 201 5461
268 0.050 0.68 0.86184681 0.86013626 0.87215120 895 193 5487
269 0.050 0.69 0.86685755 0.86592107 0.94413949 857 186 5525
270 0.050 0.70 0.88045812 0.87273428 0.36174913 823 167 5559
271 0.050 0.71 0.88260558 0.87787633 0.58169622 786 164 5596
272 0.050 0.72 0.88904796 0.88546086 0.67563884 736 155 5646
273 0.050 0.73 0.89191124 0.88996015 0.83360471 705 151 5677
274 0.050 0.74 0.89692198 0.89535930 0.87097642 670 144 5712
275 0.050 0.75 0.90193271 0.90037280 0.86846354 638 137 5744
276 0.050 0.76 0.90765927 0.90461499 0.70589327 613 129 5769
277 0.050 0.77 0.91410165 0.91027124 0.61609974 578 120 5804
278 0.050 0.78 0.92269148 0.91567039 0.32243234 548 108 5834
279 0.050 0.79 0.92555476 0.92016969 0.44403517 517 104 5865
280 0.050 0.80 0.92627058 0.92428333 0.79937705 486 103 5896
281 0.050 0.81 0.92770222 0.92736856 1.00000000 464 101 5918
282 0.050 0.82 0.93342878 0.93238205 0.90987035 433 93 5949
283 0.050 0.83 0.93772369 0.93778121 1.00000000 397 87 5985
284 0.050 0.84 0.94130279 0.94099499 1.00000000 377 82 6005
285 0.050 0.85 0.94774517 0.94433732 0.58304449 360 73 6022
286 0.050 0.86 0.95347173 0.94935082 0.47886128 329 65 6053
287 0.050 0.87 0.95561918 0.95269315 0.61765073 306 62 6076
288 0.050 0.88 0.96134574 0.95732099 0.45411109 278 54 6104
289 0.050 0.89 0.96492484 0.96117753 0.46904266 253 49 6129
290 0.050 0.90 0.96850394 0.96451986 0.41858735 232 44 6150
291 0.050 0.91 0.97136722 0.96696233 0.35013142 217 40 6165
292 0.050 0.92 0.97637795 0.97120453 0.23473558 191 33 6191
293 0.050 0.93 0.97995705 0.97531816 0.25487934 164 28 6218
294 0.050 0.94 0.98138869 0.97814629 0.41557872 144 26 6238
295 0.050 0.95 0.98711525 0.98213138 0.14959493 121 18 6261
296 0.050 0.96 0.99212598 0.98624502 0.05037509 96 11 6286
297 0.050 0.97 0.99427344 0.99010156 0.11188484 69 8 6313
298 0.050 0.98 0.99570508 0.99305823 0.25528324 48 6 6334
299 0.050 0.99 0.99856836 0.99652912 0.23812048 25 2 6357
300 0.050 1.00 1.00000000 1.00000000 0.00000000 0 0 6382
301 0.075 0.01 0.04519774 0.04422162 0.87169726 5745 1690 264
302 0.075 0.02 0.06271186 0.05913356 0.50359084 5660 1659 349
303 0.075 0.03 0.06892655 0.06710374 0.76822817 5609 1648 400
304 0.075 0.04 0.08022599 0.07828770 0.76794708 5542 1628 467
305 0.075 0.05 0.08983051 0.08792904 0.78434695 5484 1611 525
306 0.075 0.06 0.10225989 0.09949865 0.69181274 5416 1589 593
307 0.075 0.07 0.11186441 0.10824013 0.60663517 5365 1572 644
308 0.075 0.08 0.12655367 0.11813858 0.22778117 5314 1546 695
309 0.075 0.09 0.14067797 0.13022239 0.14791122 5245 1521 764
310 0.075 0.10 0.15254237 0.13909243 0.06853713 5197 1500 812
311 0.075 0.11 0.15988701 0.14899087 0.15363565 5133 1487 876
312 0.075 0.12 0.17175141 0.16068903 0.15999152 5063 1466 946
313 0.075 0.13 0.17683616 0.17058748 0.44770191 4995 1457 1014
314 0.075 0.14 0.18983051 0.18151433 0.31842322 4933 1434 1076
315 0.075 0.15 0.20056497 0.19449801 0.48420639 4851 1415 1158
316 0.075 0.16 0.21242938 0.20439645 0.35757170 4795 1394 1214
317 0.075 0.17 0.22994350 0.21943695 0.23699520 4709 1363 1300
318 0.075 0.18 0.24180791 0.23113511 0.23806915 4639 1342 1370
319 0.075 0.19 0.25649718 0.24424733 0.18242027 4563 1316 1446
320 0.075 0.20 0.27062147 0.26005913 0.26196487 4465 1291 1544
321 0.075 0.21 0.27966102 0.26905772 0.26526620 4411 1275 1598
322 0.075 0.22 0.30056497 0.28474097 0.09925384 4326 1238 1683
323 0.075 0.23 0.31581921 0.29708189 0.05321685 4257 1211 1752
324 0.075 0.24 0.33559322 0.31263659 0.01921443 4171 1176 1838
325 0.075 0.25 0.34576271 0.32754853 0.06740792 4073 1158 1936
326 0.075 0.26 0.35932203 0.34657411 0.20988272 3949 1134 2060
327 0.075 0.27 0.37401130 0.36264301 0.26968757 3850 1108 2159
328 0.075 0.28 0.39378531 0.37871192 0.14439320 3760 1073 2249
329 0.075 0.29 0.40451977 0.39375241 0.30430497 3662 1054 2347
330 0.075 0.30 0.42146893 0.41046407 0.29676898 3562 1024 2447
331 0.075 0.31 0.43785311 0.42434760 0.20030984 3483 995 2526
332 0.075 0.32 0.44745763 0.43733128 0.34215917 3399 978 2610
333 0.075 0.33 0.46892655 0.45532845 0.20057718 3297 940 2712
334 0.075 0.34 0.48531073 0.47062604 0.16721694 3207 911 2802
335 0.075 0.35 0.49491525 0.48412392 0.31413315 3119 894 2890
336 0.075 0.36 0.50960452 0.49697905 0.23732648 3045 868 2964
337 0.075 0.37 0.52937853 0.51394781 0.14683463 2948 833 3061
338 0.075 0.38 0.54124294 0.53194498 0.38710661 2829 812 3180
339 0.075 0.39 0.56214689 0.55084201 0.28880652 2719 775 3290
340 0.075 0.40 0.57740113 0.56588250 0.27784637 2629 748 3380
341 0.075 0.41 0.58813559 0.58015169 0.45508385 2537 729 3472
342 0.075 0.42 0.60508475 0.59634915 0.40952581 2441 699 3568
343 0.075 0.43 0.61920904 0.61164674 0.47461133 2347 674 3662
344 0.075 0.44 0.63728814 0.62630158 0.28954653 2265 642 3744
345 0.075 0.45 0.65141243 0.64378455 0.46280464 2154 617 3855
346 0.075 0.46 0.66101695 0.65702532 0.70838714 2068 600 3941
347 0.075 0.47 0.67175141 0.67026610 0.90252348 1984 581 4025
348 0.075 0.48 0.68531073 0.68222137 0.77291269 1915 557 4094
349 0.075 0.49 0.69830508 0.69070575 0.44858674 1872 534 4137
350 0.075 0.50 0.70847458 0.70407507 0.66592499 1786 516 4223
351 0.075 0.51 0.72033898 0.71358786 0.49339380 1733 495 4276
352 0.075 0.52 0.73672316 0.72695719 0.30823479 1658 466 4351
353 0.075 0.53 0.74463277 0.73736984 0.44767116 1591 452 4418
354 0.075 0.54 0.75706215 0.74701118 0.28209591 1538 430 4471
355 0.075 0.55 0.76949153 0.75870935 0.24012654 1469 408 4540
356 0.075 0.56 0.77853107 0.76809359 0.24941753 1412 392 4597
357 0.075 0.57 0.78305085 0.77657797 0.47684005 1354 384 4655
358 0.075 0.58 0.79378531 0.78621931 0.39509275 1298 365 4711
359 0.075 0.59 0.80000000 0.79431804 0.52253280 1246 354 4763
360 0.075 0.60 0.81525424 0.80331662 0.16044381 1203 327 4806
361 0.075 0.61 0.82203390 0.81090114 0.18472243 1156 315 4853
362 0.075 0.62 0.83220339 0.81822856 0.08922223 1117 297 4892
363 0.075 0.63 0.83841808 0.82568454 0.11618059 1070 286 4939
364 0.075 0.64 0.84576271 0.83404037 0.14105238 1018 273 4991
365 0.075 0.65 0.85254237 0.84188199 0.17333311 969 261 5040
366 0.075 0.66 0.85875706 0.84985217 0.24791305 918 250 5091
367 0.075 0.67 0.86384181 0.85576552 0.28827988 881 241 5128
368 0.075 0.68 0.86892655 0.86013626 0.24032943 856 232 5153
369 0.075 0.69 0.87514124 0.86592107 0.20925909 822 221 5187
370 0.075 0.70 0.88700565 0.87273428 0.04451081 790 200 5219
371 0.075 0.71 0.89039548 0.87787633 0.07362583 756 194 5253
372 0.075 0.72 0.89548023 0.88546086 0.14331860 706 185 5303
373 0.075 0.73 0.89774011 0.88996015 0.25144829 675 181 5334
374 0.075 0.74 0.90282486 0.89535930 0.26129594 642 172 5367
375 0.075 0.75 0.90790960 0.90037280 0.24628155 612 163 5397
376 0.075 0.76 0.91242938 0.90461499 0.21967856 587 155 5422
377 0.075 0.77 0.91977401 0.91027124 0.12250816 556 142 5453
378 0.075 0.78 0.92711864 0.91567039 0.05442616 527 129 5482
379 0.075 0.79 0.92994350 0.92016969 0.09367579 497 124 5512
380 0.075 0.80 0.93050847 0.92428333 0.28223992 466 123 5543
381 0.075 0.81 0.93220339 0.92736856 0.40110897 445 120 5564
382 0.075 0.82 0.93672316 0.93238205 0.43908117 414 112 5595
383 0.075 0.83 0.94237288 0.93778121 0.39313375 382 102 5627
384 0.075 0.84 0.94576271 0.94099499 0.36221075 363 96 5646
385 0.075 0.85 0.95141243 0.94433732 0.15613053 347 86 5662
386 0.075 0.86 0.95593220 0.94935082 0.16912170 316 78 5693
387 0.075 0.87 0.95819209 0.95269315 0.23951143 294 74 5715
388 0.075 0.88 0.96327684 0.95732099 0.17909613 267 65 5742
389 0.075 0.89 0.96610169 0.96117753 0.25005514 242 60 5767
390 0.075 0.90 0.96949153 0.96451986 0.22498553 222 54 5787
391 0.075 0.91 0.97231638 0.96696233 0.17438423 208 49 5801
392 0.075 0.92 0.97683616 0.97120453 0.12573514 183 41 5826
393 0.075 0.93 0.98079096 0.97531816 0.10930547 158 34 5851
394 0.075 0.94 0.98192090 0.97814629 0.25290014 138 32 5871
395 0.075 0.95 0.98644068 0.98213138 0.14565484 115 24 5894
396 0.075 0.96 0.99096045 0.98624502 0.06847458 91 16 5918
397 0.075 0.97 0.99378531 0.99010156 0.10004857 66 11 5943
398 0.075 0.98 0.99491525 0.99305823 0.36399814 45 9 5964
399 0.075 0.99 0.99830508 0.99652912 0.22414616 24 3 5985
400 0.075 1.00 1.00000000 1.00000000 0.00000000 0 0 6009
401 0.100 0.01 0.04588910 0.04422162 0.71012595 5439 1996 248
402 0.100 0.02 0.06214149 0.05913356 0.53003141 5357 1962 330
403 0.100 0.03 0.06931166 0.06710374 0.67378597 5310 1947 377
404 0.100 0.04 0.08078394 0.07828770 0.65306816 5247 1923 440
405 0.100 0.05 0.09034417 0.08792904 0.68102936 5192 1903 495
406 0.100 0.06 0.10277247 0.09949865 0.58757546 5128 1877 559
407 0.100 0.07 0.11137667 0.10824013 0.61785148 5078 1859 609
408 0.100 0.08 0.12571702 0.11813858 0.22384199 5031 1829 656
409 0.100 0.09 0.14005736 0.13022239 0.12719531 4967 1799 720
410 0.100 0.10 0.15152964 0.13909243 0.05933870 4922 1775 765
411 0.100 0.11 0.15869981 0.14899087 0.15483491 4860 1760 827
412 0.100 0.12 0.17112811 0.16068903 0.13734180 4795 1734 892
413 0.100 0.13 0.17782027 0.17058748 0.31992424 4732 1720 955
414 0.100 0.14 0.18977055 0.18151433 0.26585318 4672 1695 1015
415 0.100 0.15 0.20076482 0.19449801 0.41527443 4594 1672 1093
416 0.100 0.16 0.21271511 0.20439645 0.28381571 4542 1647 1145
417 0.100 0.17 0.22944551 0.21943695 0.20667939 4460 1612 1227
418 0.100 0.18 0.23996176 0.23113511 0.27583390 4391 1590 1296
419 0.100 0.19 0.25525813 0.24424733 0.17986495 4321 1558 1366
420 0.100 0.20 0.26912046 0.26005913 0.28199699 4227 1529 1460
421 0.100 0.21 0.27772467 0.26905772 0.30933464 4175 1511 1512
422 0.100 0.22 0.29780115 0.28474097 0.12857379 4095 1469 1592
423 0.100 0.23 0.31548757 0.29708189 0.03345318 4036 1432 1651
424 0.100 0.24 0.33365201 0.31263659 0.01650737 3953 1394 1734
425 0.100 0.25 0.34464627 0.32754853 0.05465896 3860 1371 1827
426 0.100 0.26 0.35946463 0.34657411 0.15498091 3743 1340 1944
427 0.100 0.27 0.37762906 0.36264301 0.10082371 3656 1302 2031
428 0.100 0.28 0.39674952 0.37871192 0.04966422 3571 1262 2116
429 0.100 0.29 0.40726577 0.39375241 0.14611935 3476 1240 2211
430 0.100 0.30 0.42351816 0.41046407 0.16344630 3380 1206 2307
431 0.100 0.31 0.44072658 0.42434760 0.08065836 3308 1170 2379
432 0.100 0.32 0.45124283 0.43733128 0.14037066 3229 1148 2458
433 0.100 0.33 0.47179732 0.45532845 0.08127123 3132 1105 2555
434 0.100 0.34 0.48661568 0.47062604 0.09140579 3044 1074 2643
435 0.100 0.35 0.49617591 0.48412392 0.20606028 2959 1054 2728
436 0.100 0.36 0.51003824 0.49697905 0.17018210 2888 1025 2799
437 0.100 0.37 0.52724665 0.51394781 0.16217993 2792 989 2895
438 0.100 0.38 0.53919694 0.53194498 0.45215257 2677 964 3010
439 0.100 0.39 0.56022945 0.55084201 0.32518296 2574 920 3113
440 0.100 0.40 0.57600382 0.56588250 0.28616203 2490 887 3197
441 0.100 0.41 0.58795411 0.58015169 0.41233645 2404 862 3283
442 0.100 0.42 0.60372849 0.59634915 0.43626579 2311 829 3376
443 0.100 0.43 0.61902486 0.61164674 0.43328870 2224 797 3463
444 0.100 0.44 0.63718929 0.62630158 0.23901262 2148 759 3539
445 0.100 0.45 0.65057361 0.64378455 0.46436540 2040 731 3647
446 0.100 0.46 0.66061185 0.65702532 0.70600491 1958 710 3729
447 0.100 0.47 0.67256214 0.67026610 0.81493417 1880 685 3807
448 0.100 0.48 0.68594646 0.68222137 0.68877986 1815 657 3872
449 0.100 0.49 0.69837476 0.69070575 0.38983321 1775 631 3912
450 0.100 0.50 0.70984704 0.70407507 0.51671168 1695 607 3992
451 0.100 0.51 0.72131931 0.71358786 0.37531926 1645 583 4042
452 0.100 0.52 0.73709369 0.72695719 0.23468249 1574 550 4113
453 0.100 0.53 0.74474187 0.73736984 0.38589733 1509 534 4178
454 0.100 0.54 0.75621415 0.74701118 0.27001474 1458 510 4229
455 0.100 0.55 0.76720841 0.75870935 0.30174181 1390 487 4297
456 0.100 0.56 0.77533461 0.76809359 0.37481806 1334 470 4353
457 0.100 0.57 0.77915870 0.77657797 0.76361763 1276 462 4411
458 0.100 0.58 0.79063098 0.78621931 0.58613174 1225 438 4462
459 0.100 0.59 0.79780115 0.79431804 0.66767621 1177 423 4510
460 0.100 0.60 0.81214149 0.80331662 0.24789884 1137 393 4550
461 0.100 0.61 0.81835564 0.81090114 0.32428724 1091 380 4596
462 0.100 0.62 0.82743786 0.81822856 0.21340784 1053 361 4634
463 0.100 0.63 0.83508604 0.82568454 0.19638022 1011 345 4676
464 0.100 0.64 0.84273423 0.83404037 0.22411407 962 329 4725
465 0.100 0.65 0.85086042 0.84188199 0.20007085 918 312 4769
466 0.100 0.66 0.85755258 0.84985217 0.26384303 870 298 4817
467 0.100 0.67 0.86281071 0.85576552 0.30005435 835 287 4852
468 0.100 0.68 0.86806883 0.86013626 0.23539824 812 276 4875
469 0.100 0.69 0.87428298 0.86592107 0.20222299 780 263 4907
470 0.100 0.70 0.88432122 0.87273428 0.06853599 748 242 4939
471 0.100 0.71 0.88957935 0.87787633 0.06107832 719 231 4968
472 0.100 0.72 0.89435946 0.88546086 0.14578574 670 221 5017
473 0.100 0.73 0.89674952 0.88996015 0.26283635 640 216 5047
474 0.100 0.74 0.90105163 0.89535930 0.34056771 607 207 5080
475 0.100 0.75 0.90630975 0.90037280 0.30882099 579 196 5108
476 0.100 0.76 0.91061185 0.90461499 0.29438405 555 187 5132
477 0.100 0.77 0.91826004 0.91027124 0.14689862 527 171 5160
478 0.100 0.78 0.92590822 0.91567039 0.05425178 501 155 5186
479 0.100 0.79 0.92829828 0.92016969 0.11942893 471 150 5216
480 0.100 0.80 0.92925430 0.92428333 0.33864201 441 148 5246
481 0.100 0.81 0.93164436 0.92736856 0.40538060 422 143 5265
482 0.100 0.82 0.93690249 0.93238205 0.36169494 394 132 5293
483 0.100 0.83 0.94311663 0.93778121 0.25905016 365 119 5322
484 0.100 0.84 0.94694073 0.94099499 0.19513419 348 111 5339
485 0.100 0.85 0.95267686 0.94433732 0.05875351 334 99 5353
486 0.100 0.86 0.95745698 0.94935082 0.05495953 305 89 5382
487 0.100 0.87 0.95984704 0.95269315 0.08143883 284 84 5403
488 0.100 0.88 0.96462715 0.95732099 0.06144314 258 74 5429
489 0.100 0.89 0.96749522 0.96117753 0.09231266 234 68 5453
490 0.100 0.90 0.97084130 0.96451986 0.07860201 215 61 5472
491 0.100 0.91 0.97370937 0.96696233 0.05144064 202 55 5485
492 0.100 0.92 0.97753346 0.97120453 0.05141121 177 47 5510
493 0.100 0.93 0.98135755 0.97531816 0.04551724 153 39 5534
494 0.100 0.94 0.98231358 0.97814629 0.15064078 133 37 5554
495 0.100 0.95 0.98613767 0.98213138 0.12819855 110 29 5577
496 0.100 0.96 0.99091778 0.98624502 0.04171606 88 19 5599
497 0.100 0.97 0.99378585 0.99010156 0.06264913 64 13 5623
498 0.100 0.98 0.99474187 0.99305823 0.35197885 43 11 5644
499 0.100 0.99 0.99808795 0.99652912 0.22995331 23 4 5664
500 0.100 1.00 1.00000000 1.00000000 0.00000000 0 0 5687
Dboth
1 17
2 25
3 29
4 34
5 39
6 46
7 51
8 59
9 64
10 72
11 76
12 83
13 86
14 91
15 97
16 102
17 110
18 116
19 119
20 124
21 128
22 140
23 149
24 158
25 162
26 170
27 177
28 182
29 185
30 194
31 197
32 201
33 209
34 215
35 218
36 226
37 238
38 243
39 254
40 260
41 268
42 276
43 278
44 283
45 292
46 296
47 303
48 309
49 320
50 324
51 332
52 340
53 342
54 344
55 349
56 355
57 356
58 363
59 364
60 368
61 368
62 375
63 376
64 382
65 387
66 389
67 393
68 396
69 397
70 403
71 404
72 408
73 408
74 410
75 412
76 414
77 414
78 420
79 422
80 423
81 424
82 427
83 430
84 431
85 433
86 436
87 437
88 439
89 440
90 442
91 444
92 444
93 446
94 446
95 450
96 452
97 453
98 453
99 456
100 457
101 35
102 51
103 57
104 68
105 78
106 91
107 101
108 115
109 126
110 138
111 146
112 158
113 164
114 176
115 189
116 198
117 214
118 225
119 238
120 250
121 256
122 271
123 285
124 299
125 310
126 323
127 337
128 350
129 357
130 374
131 388
132 395
133 411
134 423
135 429
136 441
137 460
138 469
139 491
140 503
141 516
142 536
143 546
144 559
145 577
146 584
147 595
148 607
149 621
150 633
151 648
152 662
153 668
154 678
155 691
156 698
157 700
158 712
159 717
160 732
161 734
162 747
163 749
164 758
165 767
166 775
167 779
168 786
169 791
170 804
171 806
172 813
173 817
174 822
175 826
176 831
177 836
178 846
179 848
180 849
181 850
182 855
183 860
184 863
185 869
186 874
187 875
188 881
189 883
190 887
191 891
192 895
193 898
194 899
195 906
196 911
197 913
198 915
199 918
200 920
201 60
202 86
203 95
204 113
205 127
206 143
207 156
208 176
209 193
210 212
211 225
212 243
213 251
214 271
215 290
216 307
217 331
218 349
219 368
220 387
221 395
222 422
223 443
224 470
225 486
226 505
227 522
228 549
229 561
230 584
231 607
232 621
233 649
234 674
235 684
236 704
237 732
238 748
239 782
240 801
241 820
242 845
243 863
244 887
245 907
246 919
247 932
248 948
249 965
250 980
251 999
252 1020
253 1030
254 1047
255 1066
256 1079
257 1084
258 1097
259 1106
260 1130
261 1138
262 1154
263 1160
264 1171
265 1181
266 1189
267 1196
268 1204
269 1211
270 1230
271 1233
272 1242
273 1246
274 1253
275 1260
276 1268
277 1277
278 1289
279 1293
280 1294
281 1296
282 1304
283 1310
284 1315
285 1324
286 1332
287 1335
288 1343
289 1348
290 1353
291 1357
292 1364
293 1369
294 1371
295 1379
296 1386
297 1389
298 1391
299 1395
300 1397
301 80
302 111
303 122
304 142
305 159
306 181
307 198
308 224
309 249
310 270
311 283
312 304
313 313
314 336
315 355
316 376
317 407
318 428
319 454
320 479
321 495
322 532
323 559
324 594
325 612
326 636
327 662
328 697
329 716
330 746
331 775
332 792
333 830
334 859
335 876
336 902
337 937
338 958
339 995
340 1022
341 1041
342 1071
343 1096
344 1128
345 1153
346 1170
347 1189
348 1213
349 1236
350 1254
351 1275
352 1304
353 1318
354 1340
355 1362
356 1378
357 1386
358 1405
359 1416
360 1443
361 1455
362 1473
363 1484
364 1497
365 1509
366 1520
367 1529
368 1538
369 1549
370 1570
371 1576
372 1585
373 1589
374 1598
375 1607
376 1615
377 1628
378 1641
379 1646
380 1647
381 1650
382 1658
383 1668
384 1674
385 1684
386 1692
387 1696
388 1705
389 1710
390 1716
391 1721
392 1729
393 1736
394 1738
395 1746
396 1754
397 1759
398 1761
399 1767
400 1770
401 96
402 130
403 145
404 169
405 189
406 215
407 233
408 263
409 293
410 317
411 332
412 358
413 372
414 397
415 420
416 445
417 480
418 502
419 534
420 563
421 581
422 623
423 660
424 698
425 721
426 752
427 790
428 830
429 852
430 886
431 922
432 944
433 987
434 1018
435 1038
436 1067
437 1103
438 1128
439 1172
440 1205
441 1230
442 1263
443 1295
444 1333
445 1361
446 1382
447 1407
448 1435
449 1461
450 1485
451 1509
452 1542
453 1558
454 1582
455 1605
456 1622
457 1630
458 1654
459 1669
460 1699
461 1712
462 1731
463 1747
464 1763
465 1780
466 1794
467 1805
468 1816
469 1829
470 1850
471 1861
472 1871
473 1876
474 1885
475 1896
476 1905
477 1921
478 1937
479 1942
480 1944
481 1949
482 1960
483 1973
484 1981
485 1993
486 2003
487 2008
488 2018
489 2024
490 2031
491 2037
492 2045
493 2053
494 2055
495 2063
496 2073
497 2079
498 2081
499 2088
500 2092enrichment.plotter(gene.hic.filt, "median_FDR.H", "adj.P.Val", "Median FDR of Hi-C Contacts Overlapping Gene, Human")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.02340426 0.02099433 0.833659804 7142 459 152
2 0.010 0.02 0.02765957 0.02743431 1.000000000 7094 457 200
3 0.010 0.03 0.03191489 0.02910871 0.816678531 7083 455 211
4 0.010 0.04 0.03617021 0.03232870 0.725390082 7060 453 234
5 0.010 0.05 0.03829787 0.03464709 0.751707478 7043 452 251
6 0.010 0.06 0.04468085 0.03735188 0.459901633 7025 449 269
7 0.010 0.07 0.04680851 0.03915507 0.447323621 7012 448 282
8 0.010 0.08 0.05319149 0.04070067 0.195841919 7003 445 291
9 0.010 0.09 0.05957447 0.04211747 0.067923863 6995 442 299
10 0.010 0.10 0.05957447 0.04314786 0.090812369 6987 442 307
11 0.010 0.11 0.06382979 0.04456466 0.048501670 6978 440 316
12 0.010 0.12 0.06382979 0.04662545 0.086839349 6962 440 332
13 0.010 0.13 0.06595745 0.04752705 0.067897566 6956 439 338
14 0.010 0.14 0.06595745 0.04829985 0.083427659 6950 439 344
15 0.010 0.15 0.06808511 0.05010304 0.082815724 6937 438 357
16 0.010 0.16 0.07446809 0.05280783 0.039414320 6919 435 375
17 0.010 0.17 0.07872340 0.05577022 0.032880085 6898 433 396
18 0.010 0.18 0.07872340 0.05692942 0.045380555 6889 433 405
19 0.010 0.19 0.07872340 0.05989181 0.093968894 6866 433 428
20 0.010 0.20 0.08085106 0.06272540 0.115508670 6845 432 449
21 0.010 0.21 0.08297872 0.06555899 0.139392436 6824 431 470
22 0.010 0.22 0.08510638 0.06890778 0.181400672 6799 430 495
23 0.010 0.23 0.08936170 0.07122617 0.137648595 6783 428 511
24 0.010 0.24 0.09574468 0.07444616 0.084674815 6761 425 533
25 0.010 0.25 0.09787234 0.07766615 0.109660441 6737 424 557
26 0.010 0.26 0.09787234 0.08088614 0.191499773 6712 424 582
27 0.010 0.27 0.09787234 0.08397733 0.300779214 6688 424 606
28 0.010 0.28 0.10851064 0.08925811 0.153613186 6652 419 642
29 0.010 0.29 0.11489362 0.09312210 0.110976109 6625 416 669
30 0.010 0.30 0.11702128 0.09763009 0.167248766 6591 415 703
31 0.010 0.31 0.11914894 0.10110768 0.207811386 6565 414 729
32 0.010 0.32 0.12340426 0.10432767 0.187504334 6542 412 752
33 0.010 0.33 0.12765957 0.11038125 0.247131581 6497 410 797
34 0.010 0.34 0.12978723 0.11591963 0.371008008 6455 409 839
35 0.010 0.35 0.13829787 0.12313241 0.337106496 6403 405 891
36 0.010 0.36 0.14468085 0.12995878 0.363607710 6353 402 941
37 0.010 0.37 0.15957447 0.13691396 0.159955390 6306 395 988
38 0.010 0.38 0.16382979 0.14502834 0.259876354 6245 393 1049
39 0.010 0.39 0.17234043 0.15262751 0.246098026 6190 389 1104
40 0.010 0.40 0.18297872 0.15984029 0.177870256 6139 384 1155
41 0.010 0.41 0.19148936 0.16834106 0.186780005 6077 380 1217
42 0.010 0.42 0.20851064 0.17851623 0.091071959 6006 372 1288
43 0.010 0.43 0.21489362 0.18572901 0.106044837 5953 369 1341
44 0.010 0.44 0.22340426 0.19448738 0.115491701 5889 365 1405
45 0.010 0.45 0.23617021 0.20672334 0.116953068 5800 359 1494
46 0.010 0.46 0.24255319 0.21612571 0.168110253 5730 356 1564
47 0.010 0.47 0.25957447 0.22784647 0.102010183 5647 348 1647
48 0.010 0.48 0.27021277 0.23647604 0.085459047 5585 343 1709
49 0.010 0.49 0.28510638 0.24742401 0.057685956 5507 336 1787
50 0.010 0.50 0.30212766 0.26146316 0.043834233 5406 328 1888
51 0.010 0.51 0.31914894 0.27472952 0.029821204 5311 320 1983
52 0.010 0.52 0.33191489 0.28619268 0.027105254 5228 314 2066
53 0.010 0.53 0.34680851 0.29933024 0.023397111 5133 307 2161
54 0.010 0.54 0.35744681 0.31259660 0.034621433 5035 302 2259
55 0.010 0.55 0.37021277 0.32599176 0.039462634 4937 296 2357
56 0.010 0.56 0.38723404 0.33977331 0.028446598 4838 288 2456
57 0.010 0.57 0.40638298 0.35561566 0.020212639 4724 279 2570
58 0.010 0.58 0.42978723 0.37107161 0.007601680 4615 268 2679
59 0.010 0.59 0.43829787 0.38588357 0.018304990 4504 264 2790
60 0.010 0.60 0.45957447 0.40211231 0.010090452 4388 254 2906
61 0.010 0.61 0.47659574 0.41666667 0.007570726 4283 246 3011
62 0.010 0.62 0.48936170 0.43611540 0.018591266 4138 240 3156
63 0.010 0.63 0.51702128 0.45749614 0.008672141 3985 227 3309
64 0.010 0.64 0.52978723 0.47707367 0.020726076 3839 221 3455
65 0.010 0.65 0.54255319 0.49497682 0.037451999 3706 215 3588
66 0.010 0.66 0.55744681 0.51481195 0.062820556 3559 208 3735
67 0.010 0.67 0.57659574 0.53310149 0.057137124 3426 199 3868
68 0.010 0.68 0.60000000 0.55074704 0.030243434 3300 188 3994
69 0.010 0.69 0.61489362 0.56826378 0.039626457 3171 181 4123
70 0.010 0.70 0.64468085 0.58990211 0.014577899 3017 167 4277
71 0.010 0.71 0.67021277 0.60754766 0.004776590 2892 155 4402
72 0.010 0.72 0.68085106 0.63060278 0.022645993 2718 150 4576
73 0.010 0.73 0.69787234 0.65236991 0.036870295 2557 142 4737
74 0.010 0.74 0.70638298 0.67014426 0.094251866 2423 138 4871
75 0.010 0.75 0.72340426 0.68920659 0.109312814 2283 130 5011
76 0.010 0.76 0.75531915 0.70955693 0.027644096 2140 115 5154
77 0.010 0.77 0.76382979 0.72964967 0.095366188 1988 111 5306
78 0.010 0.78 0.79148936 0.75115920 0.042211141 1834 98 5460
79 0.010 0.79 0.81063830 0.76944874 0.033099364 1701 89 5593
80 0.010 0.80 0.82553191 0.78696548 0.040496522 1572 82 5722
81 0.010 0.81 0.83829787 0.80667182 0.083451286 1425 76 5869
82 0.010 0.82 0.85106383 0.82457496 0.134853016 1292 70 6002
83 0.010 0.83 0.87234043 0.84067491 0.061450082 1177 60 6117
84 0.010 0.84 0.88936170 0.85806285 0.052646261 1050 52 6244
85 0.010 0.85 0.89574468 0.87287481 0.143145290 938 49 6356
86 0.010 0.86 0.91702128 0.88794436 0.046992298 831 39 6463
87 0.010 0.87 0.93404255 0.90056672 0.015407715 741 31 6553
88 0.010 0.88 0.94468085 0.91228748 0.013240685 655 26 6639
89 0.010 0.89 0.94680851 0.92490984 0.077009517 558 25 6736
90 0.010 0.90 0.95744681 0.93495621 0.051965268 485 20 6809
91 0.010 0.91 0.95957447 0.94345698 0.144893507 420 19 6874
92 0.010 0.92 0.96595745 0.95337455 0.221695238 346 16 6948
93 0.010 0.93 0.97446809 0.96161772 0.169990509 286 12 7008
94 0.010 0.94 0.97872340 0.96831530 0.232782554 236 10 7058
95 0.010 0.95 0.98936170 0.97514168 0.058753603 188 5 7106
96 0.010 0.96 0.99361702 0.98261206 0.088924349 132 3 7162
97 0.010 0.97 0.99361702 0.98737764 0.299763968 95 3 7199
98 0.010 0.98 0.99361702 0.99149923 0.797349469 63 3 7231
99 0.010 0.99 0.99787234 0.99600721 0.776252403 30 1 7264
100 0.010 1.00 1.00000000 1.00000000 0.000000000 0 0 7294
101 0.025 0.01 0.01733478 0.02099433 0.481511738 6694 907 147
102 0.025 0.02 0.02383532 0.02743431 0.544662978 6650 901 191
103 0.025 0.03 0.02708559 0.02910871 0.775485276 6640 898 201
104 0.025 0.04 0.03033586 0.03232870 0.790593411 6618 895 223
105 0.025 0.05 0.03250271 0.03464709 0.776694448 6602 893 239
106 0.025 0.06 0.03791983 0.03735188 0.996427247 6586 888 255
107 0.025 0.07 0.03900325 0.03915507 1.000000000 6573 887 268
108 0.025 0.08 0.04225352 0.04070067 0.868454719 6564 884 277
109 0.025 0.09 0.04658722 0.04211747 0.526763518 6557 880 284
110 0.025 0.10 0.04658722 0.04314786 0.644398176 6549 880 292
111 0.025 0.11 0.04983749 0.04456466 0.458037970 6541 877 300
112 0.025 0.12 0.05092091 0.04662545 0.564451026 6526 876 315
113 0.025 0.13 0.05200433 0.04752705 0.549385926 6520 875 321
114 0.025 0.14 0.05200433 0.04829985 0.633040562 6514 875 327
115 0.025 0.15 0.05308776 0.05010304 0.717022059 6501 874 340
116 0.025 0.16 0.05633803 0.05280783 0.665392074 6483 871 358
117 0.025 0.17 0.05850488 0.05577022 0.757098193 6462 869 379
118 0.025 0.18 0.06067172 0.05692942 0.654825803 6455 867 386
119 0.025 0.19 0.06175515 0.05989181 0.856941800 6433 866 408
120 0.025 0.20 0.06717226 0.06272540 0.602175328 6416 861 425
121 0.025 0.21 0.07258938 0.06555899 0.396163613 6399 856 442
122 0.025 0.22 0.07475623 0.06890778 0.497720887 6375 854 466
123 0.025 0.23 0.07908992 0.07122617 0.356849449 6361 850 480
124 0.025 0.24 0.08234020 0.07444616 0.364650839 6339 847 502
125 0.025 0.25 0.08342362 0.07766615 0.528219381 6315 846 526
126 0.025 0.26 0.08450704 0.08088614 0.714731230 6291 845 550
127 0.025 0.27 0.08667389 0.08397733 0.801459790 6269 843 572
128 0.025 0.28 0.09425785 0.08925811 0.612811951 6235 836 606
129 0.025 0.29 0.09967497 0.09312210 0.503185757 6210 831 631
130 0.025 0.30 0.10400867 0.09763009 0.524468591 6179 827 662
131 0.025 0.31 0.10725894 0.10110768 0.547017769 6155 824 686
132 0.025 0.32 0.11159263 0.10432767 0.476557087 6134 820 707
133 0.025 0.33 0.11700975 0.11038125 0.529565173 6092 815 749
134 0.025 0.34 0.12351029 0.11591963 0.476054547 6055 809 786
135 0.025 0.35 0.12892741 0.12313241 0.604847182 6004 804 837
136 0.025 0.36 0.13217768 0.12995878 0.871752414 5954 801 887
137 0.025 0.37 0.14301192 0.13691396 0.600879947 5910 791 931
138 0.025 0.38 0.15059588 0.14502834 0.644121140 5854 784 987
139 0.025 0.39 0.16251354 0.15262751 0.400367564 5806 773 1035
140 0.025 0.40 0.17009751 0.15984029 0.390851028 5757 766 1084
141 0.025 0.41 0.17876490 0.16834106 0.392659762 5699 758 1142
142 0.025 0.42 0.19284940 0.17851623 0.243769379 5633 745 1208
143 0.025 0.43 0.19934995 0.18572901 0.276359885 5583 739 1258
144 0.025 0.44 0.20585049 0.19448738 0.376220336 5521 733 1320
145 0.025 0.45 0.21668472 0.20672334 0.451535768 5436 723 1405
146 0.025 0.46 0.22318527 0.21612571 0.608286615 5369 717 1472
147 0.025 0.47 0.23943662 0.22784647 0.393917808 5293 702 1548
148 0.025 0.48 0.24918743 0.23647604 0.353950656 5235 693 1606
149 0.025 0.49 0.26218852 0.24742401 0.286073605 5162 681 1679
150 0.025 0.50 0.28060672 0.26146316 0.170659460 5070 664 1771
151 0.025 0.51 0.30010834 0.27472952 0.071723473 4985 646 1856
152 0.025 0.52 0.31310943 0.28619268 0.058935516 4908 634 1933
153 0.025 0.53 0.33044420 0.29933024 0.030724382 4822 618 2019
154 0.025 0.54 0.34236186 0.31259660 0.041308846 4730 607 2111
155 0.025 0.55 0.35319610 0.32599176 0.065623348 4636 597 2205
156 0.025 0.56 0.36944745 0.33977331 0.046507054 4544 582 2297
157 0.025 0.57 0.38786566 0.35561566 0.032045223 4438 565 2403
158 0.025 0.58 0.40628386 0.37107161 0.020189022 4335 548 2506
159 0.025 0.59 0.41495125 0.38588357 0.057883648 4228 540 2613
160 0.025 0.60 0.43661972 0.40211231 0.024959636 4122 520 2719
161 0.025 0.61 0.45070423 0.41666667 0.027879083 4022 507 2819
162 0.025 0.62 0.46912243 0.43611540 0.034100118 3888 490 2953
163 0.025 0.63 0.48754063 0.45749614 0.055277462 3739 473 3102
164 0.025 0.64 0.50054171 0.47707367 0.137381855 3599 461 3242
165 0.025 0.65 0.51679307 0.49497682 0.168451539 3475 446 3366
166 0.025 0.66 0.53196100 0.51481195 0.282157666 3335 432 3506
167 0.025 0.67 0.54496208 0.53310149 0.462768663 3205 420 3636
168 0.025 0.68 0.56771398 0.55074704 0.285185102 3089 399 3752
169 0.025 0.69 0.58504875 0.56826378 0.288515129 2969 383 3872
170 0.025 0.70 0.60888407 0.58990211 0.224963174 2823 361 4018
171 0.025 0.71 0.63380282 0.60754766 0.088314449 2709 338 4132
172 0.025 0.72 0.64897075 0.63060278 0.231924445 2544 324 4297
173 0.025 0.73 0.67172264 0.65236991 0.201083791 2396 303 4445
174 0.025 0.74 0.68364030 0.67014426 0.372516288 2269 292 4572
175 0.025 0.75 0.70205850 0.68920659 0.389308890 2138 275 4703
176 0.025 0.76 0.72806067 0.70955693 0.200332523 2004 251 4837
177 0.025 0.77 0.73997833 0.72964967 0.475722608 1859 240 4982
178 0.025 0.78 0.76273023 0.75115920 0.408992139 1713 219 5128
179 0.025 0.79 0.78223185 0.76944874 0.346869417 1589 201 5252
180 0.025 0.80 0.79631636 0.78696548 0.486221503 1466 188 5375
181 0.025 0.81 0.80390033 0.80667182 0.854996567 1320 181 5521
182 0.025 0.82 0.83098592 0.82457496 0.617451889 1206 156 5635
183 0.025 0.83 0.84507042 0.84067491 0.733243885 1094 143 5747
184 0.025 0.84 0.86673889 0.85806285 0.450612948 979 123 5862
185 0.025 0.85 0.88082340 0.87287481 0.471734190 877 110 5964
186 0.025 0.86 0.90249187 0.88794436 0.150694157 780 90 6061
187 0.025 0.87 0.91982665 0.90056672 0.042915472 698 74 6143
188 0.025 0.88 0.93174431 0.91228748 0.030449437 618 63 6223
189 0.025 0.89 0.93932828 0.92490984 0.088337109 527 56 6314
190 0.025 0.90 0.94907909 0.93495621 0.074672476 458 47 6383
191 0.025 0.91 0.95882990 0.94345698 0.037681027 401 38 6440
192 0.025 0.92 0.96533044 0.95337455 0.079738257 330 32 6511
193 0.025 0.93 0.97183099 0.96161772 0.103239761 272 26 6569
194 0.025 0.94 0.97724810 0.96831530 0.121023881 225 21 6616
195 0.025 0.95 0.98483207 0.97514168 0.057192973 179 14 6662
196 0.025 0.96 0.99024919 0.98261206 0.078935050 126 9 6715
197 0.025 0.97 0.99241603 0.98737764 0.192349758 91 7 6750
198 0.025 0.98 0.99349946 0.99149923 0.607121665 60 6 6781
199 0.025 0.99 0.99674973 0.99600721 0.917915743 28 3 6813
200 0.025 1.00 1.00000000 1.00000000 0.000000000 0 0 6841
201 0.050 0.01 0.01927195 0.02099433 0.693727342 6227 1374 136
202 0.050 0.02 0.02569593 0.02743431 0.726577541 6186 1365 177
203 0.050 0.03 0.02783726 0.02910871 0.822034197 6176 1362 187
204 0.050 0.04 0.03069236 0.03232870 0.764875100 6155 1358 208
205 0.050 0.05 0.03283369 0.03464709 0.741942430 6140 1355 223
206 0.050 0.06 0.03640257 0.03735188 0.897220531 6124 1350 239
207 0.050 0.07 0.03711635 0.03915507 0.719965435 6111 1349 252
208 0.050 0.08 0.03997145 0.04070067 0.937900904 6103 1345 260
209 0.050 0.09 0.04282655 0.04211747 0.942204788 6096 1341 267
210 0.050 0.10 0.04425410 0.04314786 0.878807488 6090 1339 273
211 0.050 0.11 0.04710921 0.04456466 0.661136815 6083 1335 280
212 0.050 0.12 0.04925054 0.04662545 0.656461714 6070 1332 293
213 0.050 0.13 0.04996431 0.04752705 0.686011234 6064 1331 299
214 0.050 0.14 0.04996431 0.04829985 0.800917717 6058 1331 305
215 0.050 0.15 0.05067809 0.05010304 0.967019974 6045 1330 318
216 0.050 0.16 0.05424697 0.05280783 0.841423215 6029 1325 334
217 0.050 0.17 0.05781585 0.05577022 0.760925638 6011 1320 352
218 0.050 0.18 0.05995717 0.05692942 0.633655788 6005 1317 358
219 0.050 0.19 0.06067095 0.05989181 0.941348463 5983 1316 380
220 0.050 0.20 0.06495360 0.06272540 0.749652707 5967 1310 396
221 0.050 0.21 0.06923626 0.06555899 0.579127279 5951 1304 412
222 0.050 0.22 0.07066381 0.06890778 0.819348883 5927 1302 436
223 0.050 0.23 0.07351892 0.07122617 0.755654395 5913 1298 450
224 0.050 0.24 0.07637402 0.07444616 0.804564839 5892 1294 471
225 0.050 0.25 0.07922912 0.07766615 0.852197694 5871 1290 492
226 0.050 0.26 0.07994290 0.08088614 0.929150280 5847 1289 516
227 0.050 0.27 0.08279800 0.08397733 0.902421287 5827 1285 536
228 0.050 0.28 0.09207709 0.08925811 0.721064560 5799 1272 564
229 0.050 0.29 0.09778729 0.09312210 0.539898714 5777 1264 586
230 0.050 0.30 0.10135617 0.09763009 0.638837615 5747 1259 616
231 0.050 0.31 0.10492505 0.10110768 0.635076215 5725 1254 638
232 0.050 0.32 0.10920771 0.10432767 0.540680799 5706 1248 657
233 0.050 0.33 0.11491792 0.11038125 0.581300301 5667 1240 696
234 0.050 0.34 0.12062812 0.11591963 0.574098621 5632 1232 731
235 0.050 0.35 0.12562455 0.12313241 0.788179231 5583 1225 780
236 0.050 0.36 0.13133476 0.12995878 0.900281689 5538 1217 825
237 0.050 0.37 0.14061385 0.13691396 0.687622748 5497 1204 866
238 0.050 0.38 0.14632405 0.14502834 0.912223767 5442 1196 921
239 0.050 0.39 0.15631692 0.15262751 0.701621097 5397 1182 966
240 0.050 0.40 0.16345468 0.15984029 0.713224802 5351 1172 1012
241 0.050 0.41 0.17416131 0.16834106 0.546041101 5300 1157 1063
242 0.050 0.42 0.18772305 0.17851623 0.339322599 5240 1138 1123
243 0.050 0.43 0.19414704 0.18572901 0.391420467 5193 1129 1170
244 0.050 0.44 0.20271235 0.19448738 0.411134974 5137 1117 1226
245 0.050 0.45 0.21413276 0.20672334 0.471487652 5058 1101 1305
246 0.050 0.46 0.22341185 0.21612571 0.486397133 4998 1088 1365
247 0.050 0.47 0.23911492 0.22784647 0.282112946 4929 1066 1434
248 0.050 0.48 0.24910778 0.23647604 0.232329793 4876 1052 1487
249 0.050 0.49 0.25910064 0.24742401 0.278096796 4805 1038 1558
250 0.050 0.50 0.28051392 0.26146316 0.078596187 4726 1008 1637
251 0.050 0.51 0.29550321 0.27472952 0.058610152 4644 987 1719
252 0.050 0.52 0.31049251 0.28619268 0.028508021 4576 966 1787
253 0.050 0.53 0.32476802 0.29933024 0.023553135 4494 946 1869
254 0.050 0.54 0.33333333 0.31259660 0.069102579 4403 934 1960
255 0.050 0.55 0.34832263 0.32599176 0.052596035 4320 913 2043
256 0.050 0.56 0.36259814 0.33977331 0.049839302 4233 893 2130
257 0.050 0.57 0.37615989 0.35561566 0.081228801 4129 874 2234
258 0.050 0.58 0.39400428 0.37107161 0.053339144 4034 849 2329
259 0.050 0.59 0.40471092 0.38588357 0.116704922 3934 834 2429
260 0.050 0.60 0.42612420 0.40211231 0.046079285 3838 804 2525
261 0.050 0.61 0.43968594 0.41666667 0.057358463 3744 785 2619
262 0.050 0.62 0.45681656 0.43611540 0.089847986 3617 761 2746
263 0.050 0.63 0.47822984 0.45749614 0.090815710 3481 731 2882
264 0.050 0.64 0.49393291 0.47707367 0.171926306 3351 709 3012
265 0.050 0.65 0.50892220 0.49497682 0.261136207 3233 688 3130
266 0.050 0.66 0.52248394 0.51481195 0.545071269 3098 669 3265
267 0.050 0.67 0.53818701 0.53310149 0.695149108 2978 647 3385
268 0.050 0.68 0.56459672 0.55074704 0.262062098 2878 610 3485
269 0.050 0.69 0.58172734 0.56826378 0.273931093 2766 586 3597
270 0.050 0.70 0.60528194 0.58990211 0.206642750 2631 553 3732
271 0.050 0.71 0.62740899 0.60754766 0.098634693 2525 522 3838
272 0.050 0.72 0.64525339 0.63060278 0.220771520 2371 497 3992
273 0.050 0.73 0.67023555 0.65236991 0.128478716 2237 462 4126
274 0.050 0.74 0.68593862 0.67014426 0.174601518 2121 440 4242
275 0.050 0.75 0.70378301 0.68920659 0.203978629 1998 415 4365
276 0.050 0.76 0.72947894 0.70955693 0.074761483 1876 379 4487
277 0.050 0.77 0.74518201 0.72964967 0.157741911 1742 357 4621
278 0.050 0.78 0.76302641 0.75115920 0.271002182 1600 332 4763
279 0.050 0.79 0.77872948 0.76944874 0.381023910 1480 310 4883
280 0.050 0.80 0.79514632 0.78696548 0.429498811 1367 287 4996
281 0.050 0.81 0.80371163 0.80667182 0.785193247 1226 275 5137
282 0.050 0.82 0.82655246 0.82457496 0.860154833 1119 243 5244
283 0.050 0.83 0.84296931 0.84067491 0.826738587 1017 220 5346
284 0.050 0.84 0.86224126 0.85806285 0.650728551 909 193 5454
285 0.050 0.85 0.87508922 0.87287481 0.817660644 812 175 5551
286 0.050 0.86 0.89293362 0.88794436 0.543724848 720 150 5643
287 0.050 0.87 0.91006424 0.90056672 0.206610092 646 126 5717
288 0.050 0.88 0.92148465 0.91228748 0.196318015 571 110 5792
289 0.050 0.89 0.93219129 0.92490984 0.277310565 488 95 5875
290 0.050 0.90 0.94289793 0.93495621 0.203486271 425 80 5938
291 0.050 0.91 0.95503212 0.94345698 0.044622773 376 63 5987
292 0.050 0.92 0.96145610 0.95337455 0.129812489 308 54 6055
293 0.050 0.93 0.97002141 0.96161772 0.083316367 256 42 6107
294 0.050 0.94 0.97573162 0.96831530 0.095642845 212 34 6151
295 0.050 0.95 0.98286938 0.97514168 0.050302131 169 24 6194
296 0.050 0.96 0.98715203 0.98261206 0.185781546 117 18 6246
297 0.050 0.97 0.99000714 0.98737764 0.399970625 84 14 6279
298 0.050 0.98 0.99286224 0.99149923 0.650466342 56 10 6307
299 0.050 0.99 0.99643112 0.99600721 0.964951799 26 5 6337
300 0.050 1.00 1.00000000 1.00000000 0.000000000 0 0 6363
301 0.075 0.01 0.01925255 0.02099433 0.626642746 5869 1732 129
302 0.075 0.02 0.02604757 0.02743431 0.746669356 5831 1720 167
303 0.075 0.03 0.02774632 0.02910871 0.758882892 5821 1717 177
304 0.075 0.04 0.03114383 0.03232870 0.807417926 5802 1711 196
305 0.075 0.05 0.03340883 0.03464709 0.802818314 5788 1707 210
306 0.075 0.06 0.03680634 0.03735188 0.947246282 5773 1701 225
307 0.075 0.07 0.03737259 0.03915507 0.711688874 5760 1700 238
308 0.075 0.08 0.03963760 0.04070067 0.850311233 5752 1696 246
309 0.075 0.09 0.04303511 0.04211747 0.879944521 5747 1690 251
310 0.075 0.10 0.04473386 0.04314786 0.759167652 5742 1687 256
311 0.075 0.11 0.04699887 0.04456466 0.618187287 5735 1683 263
312 0.075 0.12 0.04869762 0.04662545 0.684957621 5722 1680 276
313 0.075 0.13 0.04926387 0.04752705 0.743915481 5716 1679 282
314 0.075 0.14 0.04983012 0.04829985 0.780920437 5711 1678 287
315 0.075 0.15 0.05096263 0.05010304 0.899464077 5699 1676 299
316 0.075 0.16 0.05436014 0.05280783 0.786141179 5684 1670 314
317 0.075 0.17 0.05775764 0.05577022 0.722521110 5667 1664 331
318 0.075 0.18 0.05945640 0.05692942 0.643358077 5661 1661 337
319 0.075 0.19 0.06172140 0.05989181 0.755341060 5642 1657 356
320 0.075 0.20 0.06511891 0.06272540 0.677305775 5626 1651 372
321 0.075 0.21 0.06964892 0.06555899 0.462116355 5612 1643 386
322 0.075 0.22 0.07078143 0.06890778 0.764007032 5588 1641 410
323 0.075 0.23 0.07361268 0.07122617 0.695795660 5575 1636 423
324 0.075 0.24 0.07701019 0.07444616 0.677811582 5556 1630 442
325 0.075 0.25 0.07927520 0.07766615 0.812765045 5535 1626 463
326 0.075 0.26 0.08040770 0.08088614 0.972678617 5512 1624 486
327 0.075 0.27 0.08380521 0.08397733 1.000000000 5494 1618 504
328 0.075 0.28 0.09229898 0.08925811 0.643757114 5468 1603 530
329 0.075 0.29 0.09739524 0.09312210 0.511527776 5447 1594 551
330 0.075 0.30 0.10135900 0.09763009 0.578853947 5419 1587 579
331 0.075 0.31 0.10475651 0.10110768 0.593490910 5398 1581 600
332 0.075 0.32 0.10815402 0.10432767 0.579447917 5379 1575 619
333 0.075 0.33 0.11325028 0.11038125 0.693177209 5341 1566 657
334 0.075 0.34 0.11834655 0.11591963 0.748832565 5307 1557 691
335 0.075 0.35 0.12400906 0.12313241 0.931178581 5261 1547 737
336 0.075 0.36 0.12967157 0.12995878 0.999536645 5218 1537 780
337 0.075 0.37 0.13929785 0.13691396 0.770143828 5181 1520 817
338 0.075 0.38 0.14552661 0.14502834 0.976694594 5129 1509 869
339 0.075 0.39 0.15458664 0.15262751 0.823675235 5086 1493 912
340 0.075 0.40 0.16194790 0.15984029 0.811848995 5043 1480 955
341 0.075 0.41 0.17157418 0.16834106 0.706207989 4994 1463 1004
342 0.075 0.42 0.18516421 0.17851623 0.426807118 4939 1439 1059
343 0.075 0.43 0.19195923 0.18572901 0.464678096 4895 1427 1103
344 0.075 0.44 0.20101925 0.19448738 0.450354184 4843 1411 1155
345 0.075 0.45 0.21404304 0.20672334 0.406094870 4771 1388 1227
346 0.075 0.46 0.22366931 0.21612571 0.399015740 4715 1371 1283
347 0.075 0.47 0.24009060 0.22784647 0.172750319 4653 1342 1345
348 0.075 0.48 0.24971687 0.23647604 0.144840036 4603 1325 1395
349 0.075 0.49 0.25934315 0.24742401 0.197305107 4535 1308 1463
350 0.075 0.50 0.28086070 0.26146316 0.037551378 4464 1270 1534
351 0.075 0.51 0.29558324 0.27472952 0.027571688 4387 1244 1611
352 0.075 0.52 0.31030578 0.28619268 0.011708809 4324 1218 1674
353 0.075 0.53 0.32559456 0.29933024 0.006678748 4249 1191 1749
354 0.075 0.54 0.33691959 0.31259660 0.013155648 4166 1171 1832
355 0.075 0.55 0.35107588 0.32599176 0.011416188 4087 1146 1911
356 0.075 0.56 0.36353341 0.33977331 0.017791389 4002 1124 1996
357 0.075 0.57 0.37655719 0.35561566 0.039081276 3902 1101 2096
358 0.075 0.58 0.39354473 0.37107161 0.028080979 3812 1071 2186
359 0.075 0.59 0.40430351 0.38588357 0.074859803 3716 1052 2282
360 0.075 0.60 0.42412231 0.40211231 0.034124444 3625 1017 2373
361 0.075 0.61 0.43544734 0.41666667 0.072830452 3532 997 2466
362 0.075 0.62 0.45130238 0.43611540 0.150736138 3409 969 2589
363 0.075 0.63 0.47734994 0.45749614 0.060351524 3289 923 2709
364 0.075 0.64 0.49433749 0.47707367 0.104063889 3167 893 2831
365 0.075 0.65 0.50849377 0.49497682 0.205681522 3053 868 2945
366 0.075 0.66 0.52095130 0.51481195 0.575316542 2921 846 3077
367 0.075 0.67 0.53963760 0.53310149 0.549008357 2812 813 3186
368 0.075 0.68 0.56398641 0.55074704 0.213001960 2718 770 3280
369 0.075 0.69 0.58210646 0.56826378 0.190579052 2614 738 3384
370 0.075 0.70 0.60475651 0.58990211 0.156646263 2486 698 3512
371 0.075 0.71 0.62457531 0.60754766 0.101099130 2384 663 3614
372 0.075 0.72 0.64552661 0.63060278 0.146961564 2242 626 3756
373 0.075 0.73 0.66817667 0.65236991 0.119101352 2113 586 3885
374 0.075 0.74 0.68459796 0.67014426 0.149573097 2004 557 3994
375 0.075 0.75 0.70045300 0.68920659 0.257395136 1884 529 4114
376 0.075 0.76 0.72310306 0.70955693 0.162455942 1766 489 4232
377 0.075 0.77 0.74065685 0.72964967 0.248317499 1641 458 4357
378 0.075 0.78 0.76104190 0.75115920 0.288417935 1510 422 4488
379 0.075 0.79 0.77916195 0.76944874 0.284405183 1400 390 4598
380 0.075 0.80 0.79445074 0.78696548 0.400351261 1291 363 4707
381 0.075 0.81 0.80407701 0.80667182 0.779571933 1155 346 4843
382 0.075 0.82 0.82672707 0.82457496 0.814246211 1056 306 4942
383 0.075 0.83 0.84258211 0.84067491 0.831974501 959 278 5039
384 0.075 0.84 0.86013590 0.85806285 0.806283846 855 247 5143
385 0.075 0.85 0.87259343 0.87287481 1.000000000 762 225 5236
386 0.075 0.86 0.89014723 0.88794436 0.771064227 676 194 5322
387 0.075 0.87 0.90600227 0.90056672 0.410375906 606 166 5392
388 0.075 0.88 0.92015855 0.91228748 0.199662154 540 141 5458
389 0.075 0.89 0.93091733 0.92490984 0.299021531 461 122 5537
390 0.075 0.90 0.94280861 0.93495621 0.142228708 404 101 5594
391 0.075 0.91 0.95469989 0.94345698 0.023283682 359 80 5639
392 0.075 0.92 0.96092865 0.95337455 0.099175856 293 69 5705
393 0.075 0.93 0.96885617 0.96161772 0.083459899 243 55 5755
394 0.075 0.94 0.97451869 0.96831530 0.106093461 201 45 5797
395 0.075 0.95 0.98187995 0.97514168 0.047444627 161 32 5837
396 0.075 0.96 0.98584371 0.98261206 0.280804480 110 25 5888
397 0.075 0.97 0.98980747 0.98737764 0.357895772 80 18 5918
398 0.075 0.98 0.99263873 0.99149923 0.655604309 53 13 5945
399 0.075 0.99 0.99660249 0.99600721 0.812917085 25 6 5973
400 0.075 1.00 1.00000000 1.00000000 0.000000000 0 0 5998
401 0.100 0.01 0.01823417 0.02099433 0.348118259 5555 2046 125
402 0.100 0.02 0.02591171 0.02743431 0.675134692 5521 2030 159
403 0.100 0.03 0.02735125 0.02910871 0.629953619 5511 2027 169
404 0.100 0.04 0.03119002 0.03232870 0.786232104 5494 2019 186
405 0.100 0.05 0.03310940 0.03464709 0.704884485 5480 2015 200
406 0.100 0.06 0.03694818 0.03735188 0.963231465 5467 2007 213
407 0.100 0.07 0.03790787 0.03915507 0.781648778 5455 2005 225
408 0.100 0.08 0.03982726 0.04070067 0.864135774 5447 2001 233
409 0.100 0.09 0.04318618 0.04211747 0.825692322 5443 1994 237
410 0.100 0.10 0.04510557 0.04314786 0.651834839 5439 1990 241
411 0.100 0.11 0.04750480 0.04456466 0.484910901 5433 1985 247
412 0.100 0.12 0.04942418 0.04662545 0.517142017 5421 1981 259
413 0.100 0.13 0.04990403 0.04752705 0.591896667 5415 1980 265
414 0.100 0.14 0.05038388 0.04829985 0.646182330 5410 1979 270
415 0.100 0.15 0.05230326 0.05010304 0.631519272 5400 1975 280
416 0.100 0.16 0.05518234 0.05280783 0.610468390 5385 1969 295
417 0.100 0.17 0.05854127 0.05577022 0.556065748 5369 1962 311
418 0.100 0.18 0.05998081 0.05692942 0.517240959 5363 1959 317
419 0.100 0.19 0.06238004 0.05989181 0.613061182 5345 1954 335
420 0.100 0.20 0.06525912 0.06272540 0.613619714 5329 1948 351
421 0.100 0.21 0.07005758 0.06555899 0.358445676 5317 1938 363
422 0.100 0.22 0.07149712 0.06890778 0.620566243 5294 1935 386
423 0.100 0.23 0.07437620 0.07122617 0.545922893 5282 1929 398
424 0.100 0.24 0.07821497 0.07444616 0.473054220 5265 1921 415
425 0.100 0.25 0.08061420 0.07766615 0.589168703 5245 1916 435
426 0.100 0.26 0.08253359 0.08088614 0.782917405 5224 1912 456
427 0.100 0.27 0.08589251 0.08397733 0.747166290 5207 1905 473
428 0.100 0.28 0.09404990 0.08925811 0.394164158 5183 1888 497
429 0.100 0.29 0.09932821 0.09312210 0.273185721 5164 1877 516
430 0.100 0.30 0.10316699 0.09763009 0.340847567 5137 1869 543
431 0.100 0.31 0.10604607 0.10110768 0.405512453 5116 1863 564
432 0.100 0.32 0.11036468 0.10432767 0.311456774 5100 1854 580
433 0.100 0.33 0.11468330 0.11038125 0.489021671 5062 1845 618
434 0.100 0.34 0.11996161 0.11591963 0.526154371 5030 1834 650
435 0.100 0.35 0.12619962 0.12313241 0.646066054 4987 1821 693
436 0.100 0.36 0.13195777 0.12995878 0.780085681 4946 1809 734
437 0.100 0.37 0.14011516 0.13691396 0.645677446 4909 1792 771
438 0.100 0.38 0.14731286 0.14502834 0.756635863 4861 1777 819
439 0.100 0.39 0.15642994 0.15262751 0.597004835 4821 1758 859
440 0.100 0.40 0.16314779 0.15984029 0.655037099 4779 1744 901
441 0.100 0.41 0.17274472 0.16834106 0.552560912 4733 1724 947
442 0.100 0.42 0.18570058 0.17851623 0.333111194 4681 1697 999
443 0.100 0.43 0.19193858 0.18572901 0.412616270 4638 1684 1042
444 0.100 0.44 0.20009597 0.19448738 0.469103005 4587 1667 1093
445 0.100 0.45 0.21257198 0.20672334 0.459773374 4518 1641 1162
446 0.100 0.46 0.22168906 0.21612571 0.490011016 4464 1622 1216
447 0.100 0.47 0.23656430 0.22784647 0.280685652 4404 1591 1276
448 0.100 0.48 0.24568138 0.23647604 0.260116604 4356 1572 1324
449 0.100 0.49 0.25383877 0.24742401 0.445020590 4288 1555 1392
450 0.100 0.50 0.27351248 0.26146316 0.151473893 4220 1514 1460
451 0.100 0.51 0.28646833 0.27472952 0.169162993 4144 1487 1536
452 0.100 0.52 0.30182342 0.28619268 0.069150369 4087 1455 1593
453 0.100 0.53 0.31573896 0.29933024 0.059516689 4014 1426 1666
454 0.100 0.54 0.32869482 0.31259660 0.067865968 3938 1399 1742
455 0.100 0.55 0.34357006 0.32599176 0.048359602 3865 1368 1815
456 0.100 0.56 0.35700576 0.33977331 0.055511990 3786 1340 1894
457 0.100 0.57 0.37044146 0.35561566 0.103896756 3691 1312 1989
458 0.100 0.58 0.38867562 0.37107161 0.055059863 3609 1274 2071
459 0.100 0.59 0.39827255 0.38588357 0.182857608 3514 1254 2166
460 0.100 0.60 0.41746641 0.40211231 0.099928818 3428 1214 2252
461 0.100 0.61 0.42850288 0.41666667 0.209331922 3338 1191 2342
462 0.100 0.62 0.44529750 0.43611540 0.335838389 3222 1156 2458
463 0.100 0.63 0.47072937 0.45749614 0.163919684 3109 1103 2571
464 0.100 0.64 0.48656430 0.47707367 0.322905015 2990 1070 2690
465 0.100 0.65 0.50383877 0.49497682 0.357361110 2887 1034 2793
466 0.100 0.66 0.51919386 0.51481195 0.658250483 2765 1002 2915
467 0.100 0.67 0.53742802 0.53310149 0.661978101 2661 964 3019
468 0.100 0.68 0.56046065 0.55074704 0.309382702 2572 916 3108
469 0.100 0.69 0.57821497 0.56826378 0.295363760 2473 879 3207
470 0.100 0.70 0.60028791 0.58990211 0.270912341 2351 833 3329
471 0.100 0.71 0.61948177 0.60754766 0.201173701 2254 793 3426
472 0.100 0.72 0.64059501 0.63060278 0.280833060 2119 749 3561
473 0.100 0.73 0.66410749 0.65236991 0.197534327 1999 700 3681
474 0.100 0.74 0.68042226 0.67014426 0.254486066 1895 666 3785
475 0.100 0.75 0.69721689 0.68920659 0.370207927 1782 631 3898
476 0.100 0.76 0.71976967 0.70955693 0.240998002 1671 584 4009
477 0.100 0.77 0.73800384 0.72964967 0.329515053 1553 546 4127
478 0.100 0.78 0.75767754 0.75115920 0.438297926 1427 505 4253
479 0.100 0.79 0.77591171 0.76944874 0.430356254 1323 467 4357
480 0.100 0.80 0.79366603 0.78696548 0.399704543 1224 430 4456
481 0.100 0.81 0.80566219 0.80667182 0.917147737 1096 405 4584
482 0.100 0.82 0.82629559 0.82457496 0.835393215 1000 362 4680
483 0.100 0.83 0.84309021 0.84067491 0.751056996 910 327 4770
484 0.100 0.84 0.86180422 0.85806285 0.592306093 814 288 4866
485 0.100 0.85 0.87523992 0.87287481 0.733475913 727 260 4953
486 0.100 0.86 0.89155470 0.88794436 0.568484522 644 226 5036
487 0.100 0.87 0.90738964 0.90056672 0.240341998 579 193 5101
488 0.100 0.88 0.92082534 0.91228748 0.117433963 516 165 5164
489 0.100 0.89 0.93042226 0.92490984 0.285607385 438 145 5242
490 0.100 0.90 0.94193858 0.93495621 0.144490012 384 121 5296
491 0.100 0.91 0.95393474 0.94345698 0.017991934 343 96 5337
492 0.100 0.92 0.96113244 0.95337455 0.057020149 281 81 5399
493 0.100 0.93 0.96880998 0.96161772 0.053428493 233 65 5447
494 0.100 0.94 0.97408829 0.96831530 0.091801307 192 54 5488
495 0.100 0.95 0.98080614 0.97514168 0.062947583 153 40 5527
496 0.100 0.96 0.98656430 0.98261206 0.129564763 107 28 5573
497 0.100 0.97 0.99088292 0.98737764 0.118495866 79 19 5601
498 0.100 0.98 0.99328215 0.99149923 0.369702010 52 14 5628
499 0.100 0.99 0.99712092 0.99600721 0.459588261 25 6 5655
500 0.100 1.00 1.00000000 1.00000000 0.000000000 0 0 5680
Dboth
1 11
2 13
3 15
4 17
5 18
6 21
7 22
8 25
9 28
10 28
11 30
12 30
13 31
14 31
15 32
16 35
17 37
18 37
19 37
20 38
21 39
22 40
23 42
24 45
25 46
26 46
27 46
28 51
29 54
30 55
31 56
32 58
33 60
34 61
35 65
36 68
37 75
38 77
39 81
40 86
41 90
42 98
43 101
44 105
45 111
46 114
47 122
48 127
49 134
50 142
51 150
52 156
53 163
54 168
55 174
56 182
57 191
58 202
59 206
60 216
61 224
62 230
63 243
64 249
65 255
66 262
67 271
68 282
69 289
70 303
71 315
72 320
73 328
74 332
75 340
76 355
77 359
78 372
79 381
80 388
81 394
82 400
83 410
84 418
85 421
86 431
87 439
88 444
89 445
90 450
91 451
92 454
93 458
94 460
95 465
96 467
97 467
98 467
99 469
100 470
101 16
102 22
103 25
104 28
105 30
106 35
107 36
108 39
109 43
110 43
111 46
112 47
113 48
114 48
115 49
116 52
117 54
118 56
119 57
120 62
121 67
122 69
123 73
124 76
125 77
126 78
127 80
128 87
129 92
130 96
131 99
132 103
133 108
134 114
135 119
136 122
137 132
138 139
139 150
140 157
141 165
142 178
143 184
144 190
145 200
146 206
147 221
148 230
149 242
150 259
151 277
152 289
153 305
154 316
155 326
156 341
157 358
158 375
159 383
160 403
161 416
162 433
163 450
164 462
165 477
166 491
167 503
168 524
169 540
170 562
171 585
172 599
173 620
174 631
175 648
176 672
177 683
178 704
179 722
180 735
181 742
182 767
183 780
184 800
185 813
186 833
187 849
188 860
189 867
190 876
191 885
192 891
193 897
194 902
195 909
196 914
197 916
198 917
199 920
200 923
201 27
202 36
203 39
204 43
205 46
206 51
207 52
208 56
209 60
210 62
211 66
212 69
213 70
214 70
215 71
216 76
217 81
218 84
219 85
220 91
221 97
222 99
223 103
224 107
225 111
226 112
227 116
228 129
229 137
230 142
231 147
232 153
233 161
234 169
235 176
236 184
237 197
238 205
239 219
240 229
241 244
242 263
243 272
244 284
245 300
246 313
247 335
248 349
249 363
250 393
251 414
252 435
253 455
254 467
255 488
256 508
257 527
258 552
259 567
260 597
261 616
262 640
263 670
264 692
265 713
266 732
267 754
268 791
269 815
270 848
271 879
272 904
273 939
274 961
275 986
276 1022
277 1044
278 1069
279 1091
280 1114
281 1126
282 1158
283 1181
284 1208
285 1226
286 1251
287 1275
288 1291
289 1306
290 1321
291 1338
292 1347
293 1359
294 1367
295 1377
296 1383
297 1387
298 1391
299 1396
300 1401
301 34
302 46
303 49
304 55
305 59
306 65
307 66
308 70
309 76
310 79
311 83
312 86
313 87
314 88
315 90
316 96
317 102
318 105
319 109
320 115
321 123
322 125
323 130
324 136
325 140
326 142
327 148
328 163
329 172
330 179
331 185
332 191
333 200
334 209
335 219
336 229
337 246
338 257
339 273
340 286
341 303
342 327
343 339
344 355
345 378
346 395
347 424
348 441
349 458
350 496
351 522
352 548
353 575
354 595
355 620
356 642
357 665
358 695
359 714
360 749
361 769
362 797
363 843
364 873
365 898
366 920
367 953
368 996
369 1028
370 1068
371 1103
372 1140
373 1180
374 1209
375 1237
376 1277
377 1308
378 1344
379 1376
380 1403
381 1420
382 1460
383 1488
384 1519
385 1541
386 1572
387 1600
388 1625
389 1644
390 1665
391 1686
392 1697
393 1711
394 1721
395 1734
396 1741
397 1748
398 1753
399 1760
400 1766
401 38
402 54
403 57
404 65
405 69
406 77
407 79
408 83
409 90
410 94
411 99
412 103
413 104
414 105
415 109
416 115
417 122
418 125
419 130
420 136
421 146
422 149
423 155
424 163
425 168
426 172
427 179
428 196
429 207
430 215
431 221
432 230
433 239
434 250
435 263
436 275
437 292
438 307
439 326
440 340
441 360
442 387
443 400
444 417
445 443
446 462
447 493
448 512
449 529
450 570
451 597
452 629
453 658
454 685
455 716
456 744
457 772
458 810
459 830
460 870
461 893
462 928
463 981
464 1014
465 1050
466 1082
467 1120
468 1168
469 1205
470 1251
471 1291
472 1335
473 1384
474 1418
475 1453
476 1500
477 1538
478 1579
479 1617
480 1654
481 1679
482 1722
483 1757
484 1796
485 1824
486 1858
487 1891
488 1919
489 1939
490 1963
491 1988
492 2003
493 2019
494 2030
495 2044
496 2056
497 2065
498 2070
499 2078
500 2084enrichment.plotter(gene.hic.filt, "median_FDR.C", "adj.P.Val", "Median FDR of Hi-C Contacts Overlapping Gene, Chimp")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.02407002 0.02082530 0.740000502 7171 446 151
2 0.010 0.02 0.02625821 0.02686721 1.000000000 7125 445 197
3 0.010 0.03 0.03063457 0.02879547 0.921802892 7112 443 210
4 0.010 0.04 0.03282276 0.03226636 1.000000000 7086 442 236
5 0.010 0.05 0.03501094 0.03432318 1.000000000 7071 441 251
6 0.010 0.06 0.03719912 0.03689420 1.000000000 7052 440 270
7 0.010 0.07 0.03938731 0.03817971 0.989587066 7043 439 279
8 0.010 0.08 0.04376368 0.03959378 0.728174656 7034 437 288
9 0.010 0.09 0.04814004 0.04062219 0.473375066 7028 435 294
10 0.010 0.10 0.04814004 0.04139350 0.531804704 7022 435 300
11 0.010 0.11 0.05251641 0.04280756 0.348371342 7013 433 309
12 0.010 0.12 0.05251641 0.04486438 0.485143162 6997 433 325
13 0.010 0.13 0.05251641 0.04537858 0.522278372 6993 433 329
14 0.010 0.14 0.05251641 0.04679265 0.629075382 6982 433 340
15 0.010 0.15 0.05470460 0.04859236 0.607072083 6969 432 353
16 0.010 0.16 0.05908096 0.05077773 0.469342894 6954 430 368
17 0.010 0.17 0.06345733 0.05360586 0.391601740 6934 428 388
18 0.010 0.18 0.06345733 0.05489137 0.469779205 6924 428 398
19 0.010 0.19 0.06564551 0.05810515 0.543743114 6900 427 422
20 0.010 0.20 0.06564551 0.06016197 0.684191681 6884 427 438
21 0.010 0.21 0.07002188 0.06260445 0.565183912 6867 425 455
22 0.010 0.22 0.07002188 0.06556113 0.764395742 6844 425 478
23 0.010 0.23 0.07221007 0.06800360 0.785306384 6826 424 496
24 0.010 0.24 0.07877462 0.07108883 0.571926775 6805 421 517
25 0.010 0.25 0.08315098 0.07391696 0.493006856 6785 419 537
26 0.010 0.26 0.08315098 0.07751639 0.708300929 6757 419 565
27 0.010 0.27 0.08533917 0.08034452 0.751862070 6736 418 586
28 0.010 0.28 0.09628009 0.08432961 0.389316878 6710 413 612
29 0.010 0.29 0.10284464 0.08780049 0.277414887 6686 410 636
30 0.010 0.30 0.10284464 0.09307109 0.510364986 6645 410 677
31 0.010 0.31 0.10503282 0.09615632 0.560782547 6622 409 700
32 0.010 0.32 0.10722101 0.10091271 0.702881487 6586 408 736
33 0.010 0.33 0.10940919 0.10618331 0.878813027 6546 407 776
34 0.010 0.34 0.11159737 0.11158247 1.000000000 6505 406 817
35 0.010 0.35 0.11816193 0.11801003 1.000000000 6458 403 864
36 0.010 0.36 0.12035011 0.12379483 0.875035053 6414 402 908
37 0.010 0.37 0.13129103 0.13086515 1.000000000 6364 397 958
38 0.010 0.38 0.13785558 0.13934953 0.979702663 6301 394 1021
39 0.010 0.39 0.14660832 0.14680550 1.000000000 6247 390 1075
40 0.010 0.40 0.15536105 0.15323306 0.949571737 6201 386 1121
41 0.010 0.41 0.16630197 0.16171744 0.834536687 6140 381 1182
42 0.010 0.42 0.18380744 0.17084458 0.487148241 6077 373 1245
43 0.010 0.43 0.18599562 0.17714359 0.654340184 6029 372 1293
44 0.010 0.44 0.19256018 0.18549942 0.735185510 5967 369 1355
45 0.010 0.45 0.20568928 0.19732613 0.687346692 5881 363 1441
46 0.010 0.46 0.21006565 0.20606762 0.874302361 5815 361 1507
47 0.010 0.47 0.22100656 0.21827998 0.930607262 5725 356 1597
48 0.010 0.48 0.23632385 0.22663581 0.651041771 5667 349 1655
49 0.010 0.49 0.25164114 0.23679136 0.475852628 5595 342 1727
50 0.010 0.50 0.26477024 0.25118910 0.525805204 5489 336 1833
51 0.010 0.51 0.28227571 0.26442988 0.402622477 5394 328 1928
52 0.010 0.52 0.29759300 0.27612804 0.315384371 5310 321 2012
53 0.010 0.53 0.30853392 0.28936881 0.379894264 5212 316 2110
54 0.010 0.54 0.32166302 0.30273814 0.392466148 5114 310 2208
55 0.010 0.55 0.32822757 0.31572182 0.588517719 5016 307 2306
56 0.010 0.56 0.33698031 0.32767708 0.699955001 4927 303 2395
57 0.010 0.57 0.35667396 0.34323178 0.566599573 4815 294 2507
58 0.010 0.58 0.37855580 0.35801517 0.371435554 4710 284 2612
59 0.010 0.59 0.38949672 0.37189870 0.451793486 4607 279 2715
60 0.010 0.60 0.41794311 0.38925312 0.212359048 4485 266 2837
61 0.010 0.61 0.42888403 0.40532202 0.313269156 4365 261 2957
62 0.010 0.62 0.44638950 0.42383340 0.338576271 4229 253 3093
63 0.010 0.63 0.47483589 0.44453015 0.195206499 4081 240 3241
64 0.010 0.64 0.49234136 0.46227021 0.200317833 3951 232 3371
65 0.010 0.65 0.51203501 0.47923898 0.162027506 3828 223 3494
66 0.010 0.66 0.52954048 0.49903587 0.194940856 3682 215 3640
67 0.010 0.67 0.55798687 0.51831855 0.088928478 3545 202 3777
68 0.010 0.68 0.57768053 0.53708703 0.080895211 3408 193 3914
69 0.010 0.69 0.60175055 0.55636971 0.049506477 3269 182 4053
70 0.010 0.70 0.63238512 0.57950893 0.020810427 3103 168 4219
71 0.010 0.71 0.65864333 0.59802031 0.007466153 2971 156 4351
72 0.010 0.72 0.67177243 0.62077388 0.023429435 2800 150 4522
73 0.010 0.73 0.69146608 0.64237048 0.027332457 2641 141 4681
74 0.010 0.74 0.69803063 0.65959635 0.082496181 2510 138 4812
75 0.010 0.75 0.71115974 0.67900758 0.142684344 2365 132 4957
76 0.010 0.76 0.74179431 0.69983288 0.049445580 2217 118 5105
77 0.010 0.77 0.75273523 0.72142949 0.137554174 2054 113 5268
78 0.010 0.78 0.78774617 0.74302610 0.027813680 1902 97 5420
79 0.010 0.79 0.80087527 0.76179458 0.049425600 1762 91 5560
80 0.010 0.80 0.81619256 0.77902044 0.055364007 1635 84 5687
81 0.010 0.81 0.82275711 0.79778892 0.190283215 1492 81 5830
82 0.010 0.82 0.83369803 0.81578609 0.339107493 1357 76 5965
83 0.010 0.83 0.85120350 0.83262630 0.302099516 1234 68 6088
84 0.010 0.84 0.87308534 0.84869521 0.152009817 1119 58 6203
85 0.010 0.85 0.88183807 0.86617817 0.345835289 987 54 6335
86 0.010 0.86 0.90371991 0.88301838 0.178865317 866 44 6456
87 0.010 0.87 0.92122538 0.89613061 0.083024256 772 36 6550
88 0.010 0.88 0.92341357 0.90821442 0.281721047 679 35 6643
89 0.010 0.89 0.93216630 0.92132665 0.425096043 581 31 6741
90 0.010 0.90 0.94310722 0.93148220 0.358352917 507 26 6815
91 0.010 0.91 0.94529540 0.94073788 0.746534116 436 25 6886
92 0.010 0.92 0.95842451 0.95192184 0.577476414 355 19 6967
93 0.010 0.93 0.96498906 0.96040622 0.693422120 292 16 7030
94 0.010 0.94 0.96498906 0.96747654 0.862584837 237 16 7085
95 0.010 0.95 0.98030635 0.97544672 0.591864006 182 9 7140
96 0.010 0.96 0.98687090 0.98225993 0.557173711 132 6 7190
97 0.010 0.97 0.98687090 0.98753053 1.000000000 91 6 7231
98 0.010 0.98 0.98687090 0.99138707 0.414491736 61 6 7261
99 0.010 0.99 0.99781182 0.99601491 0.805832349 30 1 7292
100 0.010 1.00 1.00000000 1.00000000 0.000000000 0 0 7322
101 0.025 0.01 0.01847826 0.02082530 0.683294327 6714 903 145
102 0.025 0.02 0.02500000 0.02686721 0.791440987 6673 897 186
103 0.025 0.03 0.02826087 0.02879547 1.000000000 6661 894 198
104 0.025 0.04 0.03043478 0.03226636 0.813850028 6636 892 223
105 0.025 0.05 0.03260870 0.03432318 0.835411830 6622 890 237
106 0.025 0.06 0.03695652 0.03689420 1.000000000 6606 886 253
107 0.025 0.07 0.03913043 0.03817971 0.945271488 6598 884 261
108 0.025 0.08 0.04130435 0.03959378 0.846703992 6589 882 270
109 0.025 0.09 0.04347826 0.04062219 0.705135216 6583 880 276
110 0.025 0.10 0.04347826 0.04139350 0.802639536 6577 880 282
111 0.025 0.11 0.04782609 0.04280756 0.475160888 6570 876 289
112 0.025 0.12 0.04891304 0.04486438 0.584408251 6555 875 304
113 0.025 0.13 0.04891304 0.04537858 0.642509832 6551 875 308
114 0.025 0.14 0.04891304 0.04679265 0.809410956 6540 875 319
115 0.025 0.15 0.05000000 0.04859236 0.896706602 6527 874 332
116 0.025 0.16 0.05217391 0.05077773 0.900160377 6512 872 347
117 0.025 0.17 0.05652174 0.05360586 0.733684301 6494 868 365
118 0.025 0.18 0.05869565 0.05489137 0.643764894 6486 866 373
119 0.025 0.19 0.06195652 0.05810515 0.647858934 6464 863 395
120 0.025 0.20 0.06521739 0.06016197 0.539931824 6451 860 408
121 0.025 0.21 0.06956522 0.06260445 0.392172922 6436 856 423
122 0.025 0.22 0.07065217 0.06556113 0.552859956 6414 855 445
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141 0.025 0.41 0.16956522 0.16171744 0.521645409 5757 764 1102
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144 0.025 0.44 0.19239130 0.18549942 0.597803787 5593 743 1266
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147 0.025 0.47 0.22717391 0.21827998 0.513766423 5370 711 1489
148 0.025 0.48 0.23804348 0.22663581 0.401898206 5315 701 1544
149 0.025 0.49 0.25434783 0.23679136 0.196111089 5251 686 1608
150 0.025 0.50 0.27173913 0.25118910 0.136201745 5155 670 1704
151 0.025 0.51 0.28913043 0.26442988 0.076843090 5068 654 1791
152 0.025 0.52 0.30652174 0.27612804 0.031030162 4993 638 1866
153 0.025 0.53 0.31739130 0.28936881 0.050301017 4900 628 1959
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158 0.025 0.58 0.39021739 0.35801517 0.032918632 4433 561 2426
159 0.025 0.59 0.40000000 0.37189870 0.065504029 4334 552 2525
160 0.025 0.60 0.42173913 0.38925312 0.034331370 4219 532 2640
161 0.025 0.61 0.43695652 0.40532202 0.040796362 4108 518 2751
162 0.025 0.62 0.45543478 0.42383340 0.042342328 3981 501 2878
163 0.025 0.63 0.47608696 0.44453015 0.043798783 3839 482 3020
164 0.025 0.64 0.49130435 0.46227021 0.064913111 3715 468 3144
165 0.025 0.65 0.50978261 0.47923898 0.052405836 3600 451 3259
166 0.025 0.66 0.52500000 0.49903587 0.100535319 3460 437 3399
167 0.025 0.67 0.54565217 0.51831855 0.083291848 3329 418 3530
168 0.025 0.68 0.56847826 0.53708703 0.045676048 3204 397 3655
169 0.025 0.69 0.59021739 0.55636971 0.030359381 3074 377 3785
170 0.025 0.70 0.61304348 0.57950893 0.030865347 2915 356 3944
171 0.025 0.71 0.63804348 0.59802031 0.009295449 2794 333 4065
172 0.025 0.72 0.65000000 0.62077388 0.056192483 2628 322 4231
173 0.025 0.73 0.67826087 0.64237048 0.017212336 2486 296 4373
174 0.025 0.74 0.68804348 0.65959635 0.057148434 2361 287 4498
175 0.025 0.75 0.70217391 0.67900758 0.117520662 2223 274 4636
176 0.025 0.76 0.72826087 0.69983288 0.049389577 2085 250 4774
177 0.025 0.77 0.74456522 0.72142949 0.103551946 1932 235 4927
178 0.025 0.78 0.77065217 0.74302610 0.045282187 1788 211 5071
179 0.025 0.79 0.78913043 0.76179458 0.042192390 1659 194 5200
180 0.025 0.80 0.80108696 0.77902044 0.093810404 1536 183 5323
181 0.025 0.81 0.80869565 0.79778892 0.404595792 1397 176 5462
182 0.025 0.82 0.82717391 0.81578609 0.366204115 1274 159 5585
183 0.025 0.83 0.84130435 0.83262630 0.481514981 1156 146 5703
184 0.025 0.84 0.85978261 0.84869521 0.341887842 1048 129 5811
185 0.025 0.85 0.87391304 0.86617817 0.495053280 925 116 5934
186 0.025 0.86 0.89565217 0.88301838 0.224320237 814 96 6045
187 0.025 0.87 0.90978261 0.89613061 0.165175759 725 83 6134
188 0.025 0.88 0.91630435 0.90821442 0.398513009 637 77 6222
189 0.025 0.89 0.92826087 0.92132665 0.443227755 546 66 6313
190 0.025 0.90 0.93804348 0.93148220 0.441632462 476 57 6383
191 0.025 0.91 0.94782609 0.94073788 0.370598868 413 48 6446
192 0.025 0.92 0.95978261 0.95192184 0.269227061 337 37 6522
193 0.025 0.93 0.96521739 0.96040622 0.479608953 276 32 6583
194 0.025 0.94 0.96739130 0.96747654 1.000000000 223 30 6636
195 0.025 0.95 0.97826087 0.97544672 0.635542235 171 20 6688
196 0.025 0.96 0.98478261 0.98225993 0.628165295 124 14 6735
197 0.025 0.97 0.98804348 0.98753053 1.000000000 86 11 6773
198 0.025 0.98 0.98913043 0.99138707 0.549266675 57 10 6802
199 0.025 0.99 0.99565217 0.99601491 1.000000000 27 4 6832
200 0.025 1.00 1.00000000 1.00000000 0.000000000 0 0 6859
201 0.050 0.01 0.02004295 0.02082530 0.902383225 6248 1369 134
202 0.050 0.02 0.02648533 0.02686721 0.995118995 6210 1360 172
203 0.050 0.03 0.02863278 0.02879547 1.000000000 6198 1357 184
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207 0.050 0.07 0.03865426 0.03817971 0.979962636 6139 1343 243
208 0.050 0.08 0.04151754 0.03959378 0.740377927 6132 1339 250
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216 0.050 0.16 0.05225483 0.05077773 0.833386489 6060 1324 322
217 0.050 0.17 0.05654975 0.05360586 0.635666460 6044 1318 338
218 0.050 0.18 0.05869721 0.05489137 0.532191590 6037 1315 345
219 0.050 0.19 0.06227631 0.05810515 0.501191342 6017 1310 365
220 0.050 0.20 0.06442377 0.06016197 0.498107176 6004 1307 378
221 0.050 0.21 0.06800286 0.06260445 0.390560292 5990 1302 392
222 0.050 0.22 0.06871868 0.06556113 0.640677227 5968 1301 414
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231 0.050 0.31 0.09878311 0.09615632 0.750801207 5772 1259 610
232 0.050 0.32 0.10522548 0.10091271 0.587956383 5744 1250 638
233 0.050 0.33 0.11166786 0.10618331 0.492277035 5712 1241 670
234 0.050 0.34 0.11596278 0.11158247 0.598067169 5676 1235 706
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237 0.050 0.37 0.13171081 0.13086515 0.952411163 5548 1213 834
238 0.050 0.38 0.13743737 0.13934953 0.853073214 5490 1205 892
239 0.050 0.39 0.14889048 0.14680550 0.840408866 5448 1189 934
240 0.050 0.40 0.15819613 0.15323306 0.597807717 5411 1176 971
241 0.050 0.41 0.16964925 0.16171744 0.395968767 5361 1160 1021
242 0.050 0.42 0.18396564 0.17084458 0.161713259 5310 1140 1072
243 0.050 0.43 0.18682892 0.17714359 0.313390030 5265 1136 1117
244 0.050 0.44 0.19470293 0.18549942 0.347701229 5211 1125 1171
245 0.050 0.45 0.20687187 0.19732613 0.340768413 5136 1108 1246
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248 0.050 0.48 0.24123121 0.22663581 0.160519537 4956 1060 1426
249 0.050 0.49 0.25483178 0.23679136 0.086087006 4896 1041 1486
250 0.050 0.50 0.27487473 0.25118910 0.026448416 4812 1013 1570
251 0.050 0.51 0.28632785 0.26442988 0.043862731 4725 997 1657
252 0.050 0.52 0.30279170 0.27612804 0.015182915 4657 974 1725
253 0.050 0.53 0.31496063 0.28936881 0.021661888 4571 957 1811
254 0.050 0.54 0.32856120 0.30273814 0.022186522 4486 938 1896
255 0.050 0.55 0.34144596 0.31572182 0.024322076 4403 920 1979
256 0.050 0.56 0.35504653 0.32767708 0.017560545 4329 901 2053
257 0.050 0.57 0.36936292 0.34323178 0.025089999 4228 881 2154
258 0.050 0.58 0.38296349 0.35801517 0.034295612 4132 862 2250
259 0.050 0.59 0.39370079 0.37189870 0.067115943 4039 847 2343
260 0.050 0.60 0.41517538 0.38925312 0.030497829 3934 817 2448
261 0.050 0.61 0.42949177 0.40532202 0.045349462 3829 797 2553
262 0.050 0.62 0.44881890 0.42383340 0.039732857 3712 770 2670
263 0.050 0.63 0.47029349 0.44453015 0.034881401 3581 740 2801
264 0.050 0.64 0.48532570 0.46227021 0.060301024 3464 719 2918
265 0.050 0.65 0.49964209 0.47923898 0.097770838 3352 699 3030
266 0.050 0.66 0.51395848 0.49903587 0.229353049 3218 679 3164
267 0.050 0.67 0.53758053 0.51831855 0.118476463 3101 646 3281
268 0.050 0.68 0.56191840 0.53708703 0.042829080 2989 612 3393
269 0.050 0.69 0.58124553 0.55636971 0.041705695 2866 585 3516
270 0.050 0.70 0.60558339 0.57950893 0.031575793 2720 551 3662
271 0.050 0.71 0.62848962 0.59802031 0.011268238 2608 519 3774
272 0.050 0.72 0.64352183 0.62077388 0.056879437 2452 498 3930
273 0.050 0.73 0.66929134 0.64237048 0.022201274 2320 462 4062
274 0.050 0.74 0.68360773 0.65959635 0.039410723 2206 442 4176
275 0.050 0.75 0.69720830 0.67900758 0.114771690 2074 423 4308
276 0.050 0.76 0.72083035 0.69983288 0.063134006 1945 390 4437
277 0.050 0.77 0.74015748 0.72142949 0.090848230 1804 363 4578
278 0.050 0.78 0.76163207 0.74302610 0.084840723 1666 333 4716
279 0.050 0.79 0.77881174 0.76179458 0.106576637 1544 309 4838
280 0.050 0.80 0.79455977 0.77902044 0.131072969 1432 287 4950
281 0.050 0.81 0.80243379 0.79778892 0.659620158 1297 276 5085
282 0.050 0.82 0.82176092 0.81578609 0.549905114 1184 249 5198
283 0.050 0.83 0.83965641 0.83262630 0.460797967 1078 224 5304
284 0.050 0.84 0.85540444 0.84869521 0.464546456 975 202 5407
285 0.050 0.85 0.87186829 0.86617817 0.518097163 862 179 5520
286 0.050 0.86 0.89047960 0.88301838 0.361764602 757 153 5625
287 0.050 0.87 0.90551181 0.89613061 0.222297192 676 132 5706
288 0.050 0.88 0.91410165 0.90821442 0.429374395 594 120 5788
289 0.050 0.89 0.92555476 0.92132665 0.553053247 508 104 5874
290 0.050 0.90 0.93557623 0.93148220 0.541689881 443 90 5939
291 0.050 0.91 0.94559771 0.94073788 0.431407667 385 76 5997
292 0.050 0.92 0.95633500 0.95192184 0.434089359 313 61 6069
293 0.050 0.93 0.96420902 0.96040622 0.466013710 258 50 6124
294 0.050 0.94 0.96921976 0.96747654 0.747254132 210 43 6172
295 0.050 0.95 0.97924123 0.97544672 0.359490585 162 29 6220
296 0.050 0.96 0.98496779 0.98225993 0.462585101 117 21 6265
297 0.050 0.97 0.98783107 0.98753053 1.000000000 80 17 6302
298 0.050 0.98 0.99069435 0.99138707 0.881146735 54 13 6328
299 0.050 0.99 0.99570508 0.99601491 1.000000000 25 6 6357
300 0.050 1.00 1.00000000 1.00000000 0.000000000 0 0 6382
301 0.075 0.01 0.01977401 0.02082530 0.796628543 5882 1735 127
302 0.075 0.02 0.02598870 0.02686721 0.859943816 5846 1724 163
303 0.075 0.03 0.02768362 0.02879547 0.812346794 5834 1721 175
304 0.075 0.04 0.03107345 0.03226636 0.805197555 5813 1715 196
305 0.075 0.05 0.03333333 0.03432318 0.852456701 5801 1711 208
306 0.075 0.06 0.03615819 0.03689420 0.908311928 5786 1706 223
307 0.075 0.07 0.03728814 0.03817971 0.879069770 5778 1704 231
308 0.075 0.08 0.04011299 0.03959378 0.953659993 5772 1699 237
309 0.075 0.09 0.04293785 0.04062219 0.622012841 5769 1694 240
310 0.075 0.10 0.04406780 0.04139350 0.565451557 5765 1692 244
311 0.075 0.11 0.04632768 0.04280756 0.443899609 5758 1688 251
312 0.075 0.12 0.04802260 0.04486438 0.506059096 5745 1685 264
313 0.075 0.13 0.04802260 0.04537858 0.587043768 5741 1685 268
314 0.075 0.14 0.04915254 0.04679265 0.637744384 5732 1683 277
315 0.075 0.15 0.04971751 0.04859236 0.851189620 5719 1682 290
316 0.075 0.16 0.05197740 0.05077773 0.841498037 5706 1678 303
317 0.075 0.17 0.05536723 0.05360586 0.753295983 5690 1672 319
318 0.075 0.18 0.05762712 0.05489137 0.606146321 5684 1668 325
319 0.075 0.19 0.06214689 0.05810515 0.441773513 5667 1660 342
320 0.075 0.20 0.06384181 0.06016197 0.494031118 5654 1657 355
321 0.075 0.21 0.06723164 0.06260445 0.390612787 5641 1651 368
322 0.075 0.22 0.06779661 0.06556113 0.705651128 5619 1650 390
323 0.075 0.23 0.07005650 0.06800360 0.736398267 5604 1646 405
324 0.075 0.24 0.07401130 0.07108883 0.622882734 5587 1639 422
325 0.075 0.25 0.07627119 0.07391696 0.704657125 5569 1635 440
326 0.075 0.26 0.07796610 0.07751639 0.976119186 5544 1632 465
327 0.075 0.27 0.08135593 0.08034452 0.897861218 5528 1626 481
328 0.075 0.28 0.08757062 0.08432961 0.610303446 5508 1615 501
329 0.075 0.29 0.09096045 0.08780049 0.626467260 5487 1609 522
330 0.075 0.30 0.09548023 0.09307109 0.726047256 5454 1601 555
331 0.075 0.31 0.09774011 0.09615632 0.832655947 5434 1597 575
332 0.075 0.32 0.10282486 0.10091271 0.795648784 5406 1588 603
333 0.075 0.33 0.10790960 0.10618331 0.822494059 5374 1579 635
334 0.075 0.34 0.11186441 0.11158247 1.000000000 5339 1572 670
335 0.075 0.35 0.11694915 0.11801003 0.908055016 5298 1563 711
336 0.075 0.36 0.11977401 0.12379483 0.586895901 5258 1558 751
337 0.075 0.37 0.12881356 0.13086515 0.801737412 5219 1542 790
338 0.075 0.38 0.13446328 0.13934953 0.524549616 5163 1532 846
339 0.075 0.39 0.14406780 0.14680550 0.739828948 5122 1515 887
340 0.075 0.40 0.15310734 0.15323306 1.000000000 5088 1499 921
341 0.075 0.41 0.16327684 0.16171744 0.868149560 5040 1481 969
342 0.075 0.42 0.17740113 0.17084458 0.424897371 4994 1456 1015
343 0.075 0.43 0.18135593 0.17714359 0.622211279 4952 1449 1057
344 0.075 0.44 0.18983051 0.18549942 0.618074835 4902 1434 1107
345 0.075 0.45 0.20282486 0.19732613 0.530398516 4833 1411 1176
346 0.075 0.46 0.21412429 0.20606762 0.357552717 4785 1391 1224
347 0.075 0.47 0.22937853 0.21827998 0.210066191 4717 1364 1292
348 0.075 0.48 0.24124294 0.22663581 0.101452487 4673 1343 1336
349 0.075 0.49 0.25310734 0.23679136 0.071013620 4615 1322 1394
350 0.075 0.50 0.27344633 0.25118910 0.015291205 4539 1286 1470
351 0.075 0.51 0.28418079 0.26442988 0.034595961 4455 1267 1554
352 0.075 0.52 0.30056497 0.27612804 0.009704679 4393 1238 1616
353 0.075 0.53 0.31525424 0.28936881 0.006878989 4316 1212 1693
354 0.075 0.54 0.32994350 0.30273814 0.005031207 4238 1186 1771
355 0.075 0.55 0.34237288 0.31572182 0.006615614 4159 1164 1850
356 0.075 0.56 0.35536723 0.32767708 0.005187319 4089 1141 1920
357 0.075 0.57 0.36836158 0.34323178 0.012241266 3991 1118 2018
358 0.075 0.58 0.38135593 0.35801517 0.021318480 3899 1095 2110
359 0.075 0.59 0.39265537 0.37189870 0.042579886 3811 1075 2198
360 0.075 0.60 0.41355932 0.38925312 0.018347593 3713 1038 2296
361 0.075 0.61 0.42542373 0.40532202 0.053312368 3609 1017 2400
362 0.075 0.62 0.44406780 0.42383340 0.053275421 3498 984 2511
363 0.075 0.63 0.46779661 0.44453015 0.026823813 3379 942 2630
364 0.075 0.64 0.48361582 0.46227021 0.043148010 3269 914 2740
365 0.075 0.65 0.49717514 0.47923898 0.090729955 3161 890 2848
366 0.075 0.66 0.51242938 0.49903587 0.209404071 3034 863 2975
367 0.075 0.67 0.53502825 0.51831855 0.115547588 2924 823 3085
368 0.075 0.68 0.55819209 0.53708703 0.045609819 2819 782 3190
369 0.075 0.69 0.57909605 0.55636971 0.030581096 2706 745 3303
370 0.075 0.70 0.60169492 0.57950893 0.033671015 2566 705 3443
371 0.075 0.71 0.62259887 0.59802031 0.017689791 2459 668 3550
372 0.075 0.72 0.64067797 0.62077388 0.052889169 2314 636 3695
373 0.075 0.73 0.66327684 0.64237048 0.039425391 2186 596 3823
374 0.075 0.74 0.67909605 0.65959635 0.052217090 2080 568 3929
375 0.075 0.75 0.69152542 0.67900758 0.209651335 1951 546 4058
376 0.075 0.76 0.71412429 0.69983288 0.143440504 1829 506 4180
377 0.075 0.77 0.73502825 0.72142949 0.155058573 1698 469 4311
378 0.075 0.78 0.75536723 0.74302610 0.186504671 1566 433 4443
379 0.075 0.79 0.77344633 0.76179458 0.201400666 1452 401 4557
380 0.075 0.80 0.78870056 0.77902044 0.278269144 1345 374 4664
381 0.075 0.81 0.80056497 0.79778892 0.766327835 1220 353 4789
382 0.075 0.82 0.81977401 0.81578609 0.647276787 1114 319 4895
383 0.075 0.83 0.83672316 0.83262630 0.624767210 1013 289 4996
384 0.075 0.84 0.85197740 0.84869521 0.688637508 915 262 5094
385 0.075 0.85 0.86666667 0.86617817 0.976892902 805 236 5204
386 0.075 0.86 0.88531073 0.88301838 0.764677465 707 203 5302
387 0.075 0.87 0.90169492 0.89613061 0.407269505 634 174 5375
388 0.075 0.88 0.91355932 0.90821442 0.401294127 561 153 5448
389 0.075 0.89 0.92485876 0.92132665 0.563415188 479 133 5530
390 0.075 0.90 0.93615819 0.93148220 0.405142080 420 113 5589
391 0.075 0.91 0.94576271 0.94073788 0.336336319 365 96 5644
392 0.075 0.92 0.95536723 0.95192184 0.479121663 295 79 5714
393 0.075 0.93 0.96384181 0.96040622 0.438927348 244 64 5765
394 0.075 0.94 0.96892655 0.96747654 0.752714444 198 55 5811
395 0.075 0.95 0.97853107 0.97544672 0.386138838 153 38 5856
396 0.075 0.96 0.98361582 0.98225993 0.697097433 109 29 5900
397 0.075 0.97 0.98757062 0.98753053 1.000000000 75 22 5934
398 0.075 0.98 0.99039548 0.99138707 0.713371384 50 17 5959
399 0.075 0.99 0.99604520 0.99601491 1.000000000 24 7 5985
400 0.075 1.00 1.00000000 1.00000000 0.000000000 0 0 6009
401 0.100 0.01 0.01912046 0.02082530 0.582929172 5565 2052 122
402 0.100 0.02 0.02629063 0.02686721 0.911078754 5533 2037 154
403 0.100 0.03 0.02772467 0.02879547 0.790181210 5521 2034 166
404 0.100 0.04 0.03202677 0.03226636 0.999858998 5503 2025 184
405 0.100 0.05 0.03393881 0.03432318 0.965932756 5491 2021 196
406 0.100 0.06 0.03728489 0.03689420 0.965664871 5478 2014 209
407 0.100 0.07 0.03824092 0.03817971 1.000000000 5470 2012 217
408 0.100 0.08 0.04063098 0.03959378 0.826680282 5464 2007 223
409 0.100 0.09 0.04302103 0.04062219 0.558375514 5461 2002 226
410 0.100 0.10 0.04445507 0.04139350 0.448461685 5458 1999 229
411 0.100 0.11 0.04636711 0.04280756 0.380210816 5451 1995 236
412 0.100 0.12 0.04827916 0.04486438 0.411835487 5439 1991 248
413 0.100 0.13 0.04827916 0.04537858 0.493933610 5435 1991 252
414 0.100 0.14 0.04923518 0.04679265 0.576753839 5426 1989 261
415 0.100 0.15 0.05066922 0.04859236 0.647499499 5415 1986 272
416 0.100 0.16 0.05258126 0.05077773 0.703048938 5402 1982 285
417 0.100 0.17 0.05544933 0.05360586 0.703161773 5386 1976 301
418 0.100 0.18 0.05736138 0.05489137 0.600298813 5380 1972 307
419 0.100 0.19 0.06166348 0.05810515 0.447852742 5364 1963 323
420 0.100 0.20 0.06357553 0.06016197 0.475128652 5352 1959 335
421 0.100 0.21 0.06644359 0.06260445 0.426624727 5339 1953 348
422 0.100 0.22 0.06739962 0.06556113 0.729578082 5318 1951 369
423 0.100 0.23 0.06978967 0.06800360 0.742360573 5304 1946 383
424 0.100 0.24 0.07361377 0.07108883 0.634176101 5288 1938 399
425 0.100 0.25 0.07600382 0.07391696 0.705571551 5271 1933 416
426 0.100 0.26 0.07887189 0.07751639 0.823264484 5249 1927 438
427 0.100 0.27 0.08269598 0.08034452 0.677616076 5235 1919 452
428 0.100 0.28 0.08891013 0.08432961 0.403287777 5217 1906 470
429 0.100 0.29 0.09321224 0.08780049 0.328197102 5199 1897 488
430 0.100 0.30 0.09703633 0.09307109 0.492660180 5166 1889 521
431 0.100 0.31 0.09894837 0.09615632 0.643177878 5146 1885 541
432 0.100 0.32 0.10468451 0.10091271 0.530396202 5121 1873 566
433 0.100 0.33 0.10946463 0.10618331 0.597315424 5090 1863 597
434 0.100 0.34 0.11328872 0.11158247 0.803139521 5056 1855 631
435 0.100 0.35 0.11806883 0.11801003 1.000000000 5016 1845 671
436 0.100 0.36 0.12141491 0.12379483 0.728040730 4978 1838 709
437 0.100 0.37 0.12954111 0.13086515 0.863356290 4940 1821 747
438 0.100 0.38 0.13575526 0.13934953 0.604264942 4887 1808 800
439 0.100 0.39 0.14531549 0.14680550 0.850023069 4849 1788 838
440 0.100 0.40 0.15344168 0.15323306 1.000000000 4816 1771 871
441 0.100 0.41 0.16395793 0.16171744 0.771211779 4772 1749 915
442 0.100 0.42 0.17590822 0.17084458 0.492889556 4726 1724 961
443 0.100 0.43 0.17973231 0.17714359 0.741986229 4685 1716 1002
444 0.100 0.44 0.18785851 0.18549942 0.770464527 4637 1699 1050
445 0.100 0.45 0.19980880 0.19732613 0.762976054 4570 1674 1117
446 0.100 0.46 0.21080306 0.20606762 0.552066985 4525 1651 1162
447 0.100 0.47 0.22514340 0.21827998 0.390970287 4460 1621 1227
448 0.100 0.48 0.23613767 0.22663581 0.236588325 4418 1598 1269
449 0.100 0.49 0.24713193 0.23679136 0.203687401 4362 1575 1325
450 0.100 0.50 0.26768642 0.25118910 0.044925584 4293 1532 1394
451 0.100 0.51 0.27772467 0.26442988 0.113292010 4211 1511 1476
452 0.100 0.52 0.29445507 0.27612804 0.030445671 4155 1476 1532
453 0.100 0.53 0.30831740 0.28936881 0.027308793 4081 1447 1606
454 0.100 0.54 0.32456979 0.30273814 0.011935062 4011 1413 1676
455 0.100 0.55 0.33699809 0.31572182 0.015471470 3936 1387 1751
456 0.100 0.56 0.34942639 0.32767708 0.014225390 3869 1361 1818
457 0.100 0.57 0.36233270 0.34323178 0.033575146 3775 1334 1912
458 0.100 0.58 0.37715105 0.35801517 0.034985899 3691 1303 1996
459 0.100 0.59 0.38766730 0.37189870 0.085644476 3605 1281 2082
460 0.100 0.60 0.40965583 0.38925312 0.026952688 3516 1235 2171
461 0.100 0.61 0.42208413 0.40532202 0.071808817 3417 1209 2270
462 0.100 0.62 0.44168260 0.42383340 0.056609899 3314 1168 2373
463 0.100 0.63 0.46558317 0.44453015 0.025047998 3203 1118 2484
464 0.100 0.64 0.48183556 0.46227021 0.038118329 3099 1084 2588
465 0.100 0.65 0.49856597 0.47923898 0.040960540 3002 1049 2685
466 0.100 0.66 0.51529637 0.49903587 0.086511221 2883 1014 2804
467 0.100 0.67 0.53680688 0.51831855 0.050730442 2778 969 2909
468 0.100 0.68 0.55831740 0.53708703 0.024321816 2677 924 3010
469 0.100 0.69 0.57839388 0.55636971 0.018992182 2569 882 3118
470 0.100 0.70 0.60133843 0.57950893 0.019300621 2437 834 3250
471 0.100 0.71 0.62189293 0.59802031 0.009922349 2336 791 3351
472 0.100 0.72 0.63957935 0.62077388 0.040659625 2196 754 3491
473 0.100 0.73 0.66108987 0.64237048 0.039156325 2073 709 3614
474 0.100 0.74 0.67638623 0.65959635 0.061696705 1971 677 3716
475 0.100 0.75 0.69263862 0.67900758 0.124910138 1854 643 3833
476 0.100 0.76 0.71462715 0.69983288 0.089356759 1738 597 3949
477 0.100 0.77 0.73565966 0.72142949 0.095015354 1614 553 4073
478 0.100 0.78 0.75717017 0.74302610 0.088705269 1491 508 4196
479 0.100 0.79 0.77533461 0.76179458 0.094862268 1383 470 4304
480 0.100 0.80 0.79158700 0.77902044 0.111974828 1283 436 4404
481 0.100 0.81 0.80497132 0.79778892 0.355093583 1165 408 4522
482 0.100 0.82 0.82313576 0.81578609 0.326489610 1063 370 4624
483 0.100 0.83 0.84130019 0.83262630 0.226785578 970 332 4717
484 0.100 0.84 0.85659656 0.84869521 0.252694959 877 300 4810
485 0.100 0.85 0.87141491 0.86617817 0.432307641 772 269 4915
486 0.100 0.86 0.88862333 0.88301838 0.371800295 677 233 5010
487 0.100 0.87 0.90487572 0.89613061 0.135849145 609 199 5078
488 0.100 0.88 0.91634799 0.90821442 0.143556367 539 175 5148
489 0.100 0.89 0.92638623 0.92132665 0.338158247 458 154 5229
490 0.100 0.90 0.93690249 0.93148220 0.272594015 401 132 5286
491 0.100 0.91 0.94741874 0.94073788 0.144441766 351 110 5336
492 0.100 0.92 0.95745698 0.95192184 0.185404439 285 89 5402
493 0.100 0.93 0.96510516 0.96040622 0.221157171 235 73 5452
494 0.100 0.94 0.96988528 0.96747654 0.512907181 190 63 5497
495 0.100 0.95 0.97848948 0.97544672 0.332476187 146 45 5541
496 0.100 0.96 0.98470363 0.98225993 0.371628556 106 32 5581
497 0.100 0.97 0.98852772 0.98753053 0.714743610 73 24 5614
498 0.100 0.98 0.99091778 0.99138707 0.893948843 48 19 5639
499 0.100 0.99 0.99617591 0.99601491 1.000000000 23 8 5664
500 0.100 1.00 1.00000000 1.00000000 0.000000000 0 0 5687
Dboth
1 11
2 12
3 14
4 15
5 16
6 17
7 18
8 20
9 22
10 22
11 24
12 24
13 24
14 24
15 25
16 27
17 29
18 29
19 30
20 30
21 32
22 32
23 33
24 36
25 38
26 38
27 39
28 44
29 47
30 47
31 48
32 49
33 50
34 51
35 54
36 55
37 60
38 63
39 67
40 71
41 76
42 84
43 85
44 88
45 94
46 96
47 101
48 108
49 115
50 121
51 129
52 136
53 141
54 147
55 150
56 154
57 163
58 173
59 178
60 191
61 196
62 204
63 217
64 225
65 234
66 242
67 255
68 264
69 275
70 289
71 301
72 307
73 316
74 319
75 325
76 339
77 344
78 360
79 366
80 373
81 376
82 381
83 389
84 399
85 403
86 413
87 421
88 422
89 426
90 431
91 432
92 438
93 441
94 441
95 448
96 451
97 451
98 451
99 456
100 457
101 17
102 23
103 26
104 28
105 30
106 34
107 36
108 38
109 40
110 40
111 44
112 45
113 45
114 45
115 46
116 48
117 52
118 54
119 57
120 60
121 64
122 65
123 68
124 72
125 74
126 76
127 79
128 85
129 89
130 92
131 94
132 98
133 103
134 107
135 111
136 112
137 120
138 126
139 138
140 147
141 156
142 170
143 172
144 177
145 190
146 198
147 209
148 219
149 234
150 250
151 266
152 282
153 292
154 308
155 315
156 328
157 343
158 359
159 368
160 388
161 402
162 419
163 438
164 452
165 469
166 483
167 502
168 523
169 543
170 564
171 587
172 598
173 624
174 633
175 646
176 670
177 685
178 709
179 726
180 737
181 744
182 761
183 774
184 791
185 804
186 824
187 837
188 843
189 854
190 863
191 872
192 883
193 888
194 890
195 900
196 906
197 909
198 910
199 916
200 920
201 28
202 37
203 40
204 45
205 48
206 52
207 54
208 58
209 60
210 61
211 65
212 68
213 68
214 68
215 69
216 73
217 79
218 82
219 87
220 90
221 95
222 96
223 99
224 104
225 108
226 110
227 115
228 124
229 130
230 135
231 138
232 147
233 156
234 162
235 167
236 171
237 184
238 192
239 208
240 221
241 237
242 257
243 261
244 272
245 289
246 304
247 323
248 337
249 356
250 384
251 400
252 423
253 440
254 459
255 477
256 496
257 516
258 535
259 550
260 580
261 600
262 627
263 657
264 678
265 698
266 718
267 751
268 785
269 812
270 846
271 878
272 899
273 935
274 955
275 974
276 1007
277 1034
278 1064
279 1088
280 1110
281 1121
282 1148
283 1173
284 1195
285 1218
286 1244
287 1265
288 1277
289 1293
290 1307
291 1321
292 1336
293 1347
294 1354
295 1368
296 1376
297 1380
298 1384
299 1391
300 1397
301 35
302 46
303 49
304 55
305 59
306 64
307 66
308 71
309 76
310 78
311 82
312 85
313 85
314 87
315 88
316 92
317 98
318 102
319 110
320 113
321 119
322 120
323 124
324 131
325 135
326 138
327 144
328 155
329 161
330 169
331 173
332 182
333 191
334 198
335 207
336 212
337 228
338 238
339 255
340 271
341 289
342 314
343 321
344 336
345 359
346 379
347 406
348 427
349 448
350 484
351 503
352 532
353 558
354 584
355 606
356 629
357 652
358 675
359 695
360 732
361 753
362 786
363 828
364 856
365 880
366 907
367 947
368 988
369 1025
370 1065
371 1102
372 1134
373 1174
374 1202
375 1224
376 1264
377 1301
378 1337
379 1369
380 1396
381 1417
382 1451
383 1481
384 1508
385 1534
386 1567
387 1596
388 1617
389 1637
390 1657
391 1674
392 1691
393 1706
394 1715
395 1732
396 1741
397 1748
398 1753
399 1763
400 1770
401 40
402 55
403 58
404 67
405 71
406 78
407 80
408 85
409 90
410 93
411 97
412 101
413 101
414 103
415 106
416 110
417 116
418 120
419 129
420 133
421 139
422 141
423 146
424 154
425 159
426 165
427 173
428 186
429 195
430 203
431 207
432 219
433 229
434 237
435 247
436 254
437 271
438 284
439 304
440 321
441 343
442 368
443 376
444 393
445 418
446 441
447 471
448 494
449 517
450 560
451 581
452 616
453 645
454 679
455 705
456 731
457 758
458 789
459 811
460 857
461 883
462 924
463 974
464 1008
465 1043
466 1078
467 1123
468 1168
469 1210
470 1258
471 1301
472 1338
473 1383
474 1415
475 1449
476 1495
477 1539
478 1584
479 1622
480 1656
481 1684
482 1722
483 1760
484 1792
485 1823
486 1859
487 1893
488 1917
489 1938
490 1960
491 1982
492 2003
493 2019
494 2029
495 2047
496 2060
497 2068
498 2073
499 2084
500 2092enrichment.plotter(gene.hic.filt, "weighted_Z.s2post.H", "adj.P.Val", "FDR for Weighted p-val Combine of Hi-C Contacts Overlapping Gene, Human")
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC Dboth
1 0.010 0.01 0.7297872 0.6813498 0.022962069 2347 127 4947 343
2 0.010 0.02 0.7446809 0.6926842 0.013543632 2266 120 5028 350
3 0.010 0.03 0.7489362 0.6971922 0.013621159 2233 118 5061 352
4 0.010 0.04 0.7531915 0.7028594 0.015893354 2191 116 5103 354
5 0.010 0.05 0.7574468 0.7064657 0.014214503 2165 114 5129 356
6 0.010 0.06 0.7638298 0.7102009 0.009553302 2139 111 5155 359
7 0.010 0.07 0.7638298 0.7122617 0.012586039 2123 111 5171 359
8 0.010 0.08 0.7638298 0.7145801 0.017009793 2105 111 5189 359
9 0.010 0.09 0.7659574 0.7172849 0.018044986 2085 110 5209 360
10 0.010 0.10 0.7659574 0.7206337 0.027359231 2059 110 5235 360
11 0.010 0.11 0.7680851 0.7217929 0.023976287 2051 109 5243 361
12 0.010 0.12 0.7702128 0.7237249 0.023077436 2037 108 5257 362
13 0.010 0.13 0.7702128 0.7252705 0.027902316 2025 108 5269 362
14 0.010 0.14 0.7702128 0.7269449 0.034108528 2012 108 5282 362
15 0.010 0.15 0.7723404 0.7299073 0.037160470 1990 107 5304 363
16 0.010 0.16 0.7744681 0.7314529 0.034247052 1979 106 5315 364
17 0.010 0.17 0.7765957 0.7344152 0.037310268 1957 105 5337 365
18 0.010 0.18 0.7765957 0.7349304 0.039634673 1953 105 5341 365
19 0.010 0.19 0.7808511 0.7362184 0.027011163 1945 103 5349 367
20 0.010 0.20 0.7829787 0.7373776 0.023591758 1937 102 5357 368
21 0.010 0.21 0.7851064 0.7382792 0.019879701 1931 101 5363 369
22 0.010 0.22 0.7851064 0.7390520 0.021935890 1925 101 5369 369
23 0.010 0.23 0.7851064 0.7402112 0.025374185 1916 101 5378 369
24 0.010 0.24 0.7851064 0.7411128 0.028368962 1909 101 5385 369
25 0.010 0.25 0.7851064 0.7422720 0.032673318 1900 101 5394 369
26 0.010 0.26 0.7851064 0.7434312 0.037538532 1891 101 5403 369
27 0.010 0.27 0.7872340 0.7452344 0.035611594 1878 100 5416 370
28 0.010 0.28 0.7914894 0.7465224 0.023981639 1870 98 5424 372
29 0.010 0.29 0.7914894 0.7471664 0.026002062 1865 98 5429 372
30 0.010 0.30 0.7914894 0.7479392 0.028623804 1859 98 5435 372
31 0.010 0.31 0.7914894 0.7489696 0.032479796 1851 98 5443 372
32 0.010 0.32 0.7914894 0.7506440 0.039718525 1838 98 5456 372
33 0.010 0.33 0.7914894 0.7510304 0.041575605 1835 98 5459 372
34 0.010 0.34 0.7914894 0.7520608 0.046900036 1827 98 5467 372
35 0.010 0.35 0.7914894 0.7528336 0.051269969 1821 98 5473 372
36 0.010 0.36 0.7914894 0.7536064 0.055984905 1815 98 5479 372
37 0.010 0.37 0.7914894 0.7545080 0.061949295 1808 98 5486 372
38 0.010 0.38 0.7936170 0.7552808 0.052484919 1803 97 5491 373
39 0.010 0.39 0.7936170 0.7559248 0.056477919 1798 97 5496 373
40 0.010 0.40 0.7936170 0.7563112 0.058996158 1795 97 5499 373
41 0.010 0.41 0.7957447 0.7572128 0.050635401 1789 96 5505 374
42 0.010 0.42 0.7978723 0.7587584 0.046674719 1778 95 5516 375
43 0.010 0.43 0.8000000 0.7599176 0.041029756 1770 94 5524 376
44 0.010 0.44 0.8000000 0.7604328 0.043619019 1766 94 5528 376
45 0.010 0.45 0.8000000 0.7608192 0.045652753 1763 94 5531 376
46 0.010 0.46 0.8000000 0.7609480 0.046348640 1762 94 5532 376
47 0.010 0.47 0.8021277 0.7614632 0.037681343 1759 93 5535 377
48 0.010 0.48 0.8063830 0.7630088 0.026048606 1749 91 5545 379
49 0.010 0.49 0.8063830 0.7636528 0.028259074 1744 91 5550 379
50 0.010 0.50 0.8063830 0.7642968 0.030633396 1739 91 5555 379
51 0.010 0.51 0.8085106 0.7649408 0.024954217 1735 90 5559 380
52 0.010 0.52 0.8085106 0.7653272 0.026216077 1732 90 5562 380
53 0.010 0.53 0.8085106 0.7664863 0.030346702 1723 90 5571 380
54 0.010 0.54 0.8085106 0.7667439 0.031338907 1721 90 5573 380
55 0.010 0.55 0.8148936 0.7677743 0.014703701 1716 87 5578 383
56 0.010 0.56 0.8148936 0.7689335 0.017202427 1707 87 5587 383
57 0.010 0.57 0.8170213 0.7700927 0.014767418 1699 86 5595 384
58 0.010 0.58 0.8191489 0.7713807 0.012861861 1690 85 5604 385
59 0.010 0.59 0.8191489 0.7721535 0.014312251 1684 85 5610 385
60 0.010 0.60 0.8191489 0.7727975 0.015631825 1679 85 5615 385
61 0.010 0.61 0.8191489 0.7736991 0.017663132 1672 85 5622 385
62 0.010 0.62 0.8191489 0.7749871 0.020975778 1662 85 5632 385
63 0.010 0.63 0.8191489 0.7761463 0.024419928 1653 85 5641 385
64 0.010 0.64 0.8191489 0.7769191 0.026986660 1647 85 5647 385
65 0.010 0.65 0.8191489 0.7780783 0.031284784 1638 85 5656 385
66 0.010 0.66 0.8212766 0.7791087 0.026679119 1631 84 5663 386
67 0.010 0.67 0.8234043 0.7796239 0.021163917 1628 83 5666 387
68 0.010 0.68 0.8234043 0.7803967 0.023439009 1622 83 5672 387
69 0.010 0.69 0.8234043 0.7818135 0.028180843 1611 83 5683 387
70 0.010 0.70 0.8255319 0.7821999 0.021991057 1609 82 5685 388
71 0.010 0.71 0.8255319 0.7827151 0.023540998 1605 82 5689 388
72 0.010 0.72 0.8255319 0.7831015 0.024766509 1602 82 5692 388
73 0.010 0.73 0.8255319 0.7842607 0.028789937 1593 82 5701 388
74 0.010 0.74 0.8255319 0.7852911 0.032840697 1585 82 5709 388
75 0.010 0.75 0.8255319 0.7864503 0.037989822 1576 82 5718 388
76 0.010 0.76 0.8255319 0.7883823 0.048147635 1561 82 5733 388
77 0.010 0.77 0.8255319 0.7892839 0.053643288 1554 82 5740 388
78 0.010 0.78 0.8255319 0.7904431 0.061496247 1545 82 5749 388
79 0.010 0.79 0.8255319 0.7916023 0.070312155 1536 82 5758 388
80 0.010 0.80 0.8276596 0.7931479 0.064745005 1525 81 5769 389
81 0.010 0.81 0.8297872 0.7944359 0.057727224 1516 80 5778 390
82 0.010 0.82 0.8297872 0.7953375 0.064187200 1509 80 5785 390
83 0.010 0.83 0.8319149 0.7962391 0.054605725 1503 79 5791 391
84 0.010 0.84 0.8319149 0.7972694 0.061719298 1495 79 5799 391
85 0.010 0.85 0.8319149 0.7977846 0.065564261 1491 79 5803 391
86 0.010 0.86 0.8361702 0.7994590 0.046448112 1480 77 5814 393
87 0.010 0.87 0.8382979 0.8007470 0.041039122 1471 76 5823 394
88 0.010 0.88 0.8425532 0.8015198 0.024999256 1467 74 5827 396
89 0.010 0.89 0.8425532 0.8029366 0.030173465 1456 74 5838 396
90 0.010 0.90 0.8446809 0.8037094 0.024619524 1451 73 5843 397
91 0.010 0.91 0.8446809 0.8048686 0.028747040 1442 73 5852 397
92 0.010 0.92 0.8510638 0.8080886 0.017291017 1420 70 5874 400
93 0.010 0.93 0.8510638 0.8102782 0.023450725 1403 70 5891 400
94 0.010 0.94 0.8553191 0.8129830 0.017909660 1384 68 5910 402
95 0.010 0.95 0.8553191 0.8162030 0.027987326 1359 68 5935 402
96 0.010 0.96 0.8553191 0.8201958 0.047282703 1328 68 5966 402
97 0.010 0.97 0.8595745 0.8247038 0.046729618 1295 66 5999 404
98 0.010 0.98 0.8680851 0.8297269 0.026468035 1260 62 6034 408
99 0.010 0.99 0.8765957 0.8362957 0.017699671 1213 58 6081 412
100 0.010 1.00 1.0000000 1.0000000 0.000000000 0 0 7294 470
101 0.025 0.01 0.7009751 0.6813498 0.184984033 2198 276 4643 647
102 0.025 0.02 0.7139762 0.6926842 0.145498203 2122 264 4719 659
103 0.025 0.03 0.7172264 0.6971922 0.169730487 2090 261 4751 662
104 0.025 0.04 0.7237270 0.7028594 0.150002343 2052 255 4789 668
105 0.025 0.05 0.7291441 0.7064657 0.115641732 2029 250 4812 673
106 0.025 0.06 0.7334778 0.7102009 0.104809992 2004 246 4837 677
107 0.025 0.07 0.7356446 0.7122617 0.102470569 1990 244 4851 679
108 0.025 0.08 0.7367281 0.7145801 0.121515569 1973 243 4868 680
109 0.025 0.09 0.7399783 0.7172849 0.111361836 1955 240 4886 683
110 0.025 0.10 0.7399783 0.7206337 0.174994604 1929 240 4912 683
111 0.025 0.11 0.7432286 0.7217929 0.131275907 1923 237 4918 686
112 0.025 0.12 0.7443120 0.7237249 0.146803912 1909 236 4932 687
113 0.025 0.13 0.7443120 0.7252705 0.179799931 1897 236 4944 687
114 0.025 0.14 0.7453954 0.7269449 0.193259834 1885 235 4956 688
115 0.025 0.15 0.7497291 0.7299073 0.159898146 1866 231 4975 692
116 0.025 0.16 0.7508126 0.7314529 0.169375847 1855 230 4986 693
117 0.025 0.17 0.7518960 0.7344152 0.214470478 1833 229 5008 694
118 0.025 0.18 0.7518960 0.7349304 0.228451613 1829 229 5012 694
119 0.025 0.19 0.7540628 0.7362184 0.203805540 1821 227 5020 696
120 0.025 0.20 0.7562297 0.7373776 0.178078487 1814 225 5027 698
121 0.025 0.21 0.7573131 0.7382792 0.173331575 1808 224 5033 699
122 0.025 0.22 0.7573131 0.7390520 0.191578870 1802 224 5039 699
123 0.025 0.23 0.7573131 0.7402112 0.221612627 1793 224 5048 699
124 0.025 0.24 0.7583965 0.7411128 0.216061875 1787 223 5054 700
125 0.025 0.25 0.7583965 0.7422720 0.248869431 1778 223 5063 700
126 0.025 0.26 0.7583965 0.7434312 0.285116821 1769 223 5072 700
127 0.025 0.27 0.7594800 0.7452344 0.308721223 1756 222 5085 701
128 0.025 0.28 0.7616468 0.7465224 0.277921383 1748 220 5093 703
129 0.025 0.29 0.7627302 0.7471664 0.263295666 1744 219 5097 704
130 0.025 0.30 0.7638137 0.7479392 0.253069893 1739 218 5102 705
131 0.025 0.31 0.7648971 0.7489696 0.250787842 1732 217 5109 706
132 0.025 0.32 0.7659805 0.7506440 0.268384253 1720 216 5121 707
133 0.025 0.33 0.7659805 0.7510304 0.280835937 1717 216 5124 707
134 0.025 0.34 0.7659805 0.7520608 0.316000398 1709 216 5132 707
135 0.025 0.35 0.7659805 0.7528336 0.344261848 1703 216 5138 707
136 0.025 0.36 0.7659805 0.7536064 0.374148486 1697 216 5144 707
137 0.025 0.37 0.7659805 0.7545080 0.411062480 1690 216 5151 707
138 0.025 0.38 0.7670639 0.7552808 0.397393373 1685 215 5156 708
139 0.025 0.39 0.7670639 0.7559248 0.424571557 1680 215 5161 708
140 0.025 0.40 0.7670639 0.7563112 0.441410739 1677 215 5164 708
141 0.025 0.41 0.7681473 0.7572128 0.432742611 1671 214 5170 709
142 0.025 0.42 0.7713976 0.7587584 0.360101314 1662 211 5179 712
143 0.025 0.43 0.7724810 0.7599176 0.362328462 1654 210 5187 713
144 0.025 0.44 0.7735645 0.7604328 0.339731655 1651 209 5190 714
145 0.025 0.45 0.7735645 0.7608192 0.354494285 1648 209 5193 714
146 0.025 0.46 0.7735645 0.7609480 0.359507460 1647 209 5194 714
147 0.025 0.47 0.7746479 0.7614632 0.336987390 1644 208 5197 715
148 0.025 0.48 0.7768147 0.7630088 0.312701748 1634 206 5207 717
149 0.025 0.49 0.7768147 0.7636528 0.336323277 1629 206 5212 717
150 0.025 0.50 0.7768147 0.7642968 0.361109002 1624 206 5217 717
151 0.025 0.51 0.7789816 0.7649408 0.302843665 1621 204 5220 719
152 0.025 0.52 0.7789816 0.7653272 0.316617859 1618 204 5223 719
153 0.025 0.53 0.7789816 0.7664863 0.360467814 1609 204 5232 719
154 0.025 0.54 0.7789816 0.7667439 0.370727968 1607 204 5234 719
155 0.025 0.55 0.7822319 0.7677743 0.286132032 1602 201 5239 722
156 0.025 0.56 0.7833153 0.7689335 0.287921178 1594 200 5247 723
157 0.025 0.57 0.7843987 0.7700927 0.289719023 1586 199 5255 724
158 0.025 0.58 0.7865655 0.7713807 0.259081852 1578 197 5263 726
159 0.025 0.59 0.7876490 0.7721535 0.248549676 1573 196 5268 727
160 0.025 0.60 0.7876490 0.7727975 0.269034296 1568 196 5273 727
161 0.025 0.61 0.7876490 0.7736991 0.299686605 1561 196 5280 727
162 0.025 0.62 0.7876490 0.7749871 0.347536594 1551 196 5290 727
163 0.025 0.63 0.7876490 0.7761463 0.394716844 1542 196 5299 727
164 0.025 0.64 0.7876490 0.7769191 0.428323805 1536 196 5305 727
165 0.025 0.65 0.7887324 0.7780783 0.430909878 1528 195 5313 728
166 0.025 0.66 0.7908992 0.7791087 0.380154294 1522 193 5319 730
167 0.025 0.67 0.7919827 0.7796239 0.356155270 1519 192 5322 731
168 0.025 0.68 0.7919827 0.7803967 0.387883828 1513 192 5328 731
169 0.025 0.69 0.7930661 0.7818135 0.401272598 1503 191 5338 732
170 0.025 0.70 0.7941495 0.7821999 0.371030855 1501 190 5340 733
171 0.025 0.71 0.7941495 0.7827151 0.392619998 1497 190 5344 733
172 0.025 0.72 0.7941495 0.7831015 0.409325611 1494 190 5347 733
173 0.025 0.73 0.7941495 0.7842607 0.462051905 1485 190 5356 733
174 0.025 0.74 0.7941495 0.7852911 0.512124031 1477 190 5364 733
175 0.025 0.75 0.7941495 0.7864503 0.571885571 1468 190 5373 733
176 0.025 0.76 0.7941495 0.7883823 0.678826047 1453 190 5388 733
177 0.025 0.77 0.7941495 0.7892839 0.731478126 1446 190 5395 733
178 0.025 0.78 0.7963164 0.7904431 0.671574812 1439 188 5402 735
179 0.025 0.79 0.7973998 0.7916023 0.675350781 1431 187 5410 736
180 0.025 0.80 0.7984832 0.7931479 0.701691804 1420 186 5421 737
181 0.025 0.81 0.7995666 0.7944359 0.713216303 1411 185 5430 738
182 0.025 0.82 0.7995666 0.7953375 0.767373192 1404 185 5437 738
183 0.025 0.83 0.8006501 0.7962391 0.755870004 1398 184 5443 739
184 0.025 0.84 0.8006501 0.7972694 0.819222965 1390 184 5451 739
185 0.025 0.85 0.8006501 0.7977846 0.851467576 1386 184 5455 739
186 0.025 0.86 0.8039003 0.7994590 0.752601626 1376 181 5465 742
187 0.025 0.87 0.8060672 0.8007470 0.698619380 1368 179 5473 744
188 0.025 0.88 0.8104009 0.8015198 0.498593768 1366 175 5475 748
189 0.025 0.89 0.8136511 0.8029366 0.407832240 1358 172 5483 751
190 0.025 0.90 0.8147346 0.8037094 0.392963632 1353 171 5488 752
191 0.025 0.91 0.8169014 0.8048686 0.348005229 1346 169 5495 754
192 0.025 0.92 0.8212351 0.8080886 0.300225107 1325 165 5516 758
193 0.025 0.93 0.8212351 0.8102782 0.389923710 1308 165 5533 758
194 0.025 0.94 0.8234020 0.8129830 0.412297595 1289 163 5552 760
195 0.025 0.95 0.8244854 0.8162030 0.517738004 1265 162 5576 761
196 0.025 0.96 0.8266522 0.8201958 0.618135590 1236 160 5605 763
197 0.025 0.97 0.8320693 0.8247038 0.561327985 1206 155 5635 768
198 0.025 0.98 0.8396533 0.8297269 0.419037165 1174 148 5667 775
199 0.025 0.99 0.8472373 0.8362957 0.362978439 1130 141 5711 782
200 0.025 1.00 1.0000000 1.0000000 0.000000000 0 0 6841 923
201 0.050 0.01 0.6952177 0.6813498 0.230574288 2047 427 4316 974
202 0.050 0.02 0.7059243 0.6926842 0.248292085 1974 412 4389 989
203 0.050 0.03 0.7109208 0.6971922 0.228877153 1946 405 4417 996
204 0.050 0.04 0.7159172 0.7028594 0.250520797 1909 398 4454 1003
205 0.050 0.05 0.7194861 0.7064657 0.250241749 1886 393 4477 1008
206 0.050 0.06 0.7237687 0.7102009 0.228591319 1863 387 4500 1014
207 0.050 0.07 0.7266238 0.7122617 0.200863880 1851 383 4512 1018
208 0.050 0.08 0.7273376 0.7145801 0.256254166 1834 382 4529 1019
209 0.050 0.09 0.7301927 0.7172849 0.249175189 1817 378 4546 1023
210 0.050 0.10 0.7316203 0.7206337 0.327329975 1793 376 4570 1025
211 0.050 0.11 0.7344754 0.7217929 0.255442043 1788 372 4575 1029
212 0.050 0.12 0.7359029 0.7237249 0.274379477 1775 370 4588 1031
213 0.050 0.13 0.7366167 0.7252705 0.308731593 1764 369 4599 1032
214 0.050 0.14 0.7387580 0.7269449 0.287709943 1754 366 4609 1035
215 0.050 0.15 0.7437545 0.7299073 0.209039610 1738 359 4625 1042
216 0.050 0.16 0.7458958 0.7314529 0.188824794 1729 356 4634 1045
217 0.050 0.17 0.7487509 0.7344152 0.190649439 1710 352 4653 1049
218 0.050 0.18 0.7487509 0.7349304 0.207232983 1706 352 4657 1049
219 0.050 0.19 0.7508922 0.7362184 0.179193958 1699 349 4664 1052
220 0.050 0.20 0.7530335 0.7373776 0.150598743 1693 346 4670 1055
221 0.050 0.21 0.7551749 0.7382792 0.119797961 1689 343 4674 1058
222 0.050 0.22 0.7551749 0.7390520 0.137715829 1683 343 4680 1058
223 0.050 0.23 0.7573162 0.7402112 0.114314513 1677 340 4686 1061
224 0.050 0.24 0.7587438 0.7411128 0.102990705 1672 338 4691 1063
225 0.050 0.25 0.7587438 0.7422720 0.127675645 1663 338 4700 1063
226 0.050 0.26 0.7587438 0.7434312 0.156822782 1654 338 4709 1063
227 0.050 0.27 0.7601713 0.7452344 0.166518940 1642 336 4721 1065
228 0.050 0.28 0.7615989 0.7465224 0.161796811 1634 334 4729 1067
229 0.050 0.29 0.7623126 0.7471664 0.159466378 1630 333 4733 1068
230 0.050 0.30 0.7630264 0.7479392 0.160713725 1625 332 4738 1069
231 0.050 0.31 0.7637402 0.7489696 0.169319717 1618 331 4745 1070
232 0.050 0.32 0.7665953 0.7506440 0.136146269 1609 327 4754 1074
233 0.050 0.33 0.7665953 0.7510304 0.145912175 1606 327 4757 1074
234 0.050 0.34 0.7673091 0.7520608 0.153918277 1599 326 4764 1075
235 0.050 0.35 0.7680228 0.7528336 0.155123816 1594 325 4769 1076
236 0.050 0.36 0.7680228 0.7536064 0.177333196 1588 325 4775 1076
237 0.050 0.37 0.7687366 0.7545080 0.182651524 1582 324 4781 1077
238 0.050 0.38 0.7708779 0.7552808 0.142738973 1579 321 4784 1080
239 0.050 0.39 0.7708779 0.7559248 0.160025648 1574 321 4789 1080
240 0.050 0.40 0.7708779 0.7563112 0.171147430 1571 321 4792 1080
241 0.050 0.41 0.7715917 0.7572128 0.176331524 1565 320 4798 1081
242 0.050 0.42 0.7751606 0.7587584 0.120995747 1558 315 4805 1086
243 0.050 0.43 0.7758744 0.7599176 0.131032034 1550 314 4813 1087
244 0.050 0.44 0.7765882 0.7604328 0.125919878 1547 313 4816 1088
245 0.050 0.45 0.7765882 0.7608192 0.135231260 1544 313 4819 1088
246 0.050 0.46 0.7765882 0.7609480 0.138452853 1543 313 4820 1088
247 0.050 0.47 0.7780157 0.7614632 0.116139852 1541 311 4822 1090
248 0.050 0.48 0.7794433 0.7630088 0.117999752 1531 309 4832 1092
249 0.050 0.49 0.7794433 0.7636528 0.133092427 1526 309 4837 1092
250 0.050 0.50 0.7801570 0.7642968 0.130984198 1522 308 4841 1093
251 0.050 0.51 0.7822984 0.7649408 0.097386027 1520 305 4843 1096
252 0.050 0.52 0.7822984 0.7653272 0.105037310 1517 305 4846 1096
253 0.050 0.53 0.7822984 0.7664863 0.130938718 1508 305 4855 1096
254 0.050 0.54 0.7830121 0.7667439 0.119805834 1507 304 4856 1097
255 0.050 0.55 0.7851535 0.7677743 0.095560323 1502 301 4861 1100
256 0.050 0.56 0.7865810 0.7689335 0.089884489 1495 299 4868 1102
257 0.050 0.57 0.7872948 0.7700927 0.097877891 1487 298 4876 1103
258 0.050 0.58 0.7887223 0.7713807 0.094477091 1479 296 4884 1105
259 0.050 0.59 0.7901499 0.7721535 0.082074224 1475 294 4888 1107
260 0.050 0.60 0.7908637 0.7727975 0.080568388 1471 293 4892 1108
261 0.050 0.61 0.7908637 0.7736991 0.096760127 1464 293 4899 1108
262 0.050 0.62 0.7908637 0.7749871 0.124390135 1454 293 4909 1108
263 0.050 0.63 0.7908637 0.7761463 0.154317933 1445 293 4918 1108
264 0.050 0.64 0.7908637 0.7769191 0.177192586 1439 293 4924 1108
265 0.050 0.65 0.7915774 0.7780783 0.190994672 1431 292 4932 1109
266 0.050 0.66 0.7930050 0.7791087 0.177207217 1425 290 4938 1111
267 0.050 0.67 0.7937188 0.7796239 0.170580228 1422 289 4941 1112
268 0.050 0.68 0.7937188 0.7803967 0.195358738 1416 289 4947 1112
269 0.050 0.69 0.7944325 0.7818135 0.219623152 1406 288 4957 1113
270 0.050 0.70 0.7958601 0.7821999 0.182660064 1405 286 4958 1115
271 0.050 0.71 0.7965739 0.7827151 0.175845583 1402 285 4961 1116
272 0.050 0.72 0.7965739 0.7831015 0.188253053 1399 285 4964 1116
273 0.050 0.73 0.7972877 0.7842607 0.202823843 1391 284 4972 1117
274 0.050 0.74 0.7972877 0.7852911 0.241192935 1383 284 4980 1117
275 0.050 0.75 0.7972877 0.7864503 0.290342826 1374 284 4989 1117
276 0.050 0.76 0.7987152 0.7883823 0.312580907 1361 282 5002 1119
277 0.050 0.77 0.7987152 0.7892839 0.357575603 1354 282 5009 1119
278 0.050 0.78 0.8001428 0.7904431 0.342559217 1347 280 5016 1121
279 0.050 0.79 0.8008565 0.7916023 0.365085335 1339 279 5024 1122
280 0.050 0.80 0.8015703 0.7931479 0.410337375 1328 278 5035 1123
281 0.050 0.81 0.8022841 0.7944359 0.443405150 1319 277 5044 1124
282 0.050 0.82 0.8022841 0.7953375 0.499477783 1312 277 5051 1124
283 0.050 0.83 0.8037116 0.7962391 0.465140384 1307 275 5056 1126
284 0.050 0.84 0.8044254 0.7972694 0.484409908 1300 274 5063 1127
285 0.050 0.85 0.8044254 0.7977846 0.517724255 1296 274 5067 1127
286 0.050 0.86 0.8065667 0.7994590 0.485748335 1286 271 5077 1130
287 0.050 0.87 0.8087081 0.8007470 0.431222264 1279 268 5084 1133
288 0.050 0.88 0.8115632 0.8015198 0.315327435 1277 264 5086 1137
289 0.050 0.89 0.8144183 0.8029366 0.247550655 1270 260 5093 1141
290 0.050 0.90 0.8165596 0.8037094 0.193431829 1267 257 5096 1144
291 0.050 0.91 0.8179872 0.8048686 0.183054664 1260 255 5103 1146
292 0.050 0.92 0.8215560 0.8080886 0.168671774 1240 250 5123 1151
293 0.050 0.93 0.8215560 0.8102782 0.249470873 1223 250 5140 1151
294 0.050 0.94 0.8236974 0.8129830 0.272092937 1205 247 5158 1154
295 0.050 0.95 0.8251249 0.8162030 0.360555954 1182 245 5181 1156
296 0.050 0.96 0.8265525 0.8201958 0.518302129 1153 243 5210 1158
297 0.050 0.97 0.8301213 0.8247038 0.582108356 1123 238 5240 1163
298 0.050 0.98 0.8372591 0.8297269 0.429951212 1094 228 5269 1173
299 0.050 0.99 0.8451106 0.8362957 0.344594448 1054 217 5309 1184
300 0.050 1.00 1.0000000 1.0000000 0.000000000 0 0 6363 1401
301 0.075 0.01 0.6919592 0.6813498 0.289332319 1930 544 4068 1222
302 0.075 0.02 0.7032843 0.6926842 0.285016018 1862 524 4136 1242
303 0.075 0.03 0.7072480 0.6971922 0.309187143 1834 517 4164 1249
304 0.075 0.04 0.7129105 0.7028594 0.306806665 1800 507 4198 1259
305 0.075 0.05 0.7157418 0.7064657 0.345070928 1777 502 4221 1264
306 0.075 0.06 0.7197055 0.7102009 0.331127491 1755 495 4243 1271
307 0.075 0.07 0.7231031 0.7122617 0.264815502 1745 489 4253 1277
308 0.075 0.08 0.7236693 0.7145801 0.351188429 1728 488 4270 1278
309 0.075 0.09 0.7270668 0.7172849 0.313206914 1713 482 4285 1284
310 0.075 0.10 0.7287656 0.7206337 0.402954625 1690 479 4308 1287
311 0.075 0.11 0.7310306 0.7217929 0.339371493 1685 475 4313 1291
312 0.075 0.12 0.7321631 0.7237249 0.383220990 1672 473 4326 1293
313 0.075 0.13 0.7327293 0.7252705 0.442133176 1661 472 4337 1294
314 0.075 0.14 0.7349943 0.7269449 0.404595624 1652 468 4346 1298
315 0.075 0.15 0.7389581 0.7299073 0.345104512 1636 461 4362 1305
316 0.075 0.16 0.7412231 0.7314529 0.306096113 1628 457 4370 1309
317 0.075 0.17 0.7440544 0.7344152 0.311124198 1610 452 4388 1314
318 0.075 0.18 0.7446206 0.7349304 0.308191229 1607 451 4391 1315
319 0.075 0.19 0.7468856 0.7362184 0.259904794 1601 447 4397 1319
320 0.075 0.20 0.7491506 0.7373776 0.211900491 1596 443 4402 1323
321 0.075 0.21 0.7508494 0.7382792 0.181403137 1592 440 4406 1326
322 0.075 0.22 0.7519819 0.7390520 0.168547194 1588 438 4410 1328
323 0.075 0.23 0.7542469 0.7402112 0.133759232 1583 434 4415 1332
324 0.075 0.24 0.7559456 0.7411128 0.112252800 1579 431 4419 1335
325 0.075 0.25 0.7559456 0.7422720 0.143260336 1570 431 4428 1335
326 0.075 0.26 0.7559456 0.7434312 0.180566278 1561 431 4437 1335
327 0.075 0.27 0.7576444 0.7452344 0.183301851 1550 428 4448 1338
328 0.075 0.28 0.7587769 0.7465224 0.188243940 1542 426 4456 1340
329 0.075 0.29 0.7593431 0.7471664 0.190755939 1538 425 4460 1341
330 0.075 0.30 0.7604757 0.7479392 0.177246313 1534 423 4464 1343
331 0.075 0.31 0.7610419 0.7489696 0.193621857 1527 422 4471 1344
332 0.075 0.32 0.7638732 0.7506440 0.152519807 1519 417 4479 1349
333 0.075 0.33 0.7638732 0.7510304 0.164922416 1516 417 4482 1349
334 0.075 0.34 0.7655719 0.7520608 0.143020148 1511 414 4487 1352
335 0.075 0.35 0.7661382 0.7528336 0.148942196 1506 413 4492 1353
336 0.075 0.36 0.7661382 0.7536064 0.174130044 1500 413 4498 1353
337 0.075 0.37 0.7672707 0.7545080 0.165629772 1495 411 4503 1355
338 0.075 0.38 0.7689694 0.7552808 0.136004364 1492 408 4506 1358
339 0.075 0.39 0.7689694 0.7559248 0.155467148 1487 408 4511 1358
340 0.075 0.40 0.7689694 0.7563112 0.168137742 1484 408 4514 1358
341 0.075 0.41 0.7695357 0.7572128 0.179416962 1478 407 4520 1359
342 0.075 0.42 0.7729332 0.7587584 0.120559457 1472 401 4526 1365
343 0.075 0.43 0.7734994 0.7599176 0.136589189 1464 400 4534 1366
344 0.075 0.44 0.7746319 0.7604328 0.119029986 1462 398 4536 1368
345 0.075 0.45 0.7746319 0.7608192 0.129415051 1459 398 4539 1368
346 0.075 0.46 0.7746319 0.7609480 0.133031502 1458 398 4540 1368
347 0.075 0.47 0.7757644 0.7614632 0.115807148 1456 396 4542 1370
348 0.075 0.48 0.7774632 0.7630088 0.111079414 1447 393 4551 1373
349 0.075 0.49 0.7774632 0.7636528 0.127913827 1442 393 4556 1373
350 0.075 0.50 0.7780294 0.7642968 0.129757649 1438 392 4560 1374
351 0.075 0.51 0.7797282 0.7649408 0.101960699 1436 389 4562 1377
352 0.075 0.52 0.7797282 0.7653272 0.111213748 1433 389 4565 1377
353 0.075 0.53 0.7797282 0.7664863 0.143057866 1424 389 4574 1377
354 0.075 0.54 0.7802945 0.7667439 0.133625939 1423 388 4575 1378
355 0.075 0.55 0.7819932 0.7677743 0.114577379 1418 385 4580 1381
356 0.075 0.56 0.7836920 0.7689335 0.100608600 1412 382 4586 1384
357 0.075 0.57 0.7848245 0.7700927 0.100637291 1405 380 4593 1386
358 0.075 0.58 0.7859570 0.7713807 0.103670922 1397 378 4601 1388
359 0.075 0.59 0.7876557 0.7721535 0.082775926 1394 375 4604 1391
360 0.075 0.60 0.7882220 0.7727975 0.084048564 1390 374 4608 1392
361 0.075 0.61 0.7882220 0.7736991 0.103721480 1383 374 4615 1392
362 0.075 0.62 0.7882220 0.7749871 0.138101259 1373 374 4625 1392
363 0.075 0.63 0.7882220 0.7761463 0.176165025 1364 374 4634 1392
364 0.075 0.64 0.7887882 0.7769191 0.183319508 1359 373 4639 1393
365 0.075 0.65 0.7893545 0.7780783 0.205922976 1351 372 4647 1394
366 0.075 0.66 0.7904870 0.7791087 0.200991498 1345 370 4653 1396
367 0.075 0.67 0.7910532 0.7796239 0.198550751 1342 369 4656 1397
368 0.075 0.68 0.7916195 0.7803967 0.206424231 1337 368 4661 1398
369 0.075 0.69 0.7921857 0.7818135 0.242829301 1327 367 4671 1399
370 0.075 0.70 0.7933182 0.7821999 0.209436174 1326 365 4672 1401
371 0.075 0.71 0.7938845 0.7827151 0.206909321 1323 364 4675 1402
372 0.075 0.72 0.7938845 0.7831015 0.223187738 1320 364 4678 1402
373 0.075 0.73 0.7950170 0.7842607 0.223468125 1313 362 4685 1404
374 0.075 0.74 0.7950170 0.7852911 0.271551238 1305 362 4693 1404
375 0.075 0.75 0.7950170 0.7864503 0.333832026 1296 362 4702 1404
376 0.075 0.76 0.7961495 0.7883823 0.381002415 1283 360 4715 1406
377 0.075 0.77 0.7961495 0.7892839 0.440280432 1276 360 4722 1406
378 0.075 0.78 0.7972820 0.7904431 0.441212242 1269 358 4729 1408
379 0.075 0.79 0.7984145 0.7916023 0.442143372 1262 356 4736 1410
380 0.075 0.80 0.7989807 0.7931479 0.512408825 1251 355 4747 1411
381 0.075 0.81 0.7995470 0.7944359 0.567853179 1242 354 4756 1412
382 0.075 0.82 0.7995470 0.7953375 0.641713961 1235 354 4763 1412
383 0.075 0.83 0.8006795 0.7962391 0.621675892 1230 352 4768 1414
384 0.075 0.84 0.8018120 0.7972694 0.612469048 1224 350 4774 1416
385 0.075 0.85 0.8018120 0.7977846 0.655810207 1220 350 4778 1416
386 0.075 0.86 0.8035108 0.7994590 0.652709124 1210 347 4788 1419
387 0.075 0.87 0.8063420 0.8007470 0.524896953 1205 342 4793 1424
388 0.075 0.88 0.8086070 0.8015198 0.414718044 1203 338 4795 1428
389 0.075 0.89 0.8114383 0.8029366 0.323232770 1197 333 4801 1433
390 0.075 0.90 0.8131370 0.8037094 0.270998964 1194 330 4804 1436
391 0.075 0.91 0.8142695 0.8048686 0.271326032 1187 328 4811 1438
392 0.075 0.92 0.8176670 0.8080886 0.259090097 1168 322 4830 1444
393 0.075 0.93 0.8176670 0.8102782 0.386218009 1151 322 4847 1444
394 0.075 0.94 0.8193658 0.8129830 0.454503114 1133 319 4865 1447
395 0.075 0.95 0.8204983 0.8162030 0.620404912 1110 317 4888 1449
396 0.075 0.96 0.8227633 0.8201958 0.776093903 1083 313 4915 1453
397 0.075 0.97 0.8278596 0.8247038 0.717925349 1057 304 4941 1462
398 0.075 0.98 0.8352208 0.8297269 0.507445442 1031 291 4967 1475
399 0.075 0.99 0.8414496 0.8362957 0.529092534 991 280 5007 1486
400 0.075 1.00 1.0000000 1.0000000 0.000000000 0 0 5998 1766
401 0.100 0.01 0.6885797 0.6813498 0.423329736 1825 649 3855 1435
402 0.100 0.02 0.7005758 0.6926842 0.376076902 1762 624 3918 1460
403 0.100 0.03 0.7039347 0.6971922 0.450039310 1734 617 3946 1467
404 0.100 0.04 0.7111324 0.7028594 0.348148980 1705 602 3975 1482
405 0.100 0.05 0.7149712 0.7064657 0.332667303 1685 594 3995 1490
406 0.100 0.06 0.7188100 0.7102009 0.324821889 1664 586 4016 1498
407 0.100 0.07 0.7216891 0.7122617 0.278737882 1654 580 4026 1504
408 0.100 0.08 0.7226488 0.7145801 0.354856587 1638 578 4042 1506
409 0.100 0.09 0.7255278 0.7172849 0.342860790 1623 572 4057 1512
410 0.100 0.10 0.7274472 0.7206337 0.434246681 1601 568 4079 1516
411 0.100 0.11 0.7293666 0.7217929 0.382398766 1596 564 4084 1520
412 0.100 0.12 0.7308061 0.7237249 0.414166429 1584 561 4096 1523
413 0.100 0.13 0.7312860 0.7252705 0.489832662 1573 560 4107 1524
414 0.100 0.14 0.7332054 0.7269449 0.470761991 1564 556 4116 1528
415 0.100 0.15 0.7370441 0.7299073 0.407071753 1549 548 4131 1536
416 0.100 0.16 0.7389635 0.7314529 0.381261318 1541 544 4139 1540
417 0.100 0.17 0.7413628 0.7344152 0.417590051 1523 539 4157 1545
418 0.100 0.18 0.7418426 0.7349304 0.419760192 1520 538 4160 1546
419 0.100 0.19 0.7452015 0.7362184 0.289638908 1517 531 4163 1553
420 0.100 0.20 0.7476008 0.7373776 0.225967619 1513 526 4167 1558
421 0.100 0.21 0.7490403 0.7382792 0.201435961 1509 523 4171 1561
422 0.100 0.22 0.7500000 0.7390520 0.193119277 1505 521 4175 1563
423 0.100 0.23 0.7519194 0.7402112 0.162772136 1500 517 4180 1567
424 0.100 0.24 0.7533589 0.7411128 0.143485529 1496 514 4184 1570
425 0.100 0.25 0.7533589 0.7422720 0.185628741 1487 514 4193 1570
426 0.100 0.26 0.7533589 0.7434312 0.236447860 1478 514 4202 1570
427 0.100 0.27 0.7557582 0.7452344 0.207790258 1469 509 4211 1575
428 0.100 0.28 0.7567179 0.7465224 0.221900443 1461 507 4219 1577
429 0.100 0.29 0.7571977 0.7471664 0.229223980 1457 506 4223 1578
430 0.100 0.30 0.7581574 0.7479392 0.219990492 1453 504 4227 1580
431 0.100 0.31 0.7586372 0.7489696 0.245863506 1446 503 4234 1581
432 0.100 0.32 0.7610365 0.7506440 0.210400528 1438 498 4242 1586
433 0.100 0.33 0.7610365 0.7510304 0.228040714 1435 498 4245 1586
434 0.100 0.34 0.7624760 0.7520608 0.208512683 1430 495 4250 1589
435 0.100 0.35 0.7629559 0.7528336 0.221429079 1425 494 4255 1590
436 0.100 0.36 0.7629559 0.7536064 0.259190588 1419 494 4261 1590
437 0.100 0.37 0.7639155 0.7545080 0.255577851 1414 492 4266 1592
438 0.100 0.38 0.7658349 0.7552808 0.200383563 1412 488 4268 1596
439 0.100 0.39 0.7658349 0.7559248 0.229525902 1407 488 4273 1596
440 0.100 0.40 0.7658349 0.7563112 0.248423096 1404 488 4276 1596
441 0.100 0.41 0.7663148 0.7572128 0.269965751 1398 487 4282 1597
442 0.100 0.42 0.7691939 0.7587584 0.203410926 1392 481 4288 1603
443 0.100 0.43 0.7701536 0.7599176 0.211643607 1385 479 4295 1605
444 0.100 0.44 0.7711132 0.7604328 0.191702192 1383 477 4297 1607
445 0.100 0.45 0.7715931 0.7608192 0.187512271 1381 476 4299 1608
446 0.100 0.46 0.7715931 0.7609480 0.192882277 1380 476 4300 1608
447 0.100 0.47 0.7725528 0.7614632 0.174232456 1378 474 4302 1610
448 0.100 0.48 0.7744722 0.7630088 0.158927879 1370 470 4310 1614
449 0.100 0.49 0.7749520 0.7636528 0.164715067 1366 469 4314 1615
450 0.100 0.50 0.7759117 0.7642968 0.152603436 1363 467 4317 1617
451 0.100 0.51 0.7773512 0.7649408 0.125551063 1361 464 4319 1620
452 0.100 0.52 0.7773512 0.7653272 0.137783817 1358 464 4322 1620
453 0.100 0.53 0.7778311 0.7664863 0.161230829 1350 463 4330 1621
454 0.100 0.54 0.7783109 0.7667439 0.152851811 1349 462 4331 1622
455 0.100 0.55 0.7797505 0.7677743 0.137954308 1344 459 4336 1625
456 0.100 0.56 0.7816699 0.7689335 0.113580445 1339 455 4341 1629
457 0.100 0.57 0.7831094 0.7700927 0.105092224 1333 452 4347 1632
458 0.100 0.58 0.7845489 0.7713807 0.100359335 1326 449 4354 1635
459 0.100 0.59 0.7859885 0.7721535 0.083645521 1323 446 4357 1638
460 0.100 0.60 0.7864683 0.7727975 0.087130198 1319 445 4361 1639
461 0.100 0.61 0.7869482 0.7736991 0.097046432 1313 444 4367 1640
462 0.100 0.62 0.7874280 0.7749871 0.118898595 1304 443 4376 1641
463 0.100 0.63 0.7879079 0.7761463 0.140134546 1296 442 4384 1642
464 0.100 0.64 0.7883877 0.7769191 0.149994495 1291 441 4389 1643
465 0.100 0.65 0.7888676 0.7780783 0.175426042 1283 440 4397 1644
466 0.100 0.66 0.7898273 0.7791087 0.177614918 1277 438 4403 1646
467 0.100 0.67 0.7903071 0.7796239 0.178718257 1274 437 4406 1647
468 0.100 0.68 0.7907869 0.7803967 0.190656376 1269 436 4411 1648
469 0.100 0.69 0.7912668 0.7818135 0.233805574 1259 435 4421 1649
470 0.100 0.70 0.7922265 0.7821999 0.205689839 1258 433 4422 1651
471 0.100 0.71 0.7931862 0.7827151 0.185464783 1256 431 4424 1653
472 0.100 0.72 0.7931862 0.7831015 0.202334979 1253 431 4427 1653
473 0.100 0.73 0.7946257 0.7842607 0.188920732 1247 428 4433 1656
474 0.100 0.74 0.7946257 0.7852911 0.237153395 1239 428 4441 1656
475 0.100 0.75 0.7946257 0.7864503 0.301373473 1230 428 4450 1656
476 0.100 0.76 0.7955854 0.7883823 0.362887430 1217 426 4463 1658
477 0.100 0.77 0.7955854 0.7892839 0.427600017 1210 426 4470 1658
478 0.100 0.78 0.7965451 0.7904431 0.442041624 1203 424 4477 1660
479 0.100 0.79 0.7975048 0.7916023 0.456813217 1196 422 4484 1662
480 0.100 0.80 0.7979846 0.7931479 0.544700971 1185 421 4495 1663
481 0.100 0.81 0.7994242 0.7944359 0.530569889 1178 418 4502 1666
482 0.100 0.82 0.7999040 0.7953375 0.567073380 1172 417 4508 1667
483 0.100 0.83 0.8013436 0.7962391 0.519194507 1168 414 4512 1670
484 0.100 0.84 0.8027831 0.7972694 0.483851047 1163 411 4517 1673
485 0.100 0.85 0.8032630 0.7977846 0.486372853 1160 410 4520 1674
486 0.100 0.86 0.8051823 0.7994590 0.464832093 1151 406 4529 1678
487 0.100 0.87 0.8075816 0.8007470 0.378229103 1146 401 4534 1683
488 0.100 0.88 0.8095010 0.8015198 0.300257379 1144 397 4536 1687
489 0.100 0.89 0.8119002 0.8029366 0.241799718 1138 392 4542 1692
490 0.100 0.90 0.8133397 0.8037094 0.207009424 1135 389 4545 1695
491 0.100 0.91 0.8142994 0.8048686 0.215792496 1128 387 4552 1697
492 0.100 0.92 0.8171785 0.8080886 0.230356215 1109 381 4571 1703
493 0.100 0.93 0.8176583 0.8102782 0.331064989 1093 380 4587 1704
494 0.100 0.94 0.8195777 0.8129830 0.384388279 1076 376 4604 1708
495 0.100 0.95 0.8205374 0.8162030 0.572601497 1053 374 4627 1710
496 0.100 0.96 0.8238964 0.8201958 0.630538603 1029 367 4651 1717
497 0.100 0.97 0.8282150 0.8247038 0.646091356 1003 358 4677 1726
498 0.100 0.98 0.8349328 0.8297269 0.480718893 978 344 4702 1740
499 0.100 0.99 0.8416507 0.8362957 0.460618866 941 330 4739 1754
500 0.100 1.00 1.0000000 1.0000000 0.000000000 0 0 5680 2084enrichment.plotter(gene.hic.filt, "weighted_Z.s2post.C", "adj.P.Val", "FDR for Weighted p-val Combine of Hi-C Contacts Overlapping Gene, Chimp")
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC Dboth
1 0.010 0.01 0.7089716 0.6751510 0.12361082 2394 133 4928 324
2 0.010 0.02 0.7221007 0.6855637 0.09255552 2319 127 5003 330
3 0.010 0.03 0.7242888 0.6898059 0.11172806 2287 126 5035 331
4 0.010 0.04 0.7308534 0.6949479 0.09572256 2250 123 5072 334
5 0.010 0.05 0.7352298 0.6986759 0.08858915 2223 121 5099 336
6 0.010 0.06 0.7374179 0.7018897 0.09717341 2199 120 5123 337
7 0.010 0.07 0.7439825 0.7040751 0.06098030 2185 117 5137 340
8 0.010 0.08 0.7461707 0.7063890 0.06122877 2168 116 5154 341
9 0.010 0.09 0.7461707 0.7085744 0.07673085 2151 116 5171 341
10 0.010 0.10 0.7461707 0.7119167 0.10665563 2125 116 5197 341
11 0.010 0.11 0.7483589 0.7129451 0.09459925 2118 115 5204 342
12 0.010 0.12 0.7505470 0.7146163 0.08917371 2106 114 5216 343
13 0.010 0.13 0.7505470 0.7162874 0.10499692 2093 114 5229 343
14 0.010 0.14 0.7505470 0.7180872 0.12451738 2079 114 5243 343
15 0.010 0.15 0.7527352 0.7213009 0.13594355 2055 113 5267 344
16 0.010 0.16 0.7549234 0.7232292 0.13179570 2041 112 5281 345
17 0.010 0.17 0.7571116 0.7254146 0.13081349 2025 111 5297 346
18 0.010 0.18 0.7571116 0.7261859 0.14045174 2019 111 5303 346
19 0.010 0.19 0.7592998 0.7273428 0.12674004 2011 110 5311 347
20 0.010 0.20 0.7614880 0.7279856 0.10855188 2007 109 5315 348
21 0.010 0.21 0.7636761 0.7288855 0.09486715 2001 108 5321 349
22 0.010 0.22 0.7636761 0.7295282 0.10108278 1996 108 5326 349
23 0.010 0.23 0.7636761 0.7306852 0.11310836 1987 108 5335 349
24 0.010 0.24 0.7658643 0.7319707 0.10274689 1978 107 5344 350
25 0.010 0.25 0.7658643 0.7336419 0.12075207 1965 107 5357 350
26 0.010 0.26 0.7658643 0.7353130 0.14121622 1952 107 5370 350
27 0.010 0.27 0.7680525 0.7373698 0.13846190 1937 106 5385 351
28 0.010 0.28 0.7724289 0.7383983 0.09868395 1931 104 5391 353
29 0.010 0.29 0.7724289 0.7394267 0.10921155 1923 104 5399 353
30 0.010 0.30 0.7724289 0.7403265 0.11915691 1916 104 5406 353
31 0.010 0.31 0.7724289 0.7414835 0.13300449 1907 104 5415 353
32 0.010 0.32 0.7724289 0.7432832 0.15706666 1893 104 5429 353
33 0.010 0.33 0.7724289 0.7436689 0.16264224 1890 104 5432 353
34 0.010 0.34 0.7724289 0.7444402 0.17425559 1884 104 5438 353
35 0.010 0.35 0.7724289 0.7454686 0.19072519 1876 104 5446 353
36 0.010 0.36 0.7746171 0.7463684 0.16906596 1870 103 5452 354
37 0.010 0.37 0.7768053 0.7473968 0.15103825 1863 102 5459 355
38 0.010 0.38 0.7789934 0.7480396 0.12963973 1859 101 5463 356
39 0.010 0.39 0.7789934 0.7486823 0.13776681 1854 101 5468 356
40 0.010 0.40 0.7789934 0.7490680 0.14283471 1851 101 5471 356
41 0.010 0.41 0.7811816 0.7497108 0.12230304 1847 100 5475 357
42 0.010 0.42 0.7833698 0.7512534 0.11381483 1836 99 5486 358
43 0.010 0.43 0.7855580 0.7521532 0.09915889 1830 98 5492 359
44 0.010 0.44 0.7855580 0.7526674 0.10441123 1826 98 5496 359
45 0.010 0.45 0.7855580 0.7531816 0.10988927 1822 98 5500 359
46 0.010 0.46 0.7855580 0.7533102 0.11129479 1821 98 5501 359
47 0.010 0.47 0.7877462 0.7539530 0.09435448 1817 97 5505 360
48 0.010 0.48 0.7899344 0.7553670 0.08619879 1807 96 5515 361
49 0.010 0.49 0.7899344 0.7563954 0.09582150 1799 96 5523 361
50 0.010 0.50 0.7921225 0.7571667 0.08184848 1794 95 5528 362
51 0.010 0.51 0.7943107 0.7579380 0.06954985 1789 94 5533 363
52 0.010 0.52 0.7943107 0.7585808 0.07452442 1784 94 5538 363
53 0.010 0.53 0.7943107 0.7598663 0.08537372 1774 94 5548 363
54 0.010 0.54 0.7943107 0.7606376 0.09249357 1768 94 5554 363
55 0.010 0.55 0.7986871 0.7616660 0.06316611 1762 92 5560 365
56 0.010 0.56 0.7986871 0.7623088 0.06777884 1757 92 5565 365
57 0.010 0.57 0.8008753 0.7633372 0.05884761 1750 91 5572 366
58 0.010 0.58 0.8008753 0.7643656 0.06594766 1742 91 5580 366
59 0.010 0.59 0.8008753 0.7652655 0.07274389 1735 91 5587 366
60 0.010 0.60 0.8008753 0.7657797 0.07688613 1731 91 5591 366
61 0.010 0.61 0.8008753 0.7666795 0.08461165 1724 91 5598 366
62 0.010 0.62 0.8008753 0.7678365 0.09548620 1715 91 5607 366
63 0.010 0.63 0.8008753 0.7686078 0.10335961 1709 91 5613 366
64 0.010 0.64 0.8030635 0.7695076 0.08943089 1703 90 5619 367
65 0.010 0.65 0.8030635 0.7707932 0.10218492 1693 90 5629 367
66 0.010 0.66 0.8030635 0.7715645 0.11053160 1687 90 5635 367
67 0.010 0.67 0.8074398 0.7720787 0.07186967 1685 88 5637 369
68 0.010 0.68 0.8074398 0.7728500 0.07814817 1679 88 5643 369
69 0.010 0.69 0.8074398 0.7740069 0.08842556 1670 88 5652 369
70 0.010 0.70 0.8096280 0.7745211 0.07290871 1667 87 5655 370
71 0.010 0.71 0.8096280 0.7749068 0.07603936 1664 87 5658 370
72 0.010 0.72 0.8096280 0.7752925 0.07928245 1661 87 5661 370
73 0.010 0.73 0.8096280 0.7765780 0.09094264 1651 87 5671 370
74 0.010 0.74 0.8096280 0.7770922 0.09599032 1647 87 5675 370
75 0.010 0.75 0.8096280 0.7783777 0.10963103 1637 87 5685 370
76 0.010 0.76 0.8096280 0.7799203 0.12805462 1625 87 5697 370
77 0.010 0.77 0.8096280 0.7806916 0.13816345 1619 87 5703 370
78 0.010 0.78 0.8096280 0.7817200 0.15262581 1611 87 5711 370
79 0.010 0.79 0.8096280 0.7827484 0.16826329 1603 87 5719 370
80 0.010 0.80 0.8140044 0.7841625 0.12362738 1594 85 5728 372
81 0.010 0.81 0.8161926 0.7857051 0.11447236 1583 84 5739 373
82 0.010 0.82 0.8161926 0.7866050 0.12541647 1576 84 5746 373
83 0.010 0.83 0.8161926 0.7878905 0.14250608 1566 84 5756 373
84 0.010 0.84 0.8161926 0.7885332 0.15172436 1561 84 5761 373
85 0.010 0.85 0.8161926 0.7891760 0.16141034 1556 84 5766 373
86 0.010 0.86 0.8183807 0.7907186 0.15012449 1545 83 5777 374
87 0.010 0.87 0.8183807 0.7926469 0.18050419 1530 83 5792 374
88 0.010 0.88 0.8249453 0.7943180 0.10740274 1520 80 5802 377
89 0.010 0.89 0.8271335 0.7959892 0.10035589 1508 79 5814 378
90 0.010 0.90 0.8293217 0.7963749 0.08134783 1506 78 5816 379
91 0.010 0.91 0.8293217 0.7975318 0.09233990 1497 78 5825 379
92 0.010 0.92 0.8358862 0.8001028 0.05596867 1480 75 5842 382
93 0.010 0.93 0.8358862 0.8025453 0.07427123 1461 75 5861 382
94 0.010 0.94 0.8424508 0.8051163 0.04380494 1444 72 5878 385
95 0.010 0.95 0.8424508 0.8082016 0.06351742 1420 72 5902 385
96 0.010 0.96 0.8468271 0.8119296 0.05663638 1393 70 5929 387
97 0.010 0.97 0.8555799 0.8175858 0.03525628 1353 66 5969 391
98 0.010 0.98 0.8621444 0.8227279 0.02702824 1316 63 6006 394
99 0.010 0.99 0.8730853 0.8295411 0.01286596 1268 58 6054 399
100 0.010 1.00 1.0000000 1.0000000 0.00000000 0 0 7322 457
101 0.025 0.01 0.6880435 0.6751510 0.39435051 2240 287 4619 633
102 0.025 0.02 0.7010870 0.6855637 0.29733059 2171 275 4688 645
103 0.025 0.03 0.7054348 0.6898059 0.29214751 2142 271 4717 649
104 0.025 0.04 0.7130435 0.6949479 0.21818251 2109 264 4750 656
105 0.025 0.05 0.7195652 0.6986759 0.15204692 2086 258 4773 662
106 0.025 0.06 0.7217391 0.7018897 0.17278581 2063 256 4796 664
107 0.025 0.07 0.7250000 0.7040751 0.14921348 2049 253 4810 667
108 0.025 0.08 0.7282609 0.7063890 0.13033584 2034 250 4825 670
109 0.025 0.09 0.7304348 0.7085744 0.12970087 2019 248 4840 672
110 0.025 0.10 0.7304348 0.7119167 0.19981872 1993 248 4866 672
111 0.025 0.11 0.7326087 0.7129451 0.17218135 1987 246 4872 674
112 0.025 0.12 0.7347826 0.7146163 0.16044454 1976 244 4883 676
113 0.025 0.13 0.7358696 0.7162874 0.17250416 1964 243 4895 677
114 0.025 0.14 0.7369565 0.7180872 0.18828748 1951 242 4908 678
115 0.025 0.15 0.7402174 0.7213009 0.18561353 1929 239 4930 681
116 0.025 0.16 0.7413043 0.7232292 0.20560003 1915 238 4944 682
117 0.025 0.17 0.7423913 0.7254146 0.23429414 1899 237 4960 683
118 0.025 0.18 0.7423913 0.7261859 0.25656881 1893 237 4966 683
119 0.025 0.19 0.7434783 0.7273428 0.25807174 1885 236 4974 684
120 0.025 0.20 0.7445652 0.7279856 0.24440790 1881 235 4978 685
121 0.025 0.21 0.7456522 0.7288855 0.23845980 1875 234 4984 686
122 0.025 0.22 0.7456522 0.7295282 0.25722055 1870 234 4989 686
123 0.025 0.23 0.7467391 0.7306852 0.25872101 1862 233 4997 687
124 0.025 0.24 0.7500000 0.7319707 0.20224325 1855 230 5004 690
125 0.025 0.25 0.7500000 0.7336419 0.24784172 1842 230 5017 690
126 0.025 0.26 0.7500000 0.7353130 0.30040235 1829 230 5030 690
127 0.025 0.27 0.7510870 0.7373698 0.33355560 1814 229 5045 691
128 0.025 0.28 0.7532609 0.7383983 0.29262070 1808 227 5051 693
129 0.025 0.29 0.7543478 0.7394267 0.29003746 1801 226 5058 694
130 0.025 0.30 0.7565217 0.7403265 0.24887412 1796 224 5063 696
131 0.025 0.31 0.7576087 0.7414835 0.25030909 1788 223 5071 697
132 0.025 0.32 0.7586957 0.7432832 0.27154241 1775 222 5084 698
133 0.025 0.33 0.7586957 0.7436689 0.28393257 1772 222 5087 698
134 0.025 0.34 0.7586957 0.7444402 0.30988366 1766 222 5093 698
135 0.025 0.35 0.7586957 0.7454686 0.34693459 1758 222 5101 698
136 0.025 0.36 0.7597826 0.7463684 0.33930414 1752 221 5107 699
137 0.025 0.37 0.7608696 0.7473968 0.33646282 1745 220 5114 700
138 0.025 0.38 0.7619565 0.7480396 0.31971696 1741 219 5118 701
139 0.025 0.39 0.7619565 0.7486823 0.34311964 1736 219 5123 701
140 0.025 0.40 0.7619565 0.7490680 0.35769295 1733 219 5126 701
141 0.025 0.41 0.7630435 0.7497108 0.34024619 1729 218 5130 702
142 0.025 0.42 0.7663043 0.7512534 0.27834628 1720 215 5139 705
143 0.025 0.43 0.7673913 0.7521532 0.27161302 1714 214 5145 706
144 0.025 0.44 0.7684783 0.7526674 0.25303808 1711 213 5148 707
145 0.025 0.45 0.7684783 0.7531816 0.26903944 1707 213 5152 707
146 0.025 0.46 0.7684783 0.7533102 0.27314948 1706 213 5153 707
147 0.025 0.47 0.7695652 0.7539530 0.25842788 1702 212 5157 708
148 0.025 0.48 0.7706522 0.7553670 0.26797624 1692 211 5167 709
149 0.025 0.49 0.7706522 0.7563954 0.30210593 1684 211 5175 709
150 0.025 0.50 0.7717391 0.7571667 0.29059508 1679 210 5180 710
151 0.025 0.51 0.7739130 0.7579380 0.24453134 1675 208 5184 712
152 0.025 0.52 0.7739130 0.7585808 0.26430505 1670 208 5189 712
153 0.025 0.53 0.7739130 0.7598663 0.30720644 1660 208 5199 712
154 0.025 0.54 0.7739130 0.7606376 0.33512775 1654 208 5205 712
155 0.025 0.55 0.7760870 0.7616660 0.29275043 1648 206 5211 714
156 0.025 0.56 0.7771739 0.7623088 0.27712860 1644 205 5215 715
157 0.025 0.57 0.7782609 0.7633372 0.27445238 1637 204 5222 716
158 0.025 0.58 0.7793478 0.7643656 0.27178205 1630 203 5229 717
159 0.025 0.59 0.7804348 0.7652655 0.26498578 1624 202 5235 718
160 0.025 0.60 0.7804348 0.7657797 0.28178716 1620 202 5239 718
161 0.025 0.61 0.7815217 0.7666795 0.27481416 1614 201 5245 719
162 0.025 0.62 0.7815217 0.7678365 0.31469393 1605 201 5254 719
163 0.025 0.63 0.7815217 0.7686078 0.34337778 1599 201 5260 719
164 0.025 0.64 0.7826087 0.7695076 0.33546970 1593 200 5266 720
165 0.025 0.65 0.7836957 0.7707932 0.34222022 1584 199 5275 721
166 0.025 0.66 0.7847826 0.7715645 0.32946089 1579 198 5280 722
167 0.025 0.67 0.7869565 0.7720787 0.26969053 1577 196 5282 724
168 0.025 0.68 0.7880435 0.7728500 0.25871617 1572 195 5287 725
169 0.025 0.69 0.7891304 0.7740069 0.26013809 1564 194 5295 726
170 0.025 0.70 0.7902174 0.7745211 0.24150028 1561 193 5298 727
171 0.025 0.71 0.7902174 0.7749068 0.25340051 1558 193 5301 727
172 0.025 0.72 0.7902174 0.7752925 0.26571770 1555 193 5304 727
173 0.025 0.73 0.7902174 0.7765780 0.30983765 1545 193 5314 727
174 0.025 0.74 0.7902174 0.7770922 0.32882233 1541 193 5318 727
175 0.025 0.75 0.7902174 0.7783777 0.37965575 1531 193 5328 727
176 0.025 0.76 0.7902174 0.7799203 0.44698018 1519 193 5340 727
177 0.025 0.77 0.7902174 0.7806916 0.48317416 1513 193 5346 727
178 0.025 0.78 0.7913043 0.7817200 0.47958380 1506 192 5353 728
179 0.025 0.79 0.7913043 0.7827484 0.53025188 1498 192 5361 728
180 0.025 0.80 0.7945652 0.7841625 0.43886562 1490 189 5369 731
181 0.025 0.81 0.7956522 0.7857051 0.45914307 1479 188 5380 732
182 0.025 0.82 0.7956522 0.7866050 0.50257279 1472 188 5387 732
183 0.025 0.83 0.7967391 0.7878905 0.51167105 1463 187 5396 733
184 0.025 0.84 0.7967391 0.7885332 0.54443343 1458 187 5401 733
185 0.025 0.85 0.7967391 0.7891760 0.57828082 1453 187 5406 733
186 0.025 0.86 0.8000000 0.7907186 0.48778470 1444 184 5415 736
187 0.025 0.87 0.8010870 0.7926469 0.52923509 1430 183 5429 737
188 0.025 0.88 0.8076087 0.7943180 0.30834770 1423 177 5436 743
189 0.025 0.89 0.8119565 0.7959892 0.21633234 1414 173 5445 747
190 0.025 0.90 0.8130435 0.7963749 0.19585042 1412 172 5447 748
191 0.025 0.91 0.8152174 0.7975318 0.16821532 1405 170 5454 750
192 0.025 0.92 0.8195652 0.8001028 0.12649361 1389 166 5470 754
193 0.025 0.93 0.8195652 0.8025453 0.18123423 1370 166 5489 754
194 0.025 0.94 0.8239130 0.8051163 0.13661984 1354 162 5505 758
195 0.025 0.95 0.8250000 0.8082016 0.18233179 1331 161 5528 759
196 0.025 0.96 0.8304348 0.8119296 0.13760891 1307 156 5552 764
197 0.025 0.97 0.8380435 0.8175858 0.09577676 1270 149 5589 771
198 0.025 0.98 0.8434783 0.8227279 0.08742527 1235 144 5624 776
199 0.025 0.99 0.8532609 0.8295411 0.04649612 1191 135 5668 785
200 0.025 1.00 1.0000000 1.0000000 0.00000000 0 0 6859 920
201 0.050 0.01 0.6864710 0.6751510 0.33409404 2089 438 4293 959
202 0.050 0.02 0.6979241 0.6855637 0.28608353 2024 422 4358 975
203 0.050 0.03 0.7029349 0.6898059 0.25458853 1998 415 4384 982
204 0.050 0.04 0.7086614 0.6949479 0.23132066 1966 407 4416 990
205 0.050 0.05 0.7129563 0.6986759 0.21052798 1943 401 4439 996
206 0.050 0.06 0.7158196 0.7018897 0.22082336 1922 397 4460 1000
207 0.050 0.07 0.7186829 0.7040751 0.19766582 1909 393 4473 1004
208 0.050 0.08 0.7208304 0.7063890 0.20192250 1894 390 4488 1007
209 0.050 0.09 0.7222620 0.7085744 0.22610728 1879 388 4503 1009
210 0.050 0.10 0.7236936 0.7119167 0.29811342 1855 386 4527 1011
211 0.050 0.11 0.7258411 0.7129451 0.25275930 1850 383 4532 1014
212 0.050 0.12 0.7272727 0.7146163 0.26110308 1839 381 4543 1016
213 0.050 0.13 0.7287044 0.7162874 0.26965760 1828 379 4554 1018
214 0.050 0.14 0.7308518 0.7180872 0.25517331 1817 376 4565 1021
215 0.050 0.15 0.7351467 0.7213009 0.21447056 1798 370 4584 1027
216 0.050 0.16 0.7380100 0.7232292 0.18343284 1787 366 4595 1031
217 0.050 0.17 0.7394417 0.7254146 0.20628750 1772 364 4610 1033
218 0.050 0.18 0.7401575 0.7261859 0.20773736 1767 363 4615 1034
219 0.050 0.19 0.7408733 0.7273428 0.22223756 1759 362 4623 1035
220 0.050 0.20 0.7423049 0.7279856 0.19543916 1756 360 4626 1037
221 0.050 0.21 0.7437366 0.7288855 0.17850736 1751 358 4631 1039
222 0.050 0.22 0.7437366 0.7295282 0.19821303 1746 358 4636 1039
223 0.050 0.23 0.7458840 0.7306852 0.16742132 1740 355 4642 1042
224 0.050 0.24 0.7487473 0.7319707 0.12611141 1734 351 4648 1046
225 0.050 0.25 0.7487473 0.7336419 0.16861757 1721 351 4661 1046
226 0.050 0.26 0.7487473 0.7353130 0.22128665 1708 351 4674 1046
227 0.050 0.27 0.7501790 0.7373698 0.24298557 1694 349 4688 1048
228 0.050 0.28 0.7516106 0.7383983 0.22747316 1688 347 4694 1050
229 0.050 0.29 0.7530422 0.7394267 0.21264006 1682 345 4700 1052
230 0.050 0.30 0.7551897 0.7403265 0.17220448 1678 342 4704 1055
231 0.050 0.31 0.7559055 0.7414835 0.18498549 1670 341 4712 1056
232 0.050 0.32 0.7587688 0.7432832 0.15298764 1660 337 4722 1060
233 0.050 0.33 0.7587688 0.7436689 0.16352540 1657 337 4725 1060
234 0.050 0.34 0.7594846 0.7444402 0.16469880 1652 336 4730 1061
235 0.050 0.35 0.7602004 0.7454686 0.17330147 1645 335 4737 1062
236 0.050 0.36 0.7609162 0.7463684 0.17836446 1639 334 4743 1063
237 0.050 0.37 0.7623479 0.7473968 0.16577398 1633 332 4749 1065
238 0.050 0.38 0.7637795 0.7480396 0.14372335 1630 330 4752 1067
239 0.050 0.39 0.7644953 0.7486823 0.14149252 1626 329 4756 1068
240 0.050 0.40 0.7644953 0.7490680 0.15148669 1623 329 4759 1068
241 0.050 0.41 0.7652112 0.7497108 0.14916567 1619 328 4763 1069
242 0.050 0.42 0.7687903 0.7512534 0.10103390 1612 323 4770 1074
243 0.050 0.43 0.7695061 0.7521532 0.10431951 1606 322 4776 1075
244 0.050 0.44 0.7702219 0.7526674 0.10003617 1603 321 4779 1076
245 0.050 0.45 0.7702219 0.7531816 0.11035142 1599 321 4783 1076
246 0.050 0.46 0.7702219 0.7533102 0.11305955 1598 321 4784 1076
247 0.050 0.47 0.7716535 0.7539530 0.09660045 1595 319 4787 1078
248 0.050 0.48 0.7723694 0.7553670 0.11009411 1585 318 4797 1079
249 0.050 0.49 0.7723694 0.7563954 0.13330689 1577 318 4805 1079
250 0.050 0.50 0.7730852 0.7571667 0.13427242 1572 317 4810 1080
251 0.050 0.51 0.7752326 0.7579380 0.10275072 1569 314 4813 1083
252 0.050 0.52 0.7752326 0.7585808 0.11614508 1564 314 4818 1083
253 0.050 0.53 0.7752326 0.7598663 0.14710167 1554 314 4828 1083
254 0.050 0.54 0.7759485 0.7606376 0.14815568 1549 313 4833 1084
255 0.050 0.55 0.7773801 0.7616660 0.13694405 1543 311 4839 1086
256 0.050 0.56 0.7788117 0.7623088 0.11755359 1540 309 4842 1088
257 0.050 0.57 0.7795276 0.7633372 0.12426472 1533 308 4849 1089
258 0.050 0.58 0.7802434 0.7643656 0.13128938 1526 307 4856 1090
259 0.050 0.59 0.7816750 0.7652655 0.11809676 1521 305 4861 1092
260 0.050 0.60 0.7823908 0.7657797 0.11327433 1518 304 4864 1093
261 0.050 0.61 0.7838225 0.7666795 0.10149492 1513 302 4869 1095
262 0.050 0.62 0.7838225 0.7678365 0.12665871 1504 302 4878 1095
263 0.050 0.63 0.7838225 0.7686078 0.14602510 1498 302 4884 1095
264 0.050 0.64 0.7845383 0.7695076 0.15052874 1492 301 4890 1096
265 0.050 0.65 0.7852541 0.7707932 0.16618696 1483 300 4899 1097
266 0.050 0.66 0.7866858 0.7715645 0.14675005 1479 298 4903 1099
267 0.050 0.67 0.7881174 0.7720787 0.12295060 1477 296 4905 1101
268 0.050 0.68 0.7888332 0.7728500 0.12383190 1472 295 4910 1102
269 0.050 0.69 0.7895490 0.7740069 0.13409624 1464 294 4918 1103
270 0.050 0.70 0.7902649 0.7745211 0.12869704 1461 293 4921 1104
271 0.050 0.71 0.7909807 0.7749068 0.12046987 1459 292 4923 1105
272 0.050 0.72 0.7909807 0.7752925 0.12961488 1456 292 4926 1105
273 0.050 0.73 0.7916965 0.7765780 0.14366440 1447 291 4935 1106
274 0.050 0.74 0.7916965 0.7770922 0.15780299 1443 291 4939 1106
275 0.050 0.75 0.7916965 0.7783777 0.19785022 1433 291 4949 1106
276 0.050 0.76 0.7931281 0.7799203 0.20058954 1423 289 4959 1108
277 0.050 0.77 0.7931281 0.7806916 0.22836993 1417 289 4965 1108
278 0.050 0.78 0.7945598 0.7817200 0.21243806 1411 287 4971 1110
279 0.050 0.79 0.7952756 0.7827484 0.22332433 1404 286 4978 1111
280 0.050 0.80 0.7974230 0.7841625 0.19560381 1396 283 4986 1114
281 0.050 0.81 0.7981389 0.7857051 0.22459273 1385 282 4997 1115
282 0.050 0.82 0.7981389 0.7866050 0.26032018 1378 282 5004 1115
283 0.050 0.83 0.8010021 0.7878905 0.19796159 1372 278 5010 1119
284 0.050 0.84 0.8010021 0.7885332 0.22100596 1367 278 5015 1119
285 0.050 0.85 0.8010021 0.7891760 0.24596842 1362 278 5020 1119
286 0.050 0.86 0.8031496 0.7907186 0.22069433 1353 275 5029 1122
287 0.050 0.87 0.8052971 0.7926469 0.21086948 1341 272 5041 1125
288 0.050 0.88 0.8095920 0.7943180 0.12781141 1334 266 5048 1131
289 0.050 0.89 0.8131711 0.7959892 0.08492819 1326 261 5056 1136
290 0.050 0.90 0.8146027 0.7963749 0.06707391 1325 259 5057 1138
291 0.050 0.91 0.8160344 0.7975318 0.06242327 1318 257 5064 1140
292 0.050 0.92 0.8188976 0.8001028 0.05712390 1302 253 5080 1144
293 0.050 0.93 0.8188976 0.8025453 0.09731979 1283 253 5099 1144
294 0.050 0.94 0.8224767 0.8051163 0.07652100 1268 248 5114 1149
295 0.050 0.95 0.8239084 0.8082016 0.10768080 1246 246 5136 1151
296 0.050 0.96 0.8282033 0.8119296 0.09282022 1223 240 5159 1157
297 0.050 0.97 0.8339298 0.8175858 0.08760626 1187 232 5195 1165
298 0.050 0.98 0.8389406 0.8227279 0.08668548 1154 225 5228 1172
299 0.050 0.99 0.8468146 0.8295411 0.06341529 1112 214 5270 1183
300 0.050 1.00 1.0000000 1.0000000 0.00000000 0 0 6382 1397
301 0.075 0.01 0.6847458 0.6751510 0.34118268 1969 558 4040 1212
302 0.075 0.02 0.6966102 0.6855637 0.26710109 1909 537 4100 1233
303 0.075 0.03 0.7005650 0.6898059 0.27830039 1883 530 4126 1240
304 0.075 0.04 0.7067797 0.6949479 0.22986423 1854 519 4155 1251
305 0.075 0.05 0.7101695 0.6986759 0.24215891 1831 513 4178 1257
306 0.075 0.06 0.7124294 0.7018897 0.28310134 1810 509 4199 1261
307 0.075 0.07 0.7152542 0.7040751 0.25315330 1798 504 4211 1266
308 0.075 0.08 0.7169492 0.7063890 0.28002011 1783 501 4226 1269
309 0.075 0.09 0.7180791 0.7085744 0.33131651 1768 499 4241 1271
310 0.075 0.10 0.7197740 0.7119167 0.42333027 1745 496 4264 1274
311 0.075 0.11 0.7214689 0.7129451 0.38318886 1740 493 4269 1277
312 0.075 0.12 0.7225989 0.7146163 0.41438932 1729 491 4280 1279
313 0.075 0.13 0.7237288 0.7162874 0.44715375 1718 489 4291 1281
314 0.075 0.14 0.7254237 0.7180872 0.45296256 1707 486 4302 1284
315 0.075 0.15 0.7288136 0.7213009 0.44016652 1688 480 4321 1290
316 0.075 0.16 0.7322034 0.7232292 0.35240330 1679 474 4330 1296
317 0.075 0.17 0.7333333 0.7254146 0.41277254 1664 472 4345 1298
318 0.075 0.18 0.7344633 0.7261859 0.39075973 1660 470 4349 1300
319 0.075 0.19 0.7350282 0.7273428 0.42618205 1652 469 4357 1301
320 0.075 0.20 0.7361582 0.7279856 0.39602802 1649 467 4360 1303
321 0.075 0.21 0.7372881 0.7288855 0.38190336 1644 465 4365 1305
322 0.075 0.22 0.7384181 0.7295282 0.35364233 1641 463 4368 1307
323 0.075 0.23 0.7412429 0.7306852 0.26752492 1637 458 4372 1312
324 0.075 0.24 0.7435028 0.7319707 0.22407579 1631 454 4378 1316
325 0.075 0.25 0.7435028 0.7336419 0.29963533 1618 454 4391 1316
326 0.075 0.26 0.7440678 0.7353130 0.35795143 1606 453 4403 1317
327 0.075 0.27 0.7457627 0.7373698 0.37766119 1593 450 4416 1320
328 0.075 0.28 0.7468927 0.7383983 0.37111390 1587 448 4422 1322
329 0.075 0.29 0.7480226 0.7394267 0.36461778 1581 446 4428 1324
330 0.075 0.30 0.7502825 0.7403265 0.29092291 1578 442 4431 1328
331 0.075 0.31 0.7508475 0.7414835 0.32075406 1570 441 4439 1329
332 0.075 0.32 0.7542373 0.7432832 0.24223197 1562 435 4447 1335
333 0.075 0.33 0.7548023 0.7436689 0.23417929 1560 434 4449 1336
334 0.075 0.34 0.7559322 0.7444402 0.21862330 1556 432 4453 1338
335 0.075 0.35 0.7564972 0.7454686 0.23764180 1549 431 4460 1339
336 0.075 0.36 0.7570621 0.7463684 0.25202807 1543 430 4466 1340
337 0.075 0.37 0.7581921 0.7473968 0.24679780 1537 428 4472 1342
338 0.075 0.38 0.7593220 0.7480396 0.22518423 1534 426 4475 1344
339 0.075 0.39 0.7598870 0.7486823 0.22808655 1530 425 4479 1345
340 0.075 0.40 0.7604520 0.7490680 0.22030545 1528 424 4481 1346
341 0.075 0.41 0.7610169 0.7497108 0.22316036 1524 423 4485 1347
342 0.075 0.42 0.7644068 0.7512534 0.15409053 1518 417 4491 1353
343 0.075 0.43 0.7649718 0.7521532 0.16457948 1512 416 4497 1354
344 0.075 0.44 0.7655367 0.7526674 0.16258375 1509 415 4500 1355
345 0.075 0.45 0.7661017 0.7531816 0.16060278 1506 414 4503 1356
346 0.075 0.46 0.7666667 0.7533102 0.14657011 1506 413 4503 1357
347 0.075 0.47 0.7677966 0.7539530 0.13176597 1503 411 4506 1359
348 0.075 0.48 0.7689266 0.7553670 0.13928192 1494 409 4515 1361
349 0.075 0.49 0.7689266 0.7563954 0.17197098 1486 409 4523 1361
350 0.075 0.50 0.7694915 0.7571667 0.17884027 1481 408 4528 1362
351 0.075 0.51 0.7711864 0.7579380 0.14733560 1478 405 4531 1365
352 0.075 0.52 0.7711864 0.7585808 0.16807255 1473 405 4536 1365
353 0.075 0.53 0.7711864 0.7598663 0.21613032 1463 405 4546 1365
354 0.075 0.54 0.7717514 0.7606376 0.22432835 1458 404 4551 1366
355 0.075 0.55 0.7728814 0.7616660 0.21933338 1452 402 4557 1368
356 0.075 0.56 0.7740113 0.7623088 0.19906154 1449 400 4560 1370
357 0.075 0.57 0.7745763 0.7633372 0.21721914 1442 399 4567 1371
358 0.075 0.58 0.7751412 0.7643656 0.23659439 1435 398 4574 1372
359 0.075 0.59 0.7768362 0.7652655 0.20234259 1431 395 4578 1375
360 0.075 0.60 0.7774011 0.7657797 0.19997811 1428 394 4581 1376
361 0.075 0.61 0.7785311 0.7666795 0.19040909 1423 392 4586 1378
362 0.075 0.62 0.7785311 0.7678365 0.23781373 1414 392 4595 1378
363 0.075 0.63 0.7785311 0.7686078 0.27382650 1408 392 4601 1378
364 0.075 0.64 0.7796610 0.7695076 0.26188902 1403 390 4606 1380
365 0.075 0.65 0.7802260 0.7707932 0.29737352 1394 389 4615 1381
366 0.075 0.66 0.7813559 0.7715645 0.27827180 1390 387 4619 1383
367 0.075 0.67 0.7824859 0.7720787 0.24795425 1388 385 4621 1385
368 0.075 0.68 0.7836158 0.7728500 0.23103859 1384 383 4625 1387
369 0.075 0.69 0.7841808 0.7740069 0.25759371 1376 382 4633 1388
370 0.075 0.70 0.7847458 0.7745211 0.25477580 1373 381 4636 1389
371 0.075 0.71 0.7853107 0.7749068 0.24602036 1371 380 4638 1390
372 0.075 0.72 0.7853107 0.7752925 0.26418784 1368 380 4641 1390
373 0.075 0.73 0.7864407 0.7765780 0.27091838 1360 378 4649 1392
374 0.075 0.74 0.7864407 0.7770922 0.29708077 1356 378 4653 1392
375 0.075 0.75 0.7864407 0.7783777 0.36987208 1346 378 4663 1392
376 0.075 0.76 0.7875706 0.7799203 0.39461393 1336 376 4673 1394
377 0.075 0.77 0.7875706 0.7806916 0.44538896 1330 376 4679 1394
378 0.075 0.78 0.7892655 0.7817200 0.39998229 1325 373 4684 1397
379 0.075 0.79 0.7903955 0.7827484 0.39262082 1319 371 4690 1399
380 0.075 0.80 0.7920904 0.7841625 0.37369441 1311 368 4698 1402
381 0.075 0.81 0.7926554 0.7857051 0.43666063 1300 367 4709 1403
382 0.075 0.82 0.7926554 0.7866050 0.50037427 1293 367 4716 1403
383 0.075 0.83 0.7949153 0.7878905 0.42983050 1287 363 4722 1407
384 0.075 0.84 0.7954802 0.7885332 0.43466127 1283 362 4726 1408
385 0.075 0.85 0.7954802 0.7891760 0.47976473 1278 362 4731 1408
386 0.075 0.86 0.7971751 0.7907186 0.46752623 1269 359 4740 1411
387 0.075 0.87 0.8000000 0.7926469 0.40379915 1259 354 4750 1416
388 0.075 0.88 0.8033898 0.7943180 0.29792439 1252 348 4757 1422
389 0.075 0.89 0.8067797 0.7959892 0.21195568 1245 342 4764 1428
390 0.075 0.90 0.8079096 0.7963749 0.18104036 1244 340 4765 1430
391 0.075 0.91 0.8090395 0.7975318 0.18116288 1237 338 4772 1432
392 0.075 0.92 0.8118644 0.8001028 0.16944999 1222 333 4787 1437
393 0.075 0.93 0.8118644 0.8025453 0.27719749 1203 333 4806 1437
394 0.075 0.94 0.8146893 0.8051163 0.26156166 1188 328 4821 1442
395 0.075 0.95 0.8158192 0.8082016 0.37249258 1166 326 4843 1444
396 0.075 0.96 0.8203390 0.8119296 0.31947763 1145 318 4864 1452
397 0.075 0.97 0.8276836 0.8175858 0.22374773 1114 305 4895 1465
398 0.075 0.98 0.8333333 0.8227279 0.19569597 1084 295 4925 1475
399 0.075 0.99 0.8406780 0.8295411 0.16705310 1044 282 4965 1488
400 0.075 1.00 1.0000000 1.0000000 0.00000000 0 0 6009 1770
401 0.100 0.01 0.6883365 0.6751510 0.13919221 1875 652 3812 1440
402 0.100 0.02 0.6998088 0.6855637 0.10658844 1818 628 3869 1464
403 0.100 0.03 0.7036329 0.6898059 0.11609860 1793 620 3894 1472
404 0.100 0.04 0.7108031 0.6949479 0.06963030 1768 605 3919 1487
405 0.100 0.05 0.7146272 0.6986759 0.06697752 1747 597 3940 1495
406 0.100 0.06 0.7170172 0.7018897 0.08166225 1727 592 3960 1500
407 0.100 0.07 0.7194073 0.7040751 0.07692518 1715 587 3972 1505
408 0.100 0.08 0.7213193 0.7063890 0.08440983 1701 583 3986 1509
409 0.100 0.09 0.7227533 0.7085744 0.10079907 1687 580 4000 1512
410 0.100 0.10 0.7246654 0.7119167 0.13949933 1665 576 4022 1516
411 0.100 0.11 0.7260994 0.7129451 0.12671166 1660 573 4027 1519
412 0.100 0.12 0.7270554 0.7146163 0.14841297 1649 571 4038 1521
413 0.100 0.13 0.7280115 0.7162874 0.17292773 1638 569 4049 1523
414 0.100 0.14 0.7299235 0.7180872 0.16794393 1628 565 4059 1527
415 0.100 0.15 0.7332696 0.7213009 0.16167525 1610 558 4077 1534
416 0.100 0.16 0.7361377 0.7232292 0.12981808 1601 552 4086 1540
417 0.100 0.17 0.7370937 0.7254146 0.17031409 1586 550 4101 1542
418 0.100 0.18 0.7380497 0.7261859 0.16315010 1582 548 4105 1544
419 0.100 0.19 0.7390057 0.7273428 0.16998205 1575 546 4112 1546
420 0.100 0.20 0.7399618 0.7279856 0.15826430 1572 544 4115 1548
421 0.100 0.21 0.7409178 0.7288855 0.15585274 1567 542 4120 1550
422 0.100 0.22 0.7418738 0.7295282 0.14485617 1564 540 4123 1552
423 0.100 0.23 0.7442639 0.7306852 0.10770129 1560 535 4127 1557
424 0.100 0.24 0.7461759 0.7319707 0.09165813 1554 531 4133 1561
425 0.100 0.25 0.7461759 0.7336419 0.13679309 1541 531 4146 1561
426 0.100 0.26 0.7466539 0.7353130 0.17825192 1529 530 4158 1562
427 0.100 0.27 0.7490440 0.7373698 0.16451681 1518 525 4169 1567
428 0.100 0.28 0.7500000 0.7383983 0.16666833 1512 523 4175 1569
429 0.100 0.29 0.7509560 0.7394267 0.16884345 1506 521 4181 1571
430 0.100 0.30 0.7528681 0.7403265 0.13336463 1503 517 4184 1575
431 0.100 0.31 0.7533461 0.7414835 0.15555220 1495 516 4192 1576
432 0.100 0.32 0.7562141 0.7432832 0.12012128 1487 510 4200 1582
433 0.100 0.33 0.7566922 0.7436689 0.11726766 1485 509 4202 1583
434 0.100 0.34 0.7576482 0.7444402 0.11171114 1481 507 4206 1585
435 0.100 0.35 0.7581262 0.7454686 0.12724226 1474 506 4213 1586
436 0.100 0.36 0.7586042 0.7463684 0.14021689 1468 505 4219 1587
437 0.100 0.37 0.7595602 0.7473968 0.14208947 1462 503 4225 1589
438 0.100 0.38 0.7609943 0.7480396 0.11716300 1460 500 4227 1592
439 0.100 0.39 0.7614723 0.7486823 0.12166915 1456 499 4231 1593
440 0.100 0.40 0.7619503 0.7490680 0.11876197 1454 498 4233 1594
441 0.100 0.41 0.7624283 0.7497108 0.12332194 1450 497 4237 1595
442 0.100 0.42 0.7657744 0.7512534 0.07717255 1445 490 4242 1602
443 0.100 0.43 0.7667304 0.7521532 0.07566091 1440 488 4247 1604
444 0.100 0.44 0.7672084 0.7526674 0.07619759 1437 487 4250 1605
445 0.100 0.45 0.7681644 0.7531816 0.06736310 1435 485 4252 1607
446 0.100 0.46 0.7686424 0.7533102 0.06107821 1435 484 4252 1608
447 0.100 0.47 0.7695985 0.7539530 0.05568642 1432 482 4255 1610
448 0.100 0.48 0.7710325 0.7553670 0.05489680 1424 479 4263 1613
449 0.100 0.49 0.7715105 0.7563954 0.06376181 1417 478 4270 1614
450 0.100 0.50 0.7724665 0.7571667 0.06026069 1413 476 4274 1616
451 0.100 0.51 0.7739006 0.7579380 0.04956705 1410 473 4277 1619
452 0.100 0.52 0.7739006 0.7585808 0.05941429 1405 473 4282 1619
453 0.100 0.53 0.7743786 0.7598663 0.07386769 1396 472 4291 1620
454 0.100 0.54 0.7748566 0.7606376 0.07966521 1391 471 4296 1621
455 0.100 0.55 0.7758126 0.7616660 0.08078757 1385 469 4302 1623
456 0.100 0.56 0.7767686 0.7623088 0.07391735 1382 467 4305 1625
457 0.100 0.57 0.7777247 0.7633372 0.07496375 1376 465 4311 1627
458 0.100 0.58 0.7786807 0.7643656 0.07602302 1370 463 4317 1629
459 0.100 0.59 0.7801147 0.7652655 0.06518095 1366 460 4321 1632
460 0.100 0.60 0.7805927 0.7657797 0.06564404 1363 459 4324 1633
461 0.100 0.61 0.7820268 0.7666795 0.05602100 1359 456 4328 1636
462 0.100 0.62 0.7825048 0.7678365 0.06752519 1351 455 4336 1637
463 0.100 0.63 0.7825048 0.7686078 0.08319329 1345 455 4342 1637
464 0.100 0.64 0.7834608 0.7695076 0.08151751 1340 453 4347 1639
465 0.100 0.65 0.7839388 0.7707932 0.10046569 1331 452 4356 1640
466 0.100 0.66 0.7848948 0.7715645 0.09529895 1327 450 4360 1642
467 0.100 0.67 0.7858509 0.7720787 0.08439249 1325 448 4362 1644
468 0.100 0.68 0.7868069 0.7728500 0.07987751 1321 446 4366 1646
469 0.100 0.69 0.7872849 0.7740069 0.09537092 1313 445 4374 1647
470 0.100 0.70 0.7877629 0.7745211 0.09602548 1310 444 4377 1648
471 0.100 0.71 0.7882409 0.7749068 0.09348863 1308 443 4379 1649
472 0.100 0.72 0.7882409 0.7752925 0.10334045 1305 443 4382 1649
473 0.100 0.73 0.7891969 0.7765780 0.11186121 1297 441 4390 1651
474 0.100 0.74 0.7891969 0.7770922 0.12723454 1293 441 4394 1651
475 0.100 0.75 0.7891969 0.7783777 0.17298211 1283 441 4404 1651
476 0.100 0.76 0.7901530 0.7799203 0.19692603 1273 439 4414 1653
477 0.100 0.77 0.7901530 0.7806916 0.23315451 1267 439 4420 1653
478 0.100 0.78 0.7915870 0.7817200 0.21246370 1262 436 4425 1656
479 0.100 0.79 0.7925430 0.7827484 0.21513977 1256 434 4431 1658
480 0.100 0.80 0.7939771 0.7841625 0.21310067 1248 431 4439 1661
481 0.100 0.81 0.7954111 0.7857051 0.21713754 1239 428 4448 1664
482 0.100 0.82 0.7958891 0.7866050 0.23760737 1233 427 4454 1665
483 0.100 0.83 0.7982792 0.7878905 0.18413536 1228 422 4459 1670
484 0.100 0.84 0.7992352 0.7885332 0.17048661 1225 420 4462 1672
485 0.100 0.85 0.7997132 0.7891760 0.17683481 1221 419 4466 1673
486 0.100 0.86 0.8016252 0.7907186 0.16067930 1213 415 4474 1677
487 0.100 0.87 0.8040153 0.7926469 0.14196428 1203 410 4484 1682
488 0.100 0.88 0.8068834 0.7943180 0.10282265 1196 404 4491 1688
489 0.100 0.89 0.8097514 0.7959892 0.07262946 1189 398 4498 1694
490 0.100 0.90 0.8107075 0.7963749 0.06118120 1188 396 4499 1696
491 0.100 0.91 0.8116635 0.7975318 0.06439811 1181 394 4506 1698
492 0.100 0.92 0.8140535 0.8001028 0.06664361 1166 389 4521 1703
493 0.100 0.93 0.8140535 0.8025453 0.12993701 1147 389 4540 1703
494 0.100 0.94 0.8164436 0.8051163 0.13428091 1132 384 4555 1708
495 0.100 0.95 0.8173996 0.8082016 0.22350847 1110 382 4577 1710
496 0.100 0.96 0.8217017 0.8119296 0.19188458 1090 373 4597 1719
497 0.100 0.97 0.8283939 0.8175858 0.14319327 1060 359 4627 1733
498 0.100 0.98 0.8331740 0.8227279 0.15279214 1030 349 4657 1743
499 0.100 0.99 0.8408222 0.8295411 0.11622647 993 333 4694 1759
500 0.100 1.00 1.0000000 1.0000000 0.00000000 0 0 5687 2092#FIGS19C/D
enrichment.plotter(gene.hic.filt, "weighted_Z.ALLvar.H", "adj.P.Val", "FDR for Weighted p-val Combine of Hi-C Contacts Overlapping Gene", xmax=1) #FIGS19C/DWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.11276596 0.0951829 0.208016631 6608 417 686
2 0.010 0.02 0.14042553 0.1209428 0.206411215 6421 404 873
3 0.010 0.03 0.17659574 0.1448995 0.051598443 6252 387 1042
4 0.010 0.04 0.19574468 0.1664091 0.089537761 6094 378 1200
5 0.010 0.05 0.21914894 0.1846986 0.054308116 5963 367 1331
6 0.010 0.06 0.24255319 0.2005410 0.022171252 5851 356 1443
7 0.010 0.07 0.25531915 0.2153529 0.034279757 5742 350 1552
8 0.010 0.08 0.28085106 0.2315817 0.010589334 5628 338 1666
9 0.010 0.09 0.29787234 0.2461360 0.008509428 5523 330 1771
10 0.010 0.10 0.31914894 0.2592736 0.002685010 5431 320 1863
11 0.010 0.11 0.33404255 0.2753735 0.003921862 5313 313 1981
12 0.010 0.12 0.34893617 0.2881247 0.003169677 5221 306 2073
13 0.010 0.13 0.35744681 0.2982998 0.004517071 5146 302 2148
14 0.010 0.14 0.37021277 0.3082174 0.003162717 5075 296 2219
15 0.010 0.15 0.37659574 0.3212262 0.009287748 4977 293 2317
16 0.010 0.16 0.39148936 0.3323029 0.005781496 4898 286 2396
17 0.010 0.17 0.40851064 0.3444101 0.003005156 4812 278 2482
18 0.010 0.18 0.41489362 0.3563885 0.007304740 4722 275 2572
19 0.010 0.19 0.42553191 0.3633436 0.004475023 4673 270 2621
20 0.010 0.20 0.43829787 0.3733900 0.003154950 4601 264 2693
21 0.010 0.21 0.45319149 0.3858836 0.002336846 4511 257 2783
22 0.010 0.22 0.45319149 0.3956723 0.009813923 4435 257 2859
23 0.010 0.23 0.46170213 0.4045595 0.010599737 4370 253 2924
24 0.010 0.24 0.46808511 0.4149923 0.018183675 4292 250 3002
25 0.010 0.25 0.48085106 0.4234930 0.010825587 4232 244 3062
26 0.010 0.26 0.48297872 0.4328954 0.026907126 4160 243 3134
27 0.010 0.27 0.49148936 0.4434570 0.034458809 4082 239 3212
28 0.010 0.28 0.50425532 0.4545337 0.028836375 4002 233 3292
29 0.010 0.29 0.50851064 0.4645801 0.054551271 3926 231 3368
30 0.010 0.30 0.51702128 0.4733385 0.056231450 3862 227 3432
31 0.010 0.31 0.52127660 0.4841577 0.106591703 3780 225 3514
32 0.010 0.32 0.52553191 0.4945904 0.181343725 3701 223 3593
33 0.010 0.33 0.54680851 0.5038640 0.060992266 3639 213 3655
34 0.010 0.34 0.55531915 0.5117208 0.057005510 3582 209 3712
35 0.010 0.35 0.56170213 0.5204791 0.072178080 3517 206 3777
36 0.010 0.36 0.56808511 0.5278207 0.079033219 3463 203 3831
37 0.010 0.37 0.57446809 0.5350335 0.085303249 3410 200 3884
38 0.010 0.38 0.58085106 0.5421175 0.090811207 3358 197 3936
39 0.010 0.39 0.59574468 0.5508758 0.048861073 3297 190 3997
40 0.010 0.40 0.60000000 0.5580886 0.065806877 3243 188 4051
41 0.010 0.41 0.60638298 0.5659454 0.075589229 3185 185 4109
42 0.010 0.42 0.61063830 0.5739310 0.106918037 3125 183 4169
43 0.010 0.43 0.61914894 0.5829469 0.110944677 3059 179 4235
44 0.010 0.44 0.63191489 0.5914477 0.072986300 2999 173 4295
45 0.010 0.45 0.64468085 0.5999485 0.046183471 2939 167 4355
46 0.010 0.46 0.65531915 0.6079341 0.033822951 2882 162 4412
47 0.010 0.47 0.65957447 0.6159196 0.050155466 2822 160 4472
48 0.010 0.48 0.66595745 0.6239052 0.058409274 2763 157 4531
49 0.010 0.49 0.68085106 0.6322772 0.027535505 2705 150 4589
50 0.010 0.50 0.68723404 0.6385884 0.026737353 2659 147 4635
51 0.010 0.51 0.69361702 0.6455435 0.027940455 2608 144 4686
52 0.010 0.52 0.70851064 0.6534003 0.011078035 2554 137 4740
53 0.010 0.53 0.71702128 0.6625451 0.011517537 2487 133 4807
54 0.010 0.54 0.72127660 0.6700155 0.016949030 2431 131 4863
55 0.010 0.55 0.73617021 0.6789026 0.007092339 2369 124 4925
56 0.010 0.56 0.74042553 0.6863730 0.010634161 2313 122 4981
57 0.010 0.57 0.75106383 0.6946162 0.007152631 2254 117 5040
58 0.010 0.58 0.75957447 0.7022154 0.005895098 2199 113 5095
59 0.010 0.59 0.76595745 0.7108449 0.007664621 2135 110 5159
60 0.010 0.60 0.77021277 0.7174137 0.010169555 2086 108 5208
61 0.010 0.61 0.78723404 0.7246265 0.002059002 2038 100 5256
62 0.010 0.62 0.79148936 0.7317105 0.003036096 1985 98 5309
63 0.010 0.63 0.79787234 0.7396960 0.003600378 1926 95 5368
64 0.010 0.64 0.80425532 0.7465224 0.003570278 1876 92 5418
65 0.010 0.65 0.81489362 0.7545080 0.002049300 1819 87 5475
66 0.010 0.66 0.81914894 0.7613344 0.002903112 1768 85 5526
67 0.010 0.67 0.82978723 0.7690623 0.001543156 1713 80 5581
68 0.010 0.68 0.83617021 0.7774343 0.001928142 1651 77 5643
69 0.010 0.69 0.84468085 0.7856775 0.001587936 1591 73 5703
70 0.010 0.70 0.84893617 0.7921175 0.002117914 1543 71 5751
71 0.010 0.71 0.84893617 0.7980422 0.005499127 1497 71 5797
72 0.010 0.72 0.85531915 0.8070582 0.007468151 1430 68 5864
73 0.010 0.73 0.85957447 0.8143998 0.011157269 1375 66 5919
74 0.010 0.74 0.86170213 0.8207110 0.019905254 1327 65 5967
75 0.010 0.75 0.86808511 0.8274086 0.019046089 1278 62 6016
76 0.010 0.76 0.86808511 0.8339773 0.047002537 1227 62 6067
77 0.010 0.77 0.87446809 0.8414477 0.050359115 1172 59 6122
78 0.010 0.78 0.87659574 0.8482741 0.089239063 1120 58 6174
79 0.010 0.79 0.88085106 0.8571613 0.148089816 1053 56 6241
80 0.010 0.80 0.88510638 0.8642452 0.196092797 1000 54 6294
81 0.010 0.81 0.88936170 0.8691396 0.203863682 964 52 6330
82 0.010 0.82 0.89148936 0.8760948 0.330615516 911 51 6383
83 0.010 0.83 0.89574468 0.8824060 0.394040475 864 49 6430
84 0.010 0.84 0.90212766 0.8898764 0.424088692 809 46 6485
85 0.010 0.85 0.90851064 0.8973467 0.456675234 754 43 6540
86 0.010 0.86 0.91276596 0.9048171 0.599755390 698 41 6596
87 0.010 0.87 0.91914894 0.9128027 0.675365372 639 38 6655
88 0.010 0.88 0.92978723 0.9188563 0.418943154 597 33 6697
89 0.010 0.89 0.93404255 0.9264554 0.576182799 540 31 6754
90 0.010 0.90 0.94255319 0.9339258 0.495856320 486 27 6808
91 0.010 0.91 0.94893617 0.9406234 0.492669041 437 24 6857
92 0.010 0.92 0.95106383 0.9479650 0.837615472 381 23 6913
93 0.010 0.93 0.95744681 0.9544049 0.832038000 334 20 6960
94 0.010 0.94 0.96595745 0.9612313 0.671322516 285 16 7009
95 0.010 0.95 0.97021277 0.9687017 0.954186719 229 14 7065
96 0.010 0.96 0.97659574 0.9741113 0.841408218 190 11 7104
97 0.010 0.97 0.98297872 0.9802937 0.794170336 145 8 7149
98 0.010 0.98 0.99148936 0.9875064 0.556653886 93 4 7201
99 0.010 0.99 0.99361702 0.9920144 0.892300997 59 3 7235
100 0.010 1.00 1.00000000 1.0000000 0.000000000 0 0 7294
101 0.025 0.01 0.10725894 0.0951829 0.203342669 6201 824 640
102 0.025 0.02 0.13542795 0.1209428 0.166340232 6027 798 814
103 0.025 0.03 0.16359697 0.1448995 0.095042111 5867 772 974
104 0.025 0.04 0.18309859 0.1664091 0.160544735 5718 754 1123
105 0.025 0.05 0.20910076 0.1846986 0.046581728 5600 730 1241
106 0.025 0.06 0.22968581 0.2005410 0.020774749 5496 711 1345
107 0.025 0.07 0.23943662 0.2153529 0.063797299 5390 702 1451
108 0.025 0.08 0.26327194 0.2315817 0.016855172 5286 680 1555
109 0.025 0.09 0.28169014 0.2461360 0.008520845 5190 663 1651
110 0.025 0.10 0.30335861 0.2592736 0.001300533 5108 643 1733
111 0.025 0.11 0.31202600 0.2753735 0.008886512 4991 635 1850
112 0.025 0.12 0.32177681 0.2881247 0.017970430 4901 626 1940
113 0.025 0.13 0.32719393 0.2982998 0.044885802 4827 621 2014
114 0.025 0.14 0.33802817 0.3082174 0.040214866 4760 611 2081
115 0.025 0.15 0.34452871 0.3212262 0.114651729 4665 605 2176
116 0.025 0.16 0.35861322 0.3323029 0.076629384 4592 592 2249
117 0.025 0.17 0.37161430 0.3444101 0.069360983 4510 580 2331
118 0.025 0.18 0.38136511 0.3563885 0.098680879 4426 571 2415
119 0.025 0.19 0.39219935 0.3633436 0.056735175 4382 561 2459
120 0.025 0.20 0.40195016 0.3733900 0.060824457 4313 552 2528
121 0.025 0.21 0.41386782 0.3858836 0.068069638 4227 541 2614
122 0.025 0.22 0.42145179 0.3956723 0.094832833 4158 534 2683
123 0.025 0.23 0.42795233 0.4045595 0.131839548 4095 528 2746
124 0.025 0.24 0.43445287 0.4149923 0.213965427 4020 522 2821
125 0.025 0.25 0.44962080 0.4234930 0.093747605 3968 508 2873
126 0.025 0.26 0.45937161 0.4328954 0.090246743 3904 499 2937
127 0.025 0.27 0.46587216 0.4434570 0.154146400 3828 493 3013
128 0.025 0.28 0.47562297 0.4545337 0.181680426 3751 484 3090
129 0.025 0.29 0.48320693 0.4645801 0.240547391 3680 477 3161
130 0.025 0.30 0.49187432 0.4733385 0.243436089 3620 469 3221
131 0.025 0.31 0.50162514 0.4841577 0.273001602 3545 460 3296
132 0.025 0.32 0.50812568 0.4945904 0.400270915 3470 454 3371
133 0.025 0.33 0.52221018 0.5038640 0.249099040 3411 441 3430
134 0.025 0.34 0.52979415 0.5117208 0.256308825 3357 434 3484
135 0.025 0.35 0.53846154 0.5204791 0.258516050 3297 426 3544
136 0.025 0.36 0.54604550 0.5278207 0.251619970 3247 419 3594
137 0.025 0.37 0.55037920 0.5350335 0.336732245 3195 415 3646
138 0.025 0.38 0.55579632 0.5421175 0.393428537 3145 410 3696
139 0.025 0.39 0.56879740 0.5508758 0.258102022 3089 398 3752
140 0.025 0.40 0.57529794 0.5580886 0.277359189 3039 392 3802
141 0.025 0.41 0.58179848 0.5659454 0.317378735 2984 386 3857
142 0.025 0.42 0.58938245 0.5739310 0.329136600 2929 379 3912
143 0.025 0.43 0.59804984 0.5829469 0.339169369 2867 371 3974
144 0.025 0.44 0.60780065 0.5914477 0.297856895 2810 362 4031
145 0.025 0.45 0.61971831 0.5999485 0.203978459 2755 351 4086
146 0.025 0.46 0.63055255 0.6079341 0.143313091 2703 341 4138
147 0.025 0.47 0.63705309 0.6159196 0.170604818 2647 335 4194
148 0.025 0.48 0.64247021 0.6239052 0.228500165 2590 330 4251
149 0.025 0.49 0.65547129 0.6322772 0.128387482 2537 318 4304
150 0.025 0.50 0.66197183 0.6385884 0.123836095 2494 312 4347
151 0.025 0.51 0.66847237 0.6455435 0.129838383 2446 306 4395
152 0.025 0.52 0.68147346 0.6534003 0.061145156 2397 294 4444
153 0.025 0.53 0.68797400 0.6625451 0.088472904 2332 288 4509
154 0.025 0.54 0.69555796 0.6700155 0.085274150 2281 281 4560
155 0.025 0.55 0.70855905 0.6789026 0.043565635 2224 269 4617
156 0.025 0.56 0.71289274 0.6863730 0.069957221 2170 265 4671
157 0.025 0.57 0.72264355 0.6946162 0.053421311 2115 256 4726
158 0.025 0.58 0.72914410 0.7022154 0.061815349 2062 250 4779
159 0.025 0.59 0.73781148 0.7108449 0.059235416 2003 242 4838
160 0.025 0.60 0.74539545 0.7174137 0.048557186 1959 235 4882
161 0.025 0.61 0.75731311 0.7246265 0.019856667 1914 224 4927
162 0.025 0.62 0.76598050 0.7317105 0.013746915 1867 216 4974
163 0.025 0.63 0.77031419 0.7396960 0.026526364 1809 212 5032
164 0.025 0.64 0.77573131 0.7465224 0.032930025 1761 207 5080
165 0.025 0.65 0.78548212 0.7545080 0.022102636 1708 198 5133
166 0.025 0.66 0.79414951 0.7613344 0.014267660 1663 190 5178
167 0.025 0.67 0.80281690 0.7690623 0.010750241 1611 182 5230
168 0.025 0.68 0.81581798 0.7774343 0.003235837 1558 170 5283
169 0.025 0.69 0.82448537 0.7856775 0.002543112 1502 162 5339
170 0.025 0.70 0.82990249 0.7921175 0.002973234 1457 157 5384
171 0.025 0.71 0.83206934 0.7980422 0.006942751 1413 155 5428
172 0.025 0.72 0.83640303 0.8070582 0.018155945 1347 151 5494
173 0.025 0.73 0.84615385 0.8143998 0.009366570 1299 142 5542
174 0.025 0.74 0.85048754 0.8207110 0.013637222 1254 138 5587
175 0.025 0.75 0.85807151 0.8274086 0.009885582 1209 131 5632
176 0.025 0.76 0.86132178 0.8339773 0.019736435 1161 128 5680
177 0.025 0.77 0.86457205 0.8414477 0.045386734 1106 125 5735
178 0.025 0.78 0.86998917 0.8482741 0.056109193 1058 120 5783
179 0.025 0.79 0.87215601 0.8571613 0.181265384 991 118 5850
180 0.025 0.80 0.87865655 0.8642452 0.190011487 942 112 5899
181 0.025 0.81 0.88407367 0.8691396 0.167207833 909 107 5932
182 0.025 0.82 0.88624052 0.8760948 0.345453790 857 105 5984
183 0.025 0.83 0.89165764 0.8824060 0.381503105 813 100 6028
184 0.025 0.84 0.89815818 0.8898764 0.423565477 761 94 6080
185 0.025 0.85 0.90790899 0.8973467 0.285256869 712 85 6129
186 0.025 0.86 0.91982665 0.9048171 0.110575250 665 74 6176
187 0.025 0.87 0.93066089 0.9128027 0.046970080 613 64 6228
188 0.025 0.88 0.93824485 0.9188563 0.025486841 573 57 6268
189 0.025 0.89 0.94257855 0.9264554 0.053361897 518 53 6323
190 0.025 0.90 0.94907909 0.9339258 0.056943563 466 47 6375
191 0.025 0.91 0.95666306 0.9406234 0.033797870 421 40 6420
192 0.025 0.92 0.95774648 0.9479650 0.178145323 365 39 6476
193 0.025 0.93 0.96099675 0.9544049 0.347891706 318 36 6523
194 0.025 0.94 0.96749729 0.9612313 0.337191875 271 30 6570
195 0.025 0.95 0.97399783 0.9687017 0.376834874 219 24 6622
196 0.025 0.96 0.97833153 0.9741113 0.453423543 181 20 6660
197 0.025 0.97 0.98374865 0.9802937 0.497520294 138 15 6703
198 0.025 0.98 0.99241603 0.9875064 0.203108198 90 7 6751
199 0.025 0.99 0.99566631 0.9920144 0.258062753 58 4 6783
200 0.025 1.00 1.00000000 1.0000000 0.000000000 0 0 6841
201 0.050 0.01 0.09992862 0.0951829 0.536357502 5764 1261 599
202 0.050 0.02 0.12705211 0.1209428 0.465740639 5602 1223 761
203 0.050 0.03 0.15274804 0.1448995 0.378879666 5452 1187 911
204 0.050 0.04 0.17416131 0.1664091 0.411665730 5315 1157 1048
205 0.050 0.05 0.19557459 0.1846986 0.262383855 5203 1127 1160
206 0.050 0.06 0.21341899 0.2005410 0.196035184 5105 1102 1258
207 0.050 0.07 0.22412562 0.2153529 0.397286825 5005 1087 1358
208 0.050 0.08 0.24411135 0.2315817 0.232837047 4907 1059 1456
209 0.050 0.09 0.25838687 0.2461360 0.253609905 4814 1039 1549
210 0.050 0.10 0.27623126 0.2592736 0.117297788 4737 1014 1626
211 0.050 0.11 0.29264811 0.2753735 0.117380454 4635 991 1728
212 0.050 0.12 0.30549607 0.2881247 0.120349147 4554 973 1809
213 0.050 0.13 0.31120628 0.2982998 0.256746802 4483 965 1880
214 0.050 0.14 0.32334047 0.3082174 0.186113556 4423 948 1940
215 0.050 0.15 0.33404711 0.3212262 0.269755303 4337 933 2026
216 0.050 0.16 0.34475375 0.3323029 0.288439154 4266 918 2097
217 0.050 0.17 0.35831549 0.3444101 0.238447564 4191 899 2172
218 0.050 0.18 0.36902213 0.3563885 0.289215960 4113 884 2250
219 0.050 0.19 0.37758744 0.3633436 0.232563024 4071 872 2292
220 0.050 0.20 0.39186296 0.3733900 0.121498999 4013 852 2350
221 0.050 0.21 0.40328337 0.3858836 0.147754421 3932 836 2431
222 0.050 0.22 0.41256246 0.3956723 0.162134895 3869 823 2494
223 0.050 0.23 0.41970021 0.4045595 0.212986535 3810 813 2553
224 0.050 0.24 0.42683797 0.4149923 0.335014558 3739 803 2624
225 0.050 0.25 0.44039971 0.4234930 0.166103227 3692 784 2671
226 0.050 0.26 0.44967880 0.4328954 0.170458284 3632 771 2731
227 0.050 0.27 0.45538901 0.4434570 0.335374286 3558 763 2805
228 0.050 0.28 0.46823697 0.4545337 0.267766743 3490 745 2873
229 0.050 0.29 0.47394718 0.4645801 0.455098711 3420 737 2943
230 0.050 0.30 0.48394004 0.4733385 0.396240839 3366 723 2997
231 0.050 0.31 0.49393291 0.4841577 0.435855979 3296 709 3067
232 0.050 0.32 0.49964311 0.4945904 0.697774774 3223 701 3140
233 0.050 0.33 0.51249108 0.5038640 0.494040940 3169 683 3194
234 0.050 0.34 0.51962884 0.5117208 0.532239836 3118 673 3245
235 0.050 0.35 0.52748037 0.5204791 0.582393042 3061 662 3302
236 0.050 0.36 0.53319058 0.5278207 0.678014568 3012 654 3351
237 0.050 0.37 0.54104211 0.5350335 0.639425610 2967 643 3396
238 0.050 0.38 0.54817987 0.5421175 0.635870301 2922 633 3441
239 0.050 0.39 0.55817273 0.5508758 0.564024717 2868 619 3495
240 0.050 0.40 0.56388294 0.5580886 0.650768104 2820 611 3543
241 0.050 0.41 0.57102070 0.5659454 0.693867572 2769 601 3594
242 0.050 0.42 0.57815846 0.5739310 0.746223055 2717 591 3646
243 0.050 0.43 0.59172020 0.5829469 0.480348663 2666 572 3697
244 0.050 0.44 0.60171306 0.5914477 0.404617196 2614 558 3749
245 0.050 0.45 0.61099215 0.5999485 0.367105109 2561 545 3802
246 0.050 0.46 0.62098501 0.6079341 0.282357624 2513 531 3850
247 0.050 0.47 0.62740899 0.6159196 0.343972549 2460 522 3903
248 0.050 0.48 0.63311920 0.6239052 0.449656394 2406 514 3957
249 0.050 0.49 0.64596717 0.6322772 0.252927374 2359 496 4004
250 0.050 0.50 0.65096360 0.6385884 0.300976201 2317 489 4046
251 0.050 0.51 0.65738758 0.6455435 0.320765667 2272 480 4091
252 0.050 0.52 0.66880799 0.6534003 0.190997053 2227 464 4136
253 0.050 0.53 0.67880086 0.6625451 0.164463531 2170 450 4193
254 0.050 0.54 0.68665239 0.6700155 0.152281333 2123 439 4240
255 0.050 0.55 0.69950036 0.6789026 0.073067345 2072 421 4291
256 0.050 0.56 0.70521056 0.6863730 0.099582819 2022 413 4341
257 0.050 0.57 0.71377587 0.6946162 0.091421894 1970 401 4393
258 0.050 0.58 0.72019986 0.7022154 0.110977280 1920 392 4443
259 0.050 0.59 0.72733762 0.7108449 0.141148315 1863 382 4500
260 0.050 0.60 0.73304782 0.7174137 0.160657826 1820 374 4543
261 0.050 0.61 0.74232691 0.7246265 0.108432492 1777 361 4586
262 0.050 0.62 0.74946467 0.7317105 0.104490975 1732 351 4631
263 0.050 0.63 0.75517488 0.7396960 0.154198097 1678 343 4685
264 0.050 0.64 0.76017131 0.7465224 0.206456589 1632 336 4731
265 0.050 0.65 0.76730906 0.7545080 0.231894566 1580 326 4783
266 0.050 0.66 0.77587438 0.7613344 0.168918101 1539 314 4824
267 0.050 0.67 0.78586724 0.7690623 0.106597918 1493 300 4870
268 0.050 0.68 0.79514632 0.7774343 0.084521366 1441 287 4922
269 0.050 0.69 0.80585296 0.7856775 0.045840434 1392 272 4971
270 0.050 0.70 0.81084939 0.7921175 0.061177074 1349 265 5014
271 0.050 0.71 0.81513205 0.7980422 0.084835463 1309 259 5054
272 0.050 0.72 0.82084226 0.8070582 0.159470580 1247 251 5116
273 0.050 0.73 0.82940757 0.8143998 0.119216924 1202 239 5161
274 0.050 0.74 0.83511777 0.8207110 0.129931598 1161 231 5202
275 0.050 0.75 0.84653819 0.8274086 0.039980904 1125 215 5238
276 0.050 0.76 0.85082084 0.8339773 0.066966323 1080 209 5283
277 0.050 0.77 0.85438972 0.8414477 0.154276959 1027 204 5336
278 0.050 0.78 0.85867238 0.8482741 0.247170112 980 198 5383
279 0.050 0.79 0.86224126 0.8571613 0.576782468 916 193 5447
280 0.050 0.80 0.87080657 0.8642452 0.453903036 873 181 5490
281 0.050 0.81 0.87580300 0.8691396 0.439427254 842 174 5521
282 0.050 0.82 0.87937188 0.8760948 0.714024692 793 169 5570
283 0.050 0.83 0.88722341 0.8824060 0.566968365 755 158 5608
284 0.050 0.84 0.89364739 0.8898764 0.652040174 706 149 5657
285 0.050 0.85 0.90292648 0.8973467 0.476776280 661 136 5702
286 0.050 0.86 0.91220557 0.9048171 0.321852750 616 123 5747
287 0.050 0.87 0.92219843 0.9128027 0.185283729 568 109 5795
288 0.050 0.88 0.92862241 0.9188563 0.154232704 530 100 5833
289 0.050 0.89 0.93504640 0.9264554 0.192151290 480 91 5883
290 0.050 0.90 0.94004283 0.9339258 0.337700882 429 84 5934
291 0.050 0.91 0.94932191 0.9406234 0.144462327 390 71 5973
292 0.050 0.92 0.95431834 0.9479650 0.264290480 340 64 6023
293 0.050 0.93 0.95860100 0.9544049 0.446701200 296 58 6067
294 0.050 0.94 0.96431121 0.9612313 0.559753571 251 50 6112
295 0.050 0.95 0.97216274 0.9687017 0.461069645 204 39 6159
296 0.050 0.96 0.97644540 0.9741113 0.606701938 168 33 6195
297 0.050 0.97 0.98286938 0.9802937 0.509225035 129 24 6234
298 0.050 0.98 0.99286224 0.9875064 0.062776234 87 10 6276
299 0.050 0.99 0.99571734 0.9920144 0.120100978 56 6 6307
300 0.050 1.00 1.00000000 1.0000000 0.000000000 0 0 6363
301 0.075 0.01 0.09909400 0.0951829 0.554472984 5434 1591 564
302 0.075 0.02 0.12627407 0.1209428 0.459160577 5282 1543 716
303 0.075 0.03 0.15175538 0.1448995 0.371982356 5141 1498 857
304 0.075 0.04 0.17440544 0.1664091 0.322094223 5014 1458 984
305 0.075 0.05 0.19535674 0.1846986 0.201144238 4909 1421 1089
306 0.075 0.06 0.21234428 0.2005410 0.168942598 4816 1391 1182
307 0.075 0.07 0.22423556 0.2153529 0.317202711 4722 1370 1276
308 0.075 0.08 0.24462061 0.2315817 0.148247935 4632 1334 1366
309 0.075 0.09 0.25821065 0.2461360 0.190605327 4543 1310 1455
310 0.075 0.10 0.27859570 0.2592736 0.037786339 4477 1274 1521
311 0.075 0.11 0.29558324 0.2753735 0.032941280 4382 1244 1616
312 0.075 0.12 0.31030578 0.2881247 0.020789787 4309 1218 1689
313 0.075 0.13 0.31936580 0.2982998 0.029864193 4246 1202 1752
314 0.075 0.14 0.33012458 0.3082174 0.025154526 4188 1183 1810
315 0.075 0.15 0.34201586 0.3212262 0.035754225 4108 1162 1890
316 0.075 0.16 0.35277463 0.3323029 0.040442903 4041 1143 1957
317 0.075 0.17 0.36466591 0.3444101 0.044469761 3968 1122 2030
318 0.075 0.18 0.37599094 0.3563885 0.053774135 3895 1102 2103
319 0.075 0.19 0.38335221 0.3633436 0.049893359 3854 1089 2144
320 0.075 0.20 0.39694224 0.3733900 0.021446167 3800 1065 2198
321 0.075 0.21 0.40883352 0.3858836 0.025998038 3724 1044 2274
322 0.075 0.22 0.41789354 0.3956723 0.031951969 3664 1028 2334
323 0.075 0.23 0.42525481 0.4045595 0.046762322 3608 1015 2390
324 0.075 0.24 0.43374858 0.4149923 0.073041859 3542 1000 2456
325 0.075 0.25 0.44563986 0.4234930 0.034379074 3497 979 2501
326 0.075 0.26 0.45469989 0.4328954 0.037825952 3440 963 2558
327 0.075 0.27 0.46206116 0.4434570 0.077860938 3371 950 2627
328 0.075 0.28 0.47338618 0.4545337 0.074578051 3305 930 2693
329 0.075 0.29 0.47961495 0.4645801 0.157313838 3238 919 2760
330 0.075 0.30 0.48810872 0.4733385 0.165355628 3185 904 2813
331 0.075 0.31 0.49830125 0.4841577 0.184822102 3119 886 2879
332 0.075 0.32 0.50453001 0.4945904 0.355778027 3049 875 2949
333 0.075 0.33 0.51642129 0.5038640 0.240500972 2998 854 3000
334 0.075 0.34 0.52321631 0.5117208 0.283510719 2949 842 3049
335 0.075 0.35 0.53057758 0.5204791 0.347544055 2894 829 3104
336 0.075 0.36 0.53624009 0.5278207 0.435847378 2847 819 3151
337 0.075 0.37 0.54303511 0.5350335 0.459367803 2803 807 3195
338 0.075 0.38 0.54983012 0.5421175 0.475862857 2760 795 3238
339 0.075 0.39 0.55832390 0.5508758 0.491005234 2707 780 3291
340 0.075 0.40 0.56398641 0.5580886 0.588814287 2661 770 3337
341 0.075 0.41 0.57021518 0.5659454 0.700549908 2611 759 3387
342 0.075 0.42 0.57757644 0.5739310 0.745110126 2562 746 3436
343 0.075 0.43 0.59003398 0.5829469 0.509410199 2514 724 3484
344 0.075 0.44 0.59966025 0.5914477 0.440559910 2465 707 3533
345 0.075 0.45 0.60928652 0.5999485 0.376858646 2416 690 3582
346 0.075 0.46 0.61891280 0.6079341 0.294892497 2371 673 3627
347 0.075 0.47 0.62514156 0.6159196 0.379563091 2320 662 3678
348 0.075 0.48 0.63080408 0.6239052 0.513763378 2268 652 3730
349 0.075 0.49 0.64156285 0.6322772 0.372038863 2222 633 3776
350 0.075 0.50 0.64665912 0.6385884 0.438311011 2182 624 3816
351 0.075 0.51 0.65232163 0.6455435 0.516219394 2138 614 3860
352 0.075 0.52 0.66251416 0.6534003 0.374964874 2095 596 3903
353 0.075 0.53 0.67044168 0.6625451 0.441393250 2038 582 3960
354 0.075 0.54 0.67893545 0.6700155 0.379825970 1995 567 4003
355 0.075 0.55 0.69082673 0.6789026 0.233233458 1947 546 4051
356 0.075 0.56 0.69648924 0.6863730 0.310915768 1899 536 4099
357 0.075 0.57 0.70554926 0.6946162 0.268911368 1851 520 4147
358 0.075 0.58 0.71121178 0.7022154 0.362281192 1802 510 4196
359 0.075 0.59 0.71913930 0.7108449 0.398191830 1749 496 4249
360 0.075 0.60 0.72650057 0.7174137 0.349866061 1711 483 4287
361 0.075 0.61 0.73499434 0.7246265 0.280411022 1670 468 4328
362 0.075 0.62 0.74065685 0.7317105 0.349862094 1625 458 4373
363 0.075 0.63 0.74688562 0.7396960 0.451732984 1574 447 4424
364 0.075 0.64 0.75084938 0.7465224 0.656706354 1528 440 4470
365 0.075 0.65 0.75707814 0.7545080 0.799440115 1477 429 4521
366 0.075 0.66 0.76557191 0.7613344 0.657373188 1439 414 4559
367 0.075 0.67 0.77576444 0.7690623 0.466468877 1397 396 4601
368 0.075 0.68 0.78539071 0.7774343 0.377792036 1349 379 4649
369 0.075 0.69 0.79558324 0.7856775 0.262214373 1303 361 4695
370 0.075 0.70 0.80124575 0.7921175 0.297334739 1263 351 4735
371 0.075 0.71 0.80690827 0.7980422 0.306697819 1227 341 4771
372 0.075 0.72 0.81483579 0.8070582 0.363852953 1171 327 4827
373 0.075 0.73 0.82276331 0.8143998 0.320364804 1128 313 4870
374 0.075 0.74 0.83069083 0.8207110 0.226810674 1093 299 4905
375 0.075 0.75 0.84144960 0.8274086 0.081740205 1060 280 4938
376 0.075 0.76 0.84881087 0.8339773 0.061538939 1022 267 4976
377 0.075 0.77 0.85277463 0.8414477 0.148284380 971 260 5027
378 0.075 0.78 0.85730464 0.8482741 0.243702454 926 252 5072
379 0.075 0.79 0.86070215 0.8571613 0.656215424 863 246 5135
380 0.075 0.80 0.86806342 0.8642452 0.621700693 821 233 5177
381 0.075 0.81 0.87259343 0.8691396 0.653063769 791 225 5207
382 0.075 0.82 0.87655719 0.8760948 0.979245547 744 218 5254
383 0.075 0.83 0.88448471 0.8824060 0.789843458 709 204 5289
384 0.075 0.84 0.89354473 0.8898764 0.605130648 667 188 5331
385 0.075 0.85 0.90203851 0.8973467 0.487366893 624 173 5374
386 0.075 0.86 0.91166478 0.9048171 0.284845402 583 156 5415
387 0.075 0.87 0.92015855 0.9128027 0.230674257 536 141 5462
388 0.075 0.88 0.92695357 0.9188563 0.171232211 501 129 5497
389 0.075 0.89 0.93488109 0.9264554 0.135845382 456 115 5542
390 0.075 0.90 0.94054360 0.9339258 0.222756128 408 105 5590
391 0.075 0.91 0.94847112 0.9406234 0.125917208 370 91 5628
392 0.075 0.92 0.95413364 0.9479650 0.205154508 323 81 5675
393 0.075 0.93 0.95809740 0.9544049 0.434562515 280 74 5718
394 0.075 0.94 0.96545866 0.9612313 0.328628922 240 61 5758
395 0.075 0.95 0.97168743 0.9687017 0.458027414 193 50 5805
396 0.075 0.96 0.97734994 0.9741113 0.373552370 161 40 5837
397 0.075 0.97 0.98357871 0.9802937 0.301767917 124 29 5874
398 0.075 0.98 0.99207248 0.9875064 0.065244852 83 14 5915
399 0.075 0.99 0.99603624 0.9920144 0.044604773 55 7 5943
400 0.075 1.00 1.00000000 1.0000000 0.000000000 0 0 5998
401 0.100 0.01 0.10172745 0.0951829 0.251541338 5153 1872 527
402 0.100 0.02 0.12859885 0.1209428 0.224772854 5009 1816 671
403 0.100 0.03 0.15067179 0.1448995 0.401554792 4869 1770 811
404 0.100 0.04 0.17226488 0.1664091 0.420953637 4747 1725 933
405 0.100 0.05 0.19337812 0.1846986 0.245733752 4649 1681 1031
406 0.100 0.06 0.20873321 0.2005410 0.289137943 4558 1649 1122
407 0.100 0.07 0.22120921 0.2153529 0.465864298 4469 1623 1211
408 0.100 0.08 0.24088292 0.2315817 0.251605081 4384 1582 1296
409 0.100 0.09 0.25287908 0.2461360 0.420380197 4296 1557 1384
410 0.100 0.10 0.27063340 0.2592736 0.175645103 4231 1520 1449
411 0.100 0.11 0.28838772 0.2753735 0.126939031 4143 1483 1537
412 0.100 0.12 0.30230326 0.2881247 0.100454355 4073 1454 1607
413 0.100 0.13 0.31094050 0.2982998 0.147995959 4012 1436 1668
414 0.100 0.14 0.32101727 0.3082174 0.146571411 3956 1415 1724
415 0.100 0.15 0.33205374 0.3212262 0.226210953 3878 1392 1802
416 0.100 0.16 0.34404990 0.3323029 0.192288029 3817 1367 1863
417 0.100 0.17 0.35700576 0.3444101 0.165192932 3750 1340 1930
418 0.100 0.18 0.36804223 0.3563885 0.203385050 3680 1317 2000
419 0.100 0.19 0.37428023 0.3633436 0.235223306 3639 1304 2041
420 0.100 0.20 0.38819578 0.3733900 0.108007801 3590 1275 2090
421 0.100 0.21 0.40115163 0.3858836 0.099421156 3520 1248 2160
422 0.100 0.22 0.40930902 0.3956723 0.143680416 3461 1231 2219
423 0.100 0.23 0.41698656 0.4045595 0.185077221 3408 1215 2272
424 0.100 0.24 0.42706334 0.4149923 0.199992419 3348 1194 2332
425 0.100 0.25 0.43857965 0.4234930 0.108781463 3306 1170 2374
426 0.100 0.26 0.44721689 0.4328954 0.129302542 3251 1152 2429
427 0.100 0.27 0.45729367 0.4434570 0.144082279 3190 1131 2490
428 0.100 0.28 0.46833013 0.4545337 0.146194674 3127 1108 2553
429 0.100 0.29 0.47600768 0.4645801 0.231216032 3065 1092 2615
430 0.100 0.30 0.48464491 0.4733385 0.236818328 3015 1074 2665
431 0.100 0.31 0.49520154 0.4841577 0.248562506 2953 1052 2727
432 0.100 0.32 0.50239923 0.4945904 0.419097224 2887 1037 2793
433 0.100 0.33 0.51487524 0.5038640 0.250218177 2841 1011 2839
434 0.100 0.34 0.52159309 0.5117208 0.303717786 2794 997 2886
435 0.100 0.35 0.52927063 0.5204791 0.360924755 2742 981 2938
436 0.100 0.36 0.53502879 0.5278207 0.456289970 2697 969 2983
437 0.100 0.37 0.54174664 0.5350335 0.488505868 2655 955 3025
438 0.100 0.38 0.54798464 0.5421175 0.546625882 2613 942 3067
439 0.100 0.39 0.55566219 0.5508758 0.625662969 2561 926 3119
440 0.100 0.40 0.56190019 0.5580886 0.701085575 2518 913 3162
441 0.100 0.41 0.56861804 0.5659454 0.793344405 2471 899 3209
442 0.100 0.42 0.57533589 0.5739310 0.899937212 2423 885 3257
443 0.100 0.43 0.58685221 0.5829469 0.691547958 2377 861 3303
444 0.100 0.44 0.59740883 0.5914477 0.534476484 2333 839 3347
445 0.100 0.45 0.60700576 0.5999485 0.457658170 2287 819 3393
446 0.100 0.46 0.61660269 0.6079341 0.356816689 2245 799 3435
447 0.100 0.47 0.62236084 0.6159196 0.496189466 2195 787 3485
448 0.100 0.48 0.62859885 0.6239052 0.623625460 2146 774 3534
449 0.100 0.49 0.63915547 0.6322772 0.462465703 2103 752 3577
450 0.100 0.50 0.64491363 0.6385884 0.498996567 2066 740 3614
451 0.100 0.51 0.65067179 0.6455435 0.585462553 2024 728 3656
452 0.100 0.52 0.66074856 0.6534003 0.425320096 1984 707 3696
453 0.100 0.53 0.66842610 0.6625451 0.524290811 1929 691 3751
454 0.100 0.54 0.67658349 0.6700155 0.472575717 1888 674 3792
455 0.100 0.55 0.68809981 0.6789026 0.305868210 1843 650 3837
456 0.100 0.56 0.69385797 0.6863730 0.404599868 1797 638 3883
457 0.100 0.57 0.70393474 0.6946162 0.292769467 1754 617 3926
458 0.100 0.58 0.71065259 0.7022154 0.338688676 1709 603 3971
459 0.100 0.59 0.71833013 0.7108449 0.393690779 1658 587 4022
460 0.100 0.60 0.72552783 0.7174137 0.350618014 1622 572 4058
461 0.100 0.61 0.73512476 0.7246265 0.220319207 1586 552 4094
462 0.100 0.62 0.74232246 0.7317105 0.211508187 1546 537 4134
463 0.100 0.63 0.74904031 0.7396960 0.268126664 1498 523 4182
464 0.100 0.64 0.75383877 0.7465224 0.385261743 1455 513 4225
465 0.100 0.65 0.76007678 0.7545080 0.508709681 1406 500 4274
466 0.100 0.66 0.76823417 0.7613344 0.404351299 1370 483 4310
467 0.100 0.67 0.77735125 0.7690623 0.308028914 1329 464 4351
468 0.100 0.68 0.78550864 0.7774343 0.314789572 1281 447 4399
469 0.100 0.69 0.79462572 0.7856775 0.257361714 1236 428 4444
470 0.100 0.70 0.80134357 0.7921175 0.237236618 1200 414 4480
471 0.100 0.71 0.80662188 0.7980422 0.267547255 1165 403 4515
472 0.100 0.72 0.81477927 0.8070582 0.311606500 1112 386 4568
473 0.100 0.73 0.82293666 0.8143998 0.254699012 1072 369 4608
474 0.100 0.74 0.83061420 0.8207110 0.178776325 1039 353 4641
475 0.100 0.75 0.84117083 0.8274086 0.056152120 1009 331 4671
476 0.100 0.76 0.84740883 0.8339773 0.058472325 971 318 4709
477 0.100 0.77 0.85412668 0.8414477 0.069121141 927 304 4753
478 0.100 0.78 0.85892514 0.8482741 0.121410753 884 294 4796
479 0.100 0.79 0.86468330 0.8571613 0.266670746 827 282 4853
480 0.100 0.80 0.87188100 0.8642452 0.249150531 787 267 4893
481 0.100 0.81 0.87571977 0.8691396 0.315668632 757 259 4923
482 0.100 0.82 0.88003839 0.8760948 0.548526558 712 250 4968
483 0.100 0.83 0.88723608 0.8824060 0.446933199 678 235 5002
484 0.100 0.84 0.89539347 0.8898764 0.368260037 637 218 5043
485 0.100 0.85 0.90403071 0.8973467 0.257126090 597 200 5083
486 0.100 0.86 0.91458733 0.9048171 0.083049008 561 178 5119
487 0.100 0.87 0.92322457 0.9128027 0.054075150 517 160 5163
488 0.100 0.88 0.92946257 0.9188563 0.042738871 483 147 5197
489 0.100 0.89 0.93809981 0.9264554 0.019707563 442 129 5238
490 0.100 0.90 0.94337812 0.9339258 0.047779282 395 118 5285
491 0.100 0.91 0.95009597 0.9406234 0.037059512 357 104 5323
492 0.100 0.92 0.95681382 0.9479650 0.038563047 314 90 5366
493 0.100 0.93 0.96113244 0.9544049 0.096940512 273 81 5407
494 0.100 0.94 0.96833013 0.9612313 0.057914836 235 66 5445
495 0.100 0.95 0.97408829 0.9687017 0.114664768 189 54 5491
496 0.100 0.96 0.97936660 0.9741113 0.091866390 158 43 5522
497 0.100 0.97 0.98464491 0.9802937 0.114389160 121 32 5559
498 0.100 0.98 0.99184261 0.9875064 0.049033925 80 17 5600
499 0.100 0.99 0.99568138 0.9920144 0.039873584 53 9 5627
500 0.100 1.00 1.00000000 1.0000000 0.000000000 0 0 5680
Dboth
1 53
2 66
3 83
4 92
5 103
6 114
7 120
8 132
9 140
10 150
11 157
12 164
13 168
14 174
15 177
16 184
17 192
18 195
19 200
20 206
21 213
22 213
23 217
24 220
25 226
26 227
27 231
28 237
29 239
30 243
31 245
32 247
33 257
34 261
35 264
36 267
37 270
38 273
39 280
40 282
41 285
42 287
43 291
44 297
45 303
46 308
47 310
48 313
49 320
50 323
51 326
52 333
53 337
54 339
55 346
56 348
57 353
58 357
59 360
60 362
61 370
62 372
63 375
64 378
65 383
66 385
67 390
68 393
69 397
70 399
71 399
72 402
73 404
74 405
75 408
76 408
77 411
78 412
79 414
80 416
81 418
82 419
83 421
84 424
85 427
86 429
87 432
88 437
89 439
90 443
91 446
92 447
93 450
94 454
95 456
96 459
97 462
98 466
99 467
100 470
101 99
102 125
103 151
104 169
105 193
106 212
107 221
108 243
109 260
110 280
111 288
112 297
113 302
114 312
115 318
116 331
117 343
118 352
119 362
120 371
121 382
122 389
123 395
124 401
125 415
126 424
127 430
128 439
129 446
130 454
131 463
132 469
133 482
134 489
135 497
136 504
137 508
138 513
139 525
140 531
141 537
142 544
143 552
144 561
145 572
146 582
147 588
148 593
149 605
150 611
151 617
152 629
153 635
154 642
155 654
156 658
157 667
158 673
159 681
160 688
161 699
162 707
163 711
164 716
165 725
166 733
167 741
168 753
169 761
170 766
171 768
172 772
173 781
174 785
175 792
176 795
177 798
178 803
179 805
180 811
181 816
182 818
183 823
184 829
185 838
186 849
187 859
188 866
189 870
190 876
191 883
192 884
193 887
194 893
195 899
196 903
197 908
198 916
199 919
200 923
201 140
202 178
203 214
204 244
205 274
206 299
207 314
208 342
209 362
210 387
211 410
212 428
213 436
214 453
215 468
216 483
217 502
218 517
219 529
220 549
221 565
222 578
223 588
224 598
225 617
226 630
227 638
228 656
229 664
230 678
231 692
232 700
233 718
234 728
235 739
236 747
237 758
238 768
239 782
240 790
241 800
242 810
243 829
244 843
245 856
246 870
247 879
248 887
249 905
250 912
251 921
252 937
253 951
254 962
255 980
256 988
257 1000
258 1009
259 1019
260 1027
261 1040
262 1050
263 1058
264 1065
265 1075
266 1087
267 1101
268 1114
269 1129
270 1136
271 1142
272 1150
273 1162
274 1170
275 1186
276 1192
277 1197
278 1203
279 1208
280 1220
281 1227
282 1232
283 1243
284 1252
285 1265
286 1278
287 1292
288 1301
289 1310
290 1317
291 1330
292 1337
293 1343
294 1351
295 1362
296 1368
297 1377
298 1391
299 1395
300 1401
301 175
302 223
303 268
304 308
305 345
306 375
307 396
308 432
309 456
310 492
311 522
312 548
313 564
314 583
315 604
316 623
317 644
318 664
319 677
320 701
321 722
322 738
323 751
324 766
325 787
326 803
327 816
328 836
329 847
330 862
331 880
332 891
333 912
334 924
335 937
336 947
337 959
338 971
339 986
340 996
341 1007
342 1020
343 1042
344 1059
345 1076
346 1093
347 1104
348 1114
349 1133
350 1142
351 1152
352 1170
353 1184
354 1199
355 1220
356 1230
357 1246
358 1256
359 1270
360 1283
361 1298
362 1308
363 1319
364 1326
365 1337
366 1352
367 1370
368 1387
369 1405
370 1415
371 1425
372 1439
373 1453
374 1467
375 1486
376 1499
377 1506
378 1514
379 1520
380 1533
381 1541
382 1548
383 1562
384 1578
385 1593
386 1610
387 1625
388 1637
389 1651
390 1661
391 1675
392 1685
393 1692
394 1705
395 1716
396 1726
397 1737
398 1752
399 1759
400 1766
401 212
402 268
403 314
404 359
405 403
406 435
407 461
408 502
409 527
410 564
411 601
412 630
413 648
414 669
415 692
416 717
417 744
418 767
419 780
420 809
421 836
422 853
423 869
424 890
425 914
426 932
427 953
428 976
429 992
430 1010
431 1032
432 1047
433 1073
434 1087
435 1103
436 1115
437 1129
438 1142
439 1158
440 1171
441 1185
442 1199
443 1223
444 1245
445 1265
446 1285
447 1297
448 1310
449 1332
450 1344
451 1356
452 1377
453 1393
454 1410
455 1434
456 1446
457 1467
458 1481
459 1497
460 1512
461 1532
462 1547
463 1561
464 1571
465 1584
466 1601
467 1620
468 1637
469 1656
470 1670
471 1681
472 1698
473 1715
474 1731
475 1753
476 1766
477 1780
478 1790
479 1802
480 1817
481 1825
482 1834
483 1849
484 1866
485 1884
486 1906
487 1924
488 1937
489 1955
490 1966
491 1980
492 1994
493 2003
494 2018
495 2030
496 2041
497 2052
498 2067
499 2075
500 2084enrichment.plotter(gene.hic.filt, "weighted_Z.ALLvar.C", "adj.P.Val", "FDR for Weighted p-val Combine of Hi-C Contacts Overlapping Gene, Chimp")Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
| Version | Author | Date |
|---|---|---|
| 6f6db11 | Ittai Eres | 2019-04-23 |
| Version | Author | Date |
|---|---|---|
| 6f6db11 | Ittai Eres | 2019-04-23 |
| Version | Author | Date |
|---|---|---|
| 6f6db11 | Ittai Eres | 2019-04-23 |
| Version | Author | Date |
|---|---|---|
| 6f6db11 | Ittai Eres | 2019-04-23 |
DEFDR DHICFDR prop.obs prop.exp chisq.p Dneither DE DHiC
1 0.010 0.01 0.10503282 0.09139992 0.3376883752 6659 409 663
2 0.010 0.02 0.12910284 0.11633886 0.4225611847 6476 398 846
3 0.010 0.03 0.15536105 0.13857822 0.3170457095 6315 386 1007
4 0.010 0.04 0.17505470 0.15876077 0.3594262762 6167 377 1155
5 0.010 0.05 0.19037199 0.17688649 0.4742604326 6033 370 1289
6 0.010 0.06 0.20787746 0.19346960 0.4576880960 5912 362 1410
7 0.010 0.07 0.21881838 0.20825299 0.6072828039 5802 357 1520
8 0.010 0.08 0.24726477 0.22445044 0.2513366381 5689 344 1633
9 0.010 0.09 0.26695842 0.23961949 0.1754838932 5580 335 1742
10 0.010 0.10 0.28884026 0.25414578 0.0890336591 5477 325 1845
11 0.010 0.11 0.29978118 0.26867207 0.1356922508 5369 320 1953
12 0.010 0.12 0.31072210 0.28242705 0.1830628185 5267 315 2055
13 0.010 0.13 0.31728665 0.29155418 0.2322629940 5199 312 2123
14 0.010 0.14 0.32822757 0.30003857 0.1926537543 5138 307 2184
15 0.010 0.15 0.34354486 0.31147962 0.1405806378 5056 300 2266
16 0.010 0.16 0.35448578 0.32202083 0.1390372873 4979 295 2343
17 0.010 0.17 0.36980306 0.33243347 0.0897432077 4905 288 2417
18 0.010 0.18 0.38293217 0.34567425 0.0938327616 4808 282 2514
19 0.010 0.19 0.39606127 0.35338732 0.0552837229 4754 276 2568
20 0.010 0.20 0.40262582 0.36290012 0.0766746132 4683 273 2639
21 0.010 0.21 0.41137856 0.37446973 0.1029851050 4597 269 2725
22 0.010 0.22 0.41356674 0.38513948 0.2158459187 4515 268 2807
23 0.010 0.23 0.42231947 0.39478082 0.2332279961 4444 264 2878
24 0.010 0.24 0.43326039 0.40416506 0.2086438299 4376 259 2946
25 0.010 0.25 0.44201313 0.41174958 0.1915601706 4321 255 3001
26 0.010 0.26 0.44857768 0.42126237 0.2419414634 4250 252 3072
27 0.010 0.27 0.46389497 0.43141792 0.1626488057 4178 245 3144
28 0.010 0.28 0.47264770 0.44183057 0.1872273308 4101 241 3221
29 0.010 0.29 0.47921225 0.45262887 0.2591645149 4020 238 3302
30 0.010 0.30 0.48577681 0.46149891 0.3054886276 3954 235 3368
31 0.010 0.31 0.49671772 0.47114025 0.2798017537 3884 230 3438
32 0.010 0.32 0.49890591 0.48168145 0.4768798802 3803 229 3519
33 0.010 0.33 0.51203501 0.49145134 0.3903319775 3733 223 3589
34 0.010 0.34 0.52078775 0.50096413 0.4091465727 3663 219 3659
35 0.010 0.35 0.53172867 0.51009127 0.3651946162 3597 214 3725
36 0.010 0.36 0.53610503 0.51690449 0.4246422859 3546 212 3776
37 0.010 0.37 0.54266958 0.52526032 0.4715767160 3484 209 3838
38 0.010 0.38 0.54923414 0.53130222 0.4571865380 3440 206 3882
39 0.010 0.39 0.56017505 0.54017226 0.4031582705 3376 201 3946
40 0.010 0.40 0.56236324 0.54801388 0.5572971573 3316 200 4006
41 0.010 0.41 0.57768053 0.55739812 0.3946355546 3250 193 4072
42 0.010 0.42 0.58205689 0.56433989 0.4600893487 3198 191 4124
43 0.010 0.43 0.59299781 0.57269572 0.3922344189 3138 186 4184
44 0.010 0.44 0.60612691 0.58233706 0.3105696715 3069 180 4253
45 0.010 0.45 0.61925602 0.59107854 0.2248045496 3007 174 4315
46 0.010 0.46 0.63019694 0.59917727 0.1784537697 2949 169 4373
47 0.010 0.47 0.63676149 0.60663324 0.1903172226 2894 166 4428
48 0.010 0.48 0.64551422 0.61563183 0.1922240197 2828 162 4494
49 0.010 0.49 0.66083151 0.62218794 0.0879120952 2784 155 4538
50 0.010 0.50 0.66520788 0.63002957 0.1198061717 2725 153 4597
51 0.010 0.51 0.66958425 0.63787119 0.1603858178 2666 151 4656
52 0.010 0.52 0.68708972 0.64507006 0.0594804376 2618 143 4704
53 0.010 0.53 0.69365427 0.65394010 0.0736391476 2552 140 4770
54 0.010 0.54 0.69584245 0.66113896 0.1176722503 2497 139 4825
55 0.010 0.55 0.71115974 0.67026610 0.0621218526 2433 132 4889
56 0.010 0.56 0.71553611 0.67656511 0.0743993099 2386 130 4936
57 0.010 0.57 0.72647702 0.68466384 0.0534855991 2328 125 4994
58 0.010 0.58 0.73085339 0.69250546 0.0752599954 2269 123 5053
59 0.010 0.59 0.73960613 0.70124695 0.0728176726 2205 119 5117
60 0.010 0.60 0.74179431 0.70728885 0.1056603673 2159 118 5163
61 0.010 0.61 0.75492341 0.71525903 0.0596703682 2103 112 5219
62 0.010 0.62 0.76586433 0.72207225 0.0357130947 2055 107 5267
63 0.010 0.63 0.77680525 0.73029952 0.0241547302 1996 102 5326
64 0.010 0.64 0.78336980 0.73736984 0.0245442076 1944 99 5378
65 0.010 0.65 0.79431072 0.74636843 0.0176646240 1879 94 5443
66 0.010 0.66 0.79868709 0.75279599 0.0221277930 1831 92 5491
67 0.010 0.67 0.81181619 0.76269443 0.0128635602 1760 86 5562
68 0.010 0.68 0.82056893 0.77027896 0.0099670456 1705 82 5617
69 0.010 0.69 0.82494530 0.77773493 0.0145238994 1649 80 5673
70 0.010 0.70 0.82932166 0.78377684 0.0173487619 1604 78 5718
71 0.010 0.71 0.82932166 0.78956164 0.0366040403 1559 78 5763
72 0.010 0.72 0.83588621 0.79778892 0.0423556194 1498 75 5824
73 0.010 0.73 0.84463895 0.80614475 0.0371012817 1437 71 5885
74 0.010 0.74 0.84901532 0.81244376 0.0452196964 1390 69 5932
75 0.010 0.75 0.85339168 0.81810001 0.0507776579 1348 67 5974
76 0.010 0.76 0.85776805 0.82568454 0.0718795486 1291 65 6031
77 0.010 0.77 0.86214442 0.83314051 0.0990644626 1235 63 6087
78 0.010 0.78 0.86870897 0.84021082 0.0993566097 1183 60 6139
79 0.010 0.79 0.87089716 0.84946651 0.2101619648 1112 59 6210
80 0.010 0.80 0.87746171 0.85743669 0.2328353480 1053 56 6269
81 0.010 0.81 0.88402626 0.86412135 0.2264239101 1004 53 6318
82 0.010 0.82 0.88840263 0.87157732 0.3001646528 948 51 6374
83 0.010 0.83 0.89059081 0.87736213 0.4149962135 904 50 6418
84 0.010 0.84 0.89715536 0.88494665 0.4427705343 848 47 6474
85 0.010 0.85 0.90153173 0.89253117 0.5737649100 791 45 6531
86 0.010 0.86 0.90809628 0.90011570 0.6128117703 735 42 6587
87 0.010 0.87 0.91247265 0.90898573 0.8545559062 668 40 6654
88 0.010 0.88 0.92341357 0.91489909 0.5578932964 627 35 6695
89 0.010 0.89 0.92778993 0.92196940 0.6978066395 574 33 6748
90 0.010 0.90 0.93216630 0.92955393 0.8959848860 517 31 6805
91 0.010 0.91 0.94091904 0.93726700 0.8161939282 461 27 6861
92 0.010 0.92 0.94310722 0.94575138 0.8801411598 396 26 6926
93 0.010 0.93 0.94529540 0.95192184 0.5687979545 349 25 6973
94 0.010 0.94 0.95404814 0.95963491 0.6149749278 293 21 7029
95 0.010 0.95 0.95842451 0.96657668 0.3868936899 241 19 7081
96 0.010 0.96 0.96936543 0.97261859 0.7706514562 199 14 7123
97 0.010 0.97 0.97374179 0.97891760 0.5312770376 152 12 7170
98 0.010 0.98 0.98249453 0.98675922 0.5410660675 95 8 7227
99 0.010 0.99 0.99124726 0.99190127 1.0000000000 59 4 7263
100 0.010 1.00 1.00000000 1.00000000 0.0000000000 0 0 7322
101 0.025 0.01 0.10652174 0.09139992 0.1022414223 6246 822 613
102 0.025 0.02 0.13586957 0.11633886 0.0557668528 6079 795 780
103 0.025 0.03 0.16195652 0.13857822 0.0327731811 5930 771 929
104 0.025 0.04 0.18152174 0.15876077 0.0495575525 5791 753 1068
105 0.025 0.05 0.20652174 0.17688649 0.0137880282 5673 730 1186
106 0.025 0.06 0.22608696 0.19346960 0.0087218186 5562 712 1297
107 0.025 0.07 0.23478261 0.20825299 0.0387171662 5455 704 1404
108 0.025 0.08 0.26086957 0.22445044 0.0054772352 5353 680 1506
109 0.025 0.09 0.28152174 0.23961949 0.0017492580 5254 661 1605
110 0.025 0.10 0.30326087 0.25414578 0.0003138105 5161 641 1698
111 0.025 0.11 0.30978261 0.26867207 0.0031146997 5054 635 1805
112 0.025 0.12 0.31847826 0.28242705 0.0108410311 4955 627 1904
113 0.025 0.13 0.32282609 0.29155418 0.0289622124 4888 623 1971
114 0.025 0.14 0.33152174 0.30003857 0.0291982132 4830 615 2029
115 0.025 0.15 0.34130435 0.31147962 0.0411114438 4750 606 2109
116 0.025 0.16 0.35760870 0.32202083 0.0154070309 4683 591 2176
117 0.025 0.17 0.37065217 0.33243347 0.0097848863 4614 579 2245
118 0.025 0.18 0.38586957 0.34567425 0.0070783533 4525 565 2334
119 0.025 0.19 0.39782609 0.35338732 0.0030153624 4476 554 2383
120 0.025 0.20 0.40434783 0.36290012 0.0059983621 4408 548 2451
121 0.025 0.21 0.41413043 0.37446973 0.0090350763 4327 539 2532
122 0.025 0.22 0.42065217 0.38513948 0.0202756405 4250 533 2609
123 0.025 0.23 0.42717391 0.39478082 0.0353153136 4181 527 2678
124 0.025 0.24 0.43913043 0.40416506 0.0234648277 4119 516 2740
125 0.025 0.25 0.44782609 0.41174958 0.0196918380 4068 508 2791
126 0.025 0.26 0.45760870 0.42126237 0.0191699522 4003 499 2856
127 0.025 0.27 0.46630435 0.43141792 0.0251012051 3932 491 2927
128 0.025 0.28 0.47282609 0.44183057 0.0476186975 3857 485 3002
129 0.025 0.29 0.48043478 0.45262887 0.0768598321 3780 478 3079
130 0.025 0.30 0.48913043 0.46149891 0.0792265659 3719 470 3140
131 0.025 0.31 0.50217391 0.47114025 0.0484888256 3656 458 3203
132 0.025 0.32 0.50869565 0.48168145 0.0870353956 3580 452 3279
133 0.025 0.33 0.51847826 0.49145134 0.0870496586 3513 443 3346
134 0.025 0.34 0.52826087 0.50096413 0.0839246595 3448 434 3411
135 0.025 0.35 0.53695652 0.51009127 0.0889763262 3385 426 3474
136 0.025 0.36 0.54239130 0.51690449 0.1068865871 3337 421 3522
137 0.025 0.37 0.54782609 0.52526032 0.1542919111 3277 416 3582
138 0.025 0.38 0.55434783 0.53130222 0.1452348496 3236 410 3623
139 0.025 0.39 0.56413043 0.54017226 0.1291215113 3176 401 3683
140 0.025 0.40 0.56847826 0.54801388 0.1960342484 3119 397 3740
141 0.025 0.41 0.58043478 0.55739812 0.1435203140 3057 386 3802
142 0.025 0.42 0.58804348 0.56433989 0.1313587128 3010 379 3849
143 0.025 0.43 0.59673913 0.57269572 0.1249114377 2953 371 3906
144 0.025 0.44 0.60434783 0.58233706 0.1597077394 2885 364 3974
145 0.025 0.45 0.61521739 0.59107854 0.1210747822 2827 354 4032
146 0.025 0.46 0.62608696 0.59917727 0.0822323694 2774 344 4085
147 0.025 0.47 0.63260870 0.60663324 0.0926307731 2722 338 4137
148 0.025 0.48 0.64021739 0.61563183 0.1103818477 2659 331 4200
149 0.025 0.49 0.65217391 0.62218794 0.0498139028 2619 320 4240
150 0.025 0.50 0.65978261 0.63002957 0.0506682696 2565 313 4294
151 0.025 0.51 0.66630435 0.63787119 0.0608702081 2510 307 4349
152 0.025 0.52 0.67934783 0.64507006 0.0227677123 2466 295 4393
153 0.025 0.53 0.68478261 0.65394010 0.0396519366 2402 290 4457
154 0.025 0.54 0.69239130 0.66113896 0.0361076054 2353 283 4506
155 0.025 0.55 0.70326087 0.67026610 0.0257654878 2292 273 4567
156 0.025 0.56 0.70760870 0.67656511 0.0351958650 2247 269 4612
157 0.025 0.57 0.71847826 0.68466384 0.0207257708 2194 259 4665
158 0.025 0.58 0.72282609 0.69250546 0.0371250676 2137 255 4722
159 0.025 0.59 0.73043478 0.70124695 0.0432284805 2076 248 4783
160 0.025 0.60 0.73804348 0.70728885 0.0319736801 2036 241 4823
161 0.025 0.61 0.74782609 0.71525903 0.0218983401 1983 232 4876
162 0.025 0.62 0.75978261 0.72207225 0.0073635084 1941 221 4918
163 0.025 0.63 0.76847826 0.73029952 0.0061582762 1885 213 4974
164 0.025 0.64 0.77282609 0.73736984 0.0103867197 1834 209 5025
165 0.025 0.65 0.78260870 0.74636843 0.0080447592 1773 200 5086
166 0.025 0.66 0.78804348 0.75279599 0.0093606034 1728 195 5131
167 0.025 0.67 0.80000000 0.76269443 0.0052507423 1662 184 5197
168 0.025 0.68 0.80978261 0.77027896 0.0027740471 1612 175 5247
169 0.025 0.69 0.81847826 0.77773493 0.0017890191 1562 167 5297
170 0.025 0.70 0.82391304 0.78377684 0.0018921480 1520 162 5339
171 0.025 0.71 0.82608696 0.78956164 0.0043531560 1477 160 5382
172 0.025 0.72 0.83152174 0.79778892 0.0076037103 1418 155 5441
173 0.025 0.73 0.84347826 0.80614475 0.0026458062 1364 144 5495
174 0.025 0.74 0.84782609 0.81244376 0.0039405563 1319 140 5540
175 0.025 0.75 0.85326087 0.81810001 0.0037473901 1280 135 5579
176 0.025 0.76 0.85760870 0.82568454 0.0075433433 1225 131 5634
177 0.025 0.77 0.86086957 0.83314051 0.0185122982 1170 128 5689
178 0.025 0.78 0.86956522 0.84021082 0.0110887842 1123 120 5736
179 0.025 0.79 0.87173913 0.84946651 0.0496682679 1053 118 5806
180 0.025 0.80 0.88152174 0.85743669 0.0296313608 1000 109 5859
181 0.025 0.81 0.88695652 0.86412135 0.0356072640 953 104 5906
182 0.025 0.82 0.89021739 0.87157732 0.0805986810 898 101 5961
183 0.025 0.83 0.89456522 0.87736213 0.1008916289 857 97 6002
184 0.025 0.84 0.90000000 0.88494665 0.1418704420 803 92 6056
185 0.025 0.85 0.90760870 0.89253117 0.1295549179 751 85 6108
186 0.025 0.86 0.91630435 0.90011570 0.0919076826 700 77 6159
187 0.025 0.87 0.92173913 0.90898573 0.1703085011 636 72 6223
188 0.025 0.88 0.92934783 0.91489909 0.1074586062 597 65 6262
189 0.025 0.89 0.93478261 0.92196940 0.1395033458 547 60 6312
190 0.025 0.90 0.94239130 0.92955393 0.1206984573 495 53 6364
191 0.025 0.91 0.94891304 0.93726700 0.1391395565 441 47 6418
192 0.025 0.92 0.95000000 0.94575138 0.5972349439 376 46 6483
193 0.025 0.93 0.95108696 0.95192184 0.9649046659 329 45 6530
194 0.025 0.94 0.95760870 0.95963491 0.8077328552 275 39 6584
195 0.025 0.95 0.96630435 0.96657668 1.0000000000 229 31 6630
196 0.025 0.96 0.97500000 0.97261859 0.7160115947 190 23 6669
197 0.025 0.97 0.97934783 0.97891760 1.0000000000 145 19 6714
198 0.025 0.98 0.98804348 0.98675922 0.8341835725 92 11 6767
199 0.025 0.99 0.99347826 0.99190127 0.7095390184 57 6 6802
200 0.025 1.00 1.00000000 1.00000000 0.0000000000 0 0 6859
201 0.050 0.01 0.10236220 0.09139992 0.1288949617 5814 1254 568
202 0.050 0.02 0.13027917 0.11633886 0.0804560429 5659 1215 723
203 0.050 0.03 0.15390122 0.13857822 0.0738837192 5519 1182 863
204 0.050 0.04 0.17465999 0.15876077 0.0792863848 5391 1153 991
205 0.050 0.05 0.19470293 0.17688649 0.0590199277 5278 1125 1104
206 0.050 0.06 0.21188261 0.19346960 0.0592814267 5173 1101 1209
207 0.050 0.07 0.22118826 0.20825299 0.2011959347 5071 1088 1311
208 0.050 0.08 0.24481031 0.22445044 0.0478964334 4978 1055 1404
209 0.050 0.09 0.26413744 0.23961949 0.0195108923 4887 1028 1495
210 0.050 0.10 0.28203293 0.25414578 0.0090753979 4799 1003 1583
211 0.050 0.11 0.29062276 0.26867207 0.0444179632 4698 991 1684
212 0.050 0.12 0.30136006 0.28242705 0.0886326326 4606 976 1776
213 0.050 0.13 0.30780243 0.29155418 0.1490807734 4544 967 1838
214 0.050 0.14 0.31710809 0.30003857 0.1323793399 4491 954 1891
215 0.050 0.15 0.32927702 0.31147962 0.1201919457 4419 937 1963
216 0.050 0.16 0.34144596 0.32202083 0.0921990292 4354 920 2028
217 0.050 0.17 0.35218325 0.33243347 0.0893868304 4288 905 2094
218 0.050 0.18 0.36649964 0.34567425 0.0757513837 4205 885 2177
219 0.050 0.19 0.37795276 0.35338732 0.0366445507 4161 869 2221
220 0.050 0.20 0.39012169 0.36290012 0.0211431931 4104 852 2278
221 0.050 0.21 0.39942734 0.37446973 0.0359590953 4027 839 2355
222 0.050 0.22 0.40873300 0.38513948 0.0488008222 3957 826 2425
223 0.050 0.23 0.41589120 0.39478082 0.0797856544 3892 816 2490
224 0.050 0.24 0.42734431 0.40416506 0.0549812681 3835 800 2547
225 0.050 0.25 0.43808160 0.41174958 0.0294184317 3791 785 2591
226 0.050 0.26 0.44810308 0.42126237 0.0268811722 3731 771 2651
227 0.050 0.27 0.45454545 0.43141792 0.0578137827 3661 762 2721
228 0.050 0.28 0.46313529 0.44183057 0.0817609506 3592 750 2790
229 0.050 0.29 0.46957767 0.45262887 0.1689975319 3517 741 2865
230 0.050 0.30 0.47816750 0.46149891 0.1769746257 3460 729 2922
231 0.050 0.31 0.49033644 0.47114025 0.1193948509 3402 712 2980
232 0.050 0.32 0.49606299 0.48168145 0.2468038564 3328 704 3054
233 0.050 0.33 0.50823193 0.49145134 0.1752389536 3269 687 3113
234 0.050 0.34 0.51753758 0.50096413 0.1808083653 3208 674 3174
235 0.050 0.35 0.52612742 0.51009127 0.1956005448 3149 662 3233
236 0.050 0.36 0.53042233 0.51690449 0.2771647792 3102 656 3280
237 0.050 0.37 0.53972799 0.52526032 0.2436277118 3050 643 3332
238 0.050 0.38 0.54760200 0.53130222 0.1874139554 3014 632 3368
239 0.050 0.39 0.55547602 0.54017226 0.2159084664 2956 621 3426
240 0.050 0.40 0.55905512 0.54801388 0.3757317781 2900 616 3482
241 0.050 0.41 0.56979241 0.55739812 0.3173236799 2842 601 3540
242 0.050 0.42 0.57551897 0.56433989 0.3678234340 2796 593 3586
243 0.050 0.43 0.58554044 0.57269572 0.2975951704 2745 579 3637
244 0.050 0.44 0.59484610 0.58233706 0.3092903020 2683 566 3699
245 0.050 0.45 0.60343593 0.59107854 0.3138544569 2627 554 3755
246 0.050 0.46 0.61488905 0.59917727 0.1960655591 2580 538 3802
247 0.050 0.47 0.62061560 0.60663324 0.2497720667 2530 530 3852
248 0.050 0.48 0.62848962 0.61563183 0.2889808436 2471 519 3911
249 0.050 0.49 0.63922691 0.62218794 0.1556853937 2435 504 3947
250 0.050 0.50 0.64566929 0.63002957 0.1915019713 2383 495 3999
251 0.050 0.51 0.65354331 0.63787119 0.1885583657 2333 484 4049
252 0.050 0.52 0.66499642 0.64507006 0.0914926271 2293 468 4089
253 0.050 0.53 0.67358626 0.65394010 0.0943015296 2236 456 4146
254 0.050 0.54 0.68002863 0.66113896 0.1061749514 2189 447 4193
255 0.050 0.55 0.69148175 0.67026610 0.0671288145 2134 431 4248
256 0.050 0.56 0.69792412 0.67656511 0.0639434548 2094 422 4288
257 0.050 0.57 0.70866142 0.68466384 0.0357806741 2046 407 4336
258 0.050 0.58 0.71438797 0.69250546 0.0542538934 1993 399 4389
259 0.050 0.59 0.72083035 0.70124695 0.0830467618 1934 390 4448
260 0.050 0.60 0.72727273 0.70728885 0.0750929841 1896 381 4486
261 0.050 0.61 0.73586256 0.71525903 0.0641390577 1846 369 4536
262 0.050 0.62 0.74588404 0.72207225 0.0307394228 1807 355 4575
263 0.050 0.63 0.75518969 0.73029952 0.0225476505 1756 342 4626
264 0.050 0.64 0.75876879 0.73736984 0.0484920464 1706 337 4676
265 0.050 0.65 0.76735863 0.74636843 0.0503686799 1648 325 4734
266 0.050 0.66 0.77165354 0.75279599 0.0767913857 1604 319 4778
267 0.050 0.67 0.78453830 0.76269443 0.0371560903 1545 301 4837
268 0.050 0.68 0.79241231 0.77027896 0.0326701562 1497 290 4885
269 0.050 0.69 0.80171797 0.77773493 0.0190376533 1452 277 4930
270 0.050 0.70 0.80672870 0.78377684 0.0235259619 1412 270 4970
271 0.050 0.71 0.81245526 0.78956164 0.0225260164 1375 262 5007
272 0.050 0.72 0.81961346 0.79778892 0.0274218770 1321 252 5061
273 0.050 0.73 0.82963493 0.80614475 0.0157502116 1270 238 5112
274 0.050 0.74 0.83464567 0.81244376 0.0209354623 1228 231 5154
275 0.050 0.75 0.84323550 0.81810001 0.0080382503 1196 219 5186
276 0.050 0.76 0.84753042 0.82568454 0.0194270217 1143 213 5239
277 0.050 0.77 0.85182534 0.83314051 0.0425275025 1091 207 5291
278 0.050 0.78 0.85898354 0.84021082 0.0380916492 1046 197 5336
279 0.050 0.79 0.86327845 0.84946651 0.1205316666 980 191 5402
280 0.050 0.80 0.87329993 0.85743669 0.0672457193 932 177 5450
281 0.050 0.81 0.87974230 0.86412135 0.0660534710 889 168 5493
282 0.050 0.82 0.88403722 0.87157732 0.1355228449 837 162 5545
283 0.050 0.83 0.89047960 0.87736213 0.1084607667 801 153 5581
284 0.050 0.84 0.89620616 0.88494665 0.1585929465 750 145 5632
285 0.050 0.85 0.90193271 0.89253117 0.2282199550 699 137 5683
286 0.050 0.86 0.90909091 0.90011570 0.2356540264 650 127 5732
287 0.050 0.87 0.91696492 0.90898573 0.2742209744 592 116 5790
288 0.050 0.88 0.92340730 0.91489909 0.2280803893 555 107 5827
289 0.050 0.89 0.92913386 0.92196940 0.2950207156 508 99 5874
290 0.050 0.90 0.93486042 0.92955393 0.4248773780 457 91 5925
291 0.050 0.91 0.94345025 0.93726700 0.3215188788 409 79 5973
292 0.050 0.92 0.94774517 0.94575138 0.7656858270 349 73 6033
293 0.050 0.93 0.94989263 0.95192184 0.7471679339 304 70 6078
294 0.050 0.94 0.95633500 0.95963491 0.5373439788 253 61 6129
295 0.050 0.95 0.96564066 0.96657668 0.8944112744 212 48 6170
296 0.050 0.96 0.97351467 0.97261859 0.8917549403 176 37 6206
297 0.050 0.97 0.97995705 0.97891760 0.8447918536 136 28 6246
298 0.050 0.98 0.98854689 0.98675922 0.6057459988 87 16 6295
299 0.050 0.99 0.99355762 0.99190127 0.5499684387 54 9 6328
300 0.050 1.00 1.00000000 1.00000000 0.0000000000 0 0 6382
301 0.075 0.01 0.10000000 0.09139992 0.1670912629 5475 1593 534
302 0.075 0.02 0.12598870 0.11633886 0.1619662765 5327 1547 682
303 0.075 0.03 0.14745763 0.13857822 0.2336288099 5192 1509 817
304 0.075 0.04 0.16892655 0.15876077 0.1954762458 5073 1471 936
305 0.075 0.05 0.18870056 0.17688649 0.1479987686 4967 1436 1042
306 0.075 0.06 0.20564972 0.19346960 0.1493716703 4868 1406 1141
307 0.075 0.07 0.21581921 0.20825299 0.3905374597 4771 1388 1238
308 0.075 0.08 0.23785311 0.22445044 0.1322477932 4684 1349 1325
309 0.075 0.09 0.25536723 0.23961949 0.0828621846 4597 1318 1412
310 0.075 0.10 0.27570621 0.25414578 0.0193135369 4520 1282 1489
311 0.075 0.11 0.28813559 0.26867207 0.0383266453 4429 1260 1580
312 0.075 0.12 0.30169492 0.28242705 0.0435141535 4346 1236 1663
313 0.075 0.13 0.31016949 0.29155418 0.0534932312 4290 1221 1719
314 0.075 0.14 0.31920904 0.30003857 0.0485056869 4240 1205 1769
315 0.075 0.15 0.33050847 0.31147962 0.0526572713 4171 1185 1838
316 0.075 0.16 0.34180791 0.32202083 0.0456973155 4109 1165 1900
317 0.075 0.17 0.35141243 0.33243347 0.0574596743 4045 1148 1964
318 0.075 0.18 0.36779661 0.34567425 0.0279349276 3971 1119 2038
319 0.075 0.19 0.37740113 0.35338732 0.0174819385 3928 1102 2081
320 0.075 0.20 0.38926554 0.36290012 0.0094149022 3875 1081 2134
321 0.075 0.21 0.39887006 0.37446973 0.0170625552 3802 1064 2207
322 0.075 0.22 0.40960452 0.38513948 0.0173705519 3738 1045 2271
323 0.075 0.23 0.41694915 0.39478082 0.0320916155 3676 1032 2333
324 0.075 0.24 0.42881356 0.40416506 0.0174643585 3624 1011 2385
325 0.075 0.25 0.43898305 0.41174958 0.0087583371 3583 993 2426
326 0.075 0.26 0.44971751 0.42126237 0.0063098747 3528 974 2481
327 0.075 0.27 0.45762712 0.43141792 0.0122169137 3463 960 2546
328 0.075 0.28 0.46553672 0.44183057 0.0239562038 3396 946 2613
329 0.075 0.29 0.47231638 0.45262887 0.0620185669 3324 934 2685
330 0.075 0.30 0.47966102 0.46149891 0.0860103346 3268 921 2741
331 0.075 0.31 0.49152542 0.47114025 0.0538831994 3214 900 2795
332 0.075 0.32 0.49717514 0.48168145 0.1450486385 3142 890 2867
333 0.075 0.33 0.50847458 0.49145134 0.1089491521 3086 870 2923
334 0.075 0.34 0.51751412 0.50096413 0.1193764162 3028 854 2981
335 0.075 0.35 0.52542373 0.51009127 0.1495494567 2971 840 3038
336 0.075 0.36 0.53050847 0.51690449 0.2019261349 2927 831 3082
337 0.075 0.37 0.53898305 0.52526032 0.1976181710 2877 816 3132
338 0.075 0.38 0.54576271 0.53130222 0.1738237005 2842 804 3167
339 0.075 0.39 0.55367232 0.54017226 0.2042600093 2787 790 3222
340 0.075 0.40 0.55875706 0.54801388 0.3143581248 2735 781 3274
341 0.075 0.41 0.56836158 0.55739812 0.3033087956 2679 764 3330
342 0.075 0.42 0.57457627 0.56433989 0.3365823758 2636 753 3373
343 0.075 0.43 0.58418079 0.57269572 0.2783583525 2588 736 3421
344 0.075 0.44 0.59322034 0.58233706 0.3035128967 2529 720 3480
345 0.075 0.45 0.60282486 0.59107854 0.2643447654 2478 703 3531
346 0.075 0.46 0.61299435 0.59917727 0.1861607480 2433 685 3576
347 0.075 0.47 0.61920904 0.60663324 0.2283454665 2386 674 3623
348 0.075 0.48 0.62655367 0.61563183 0.2951197233 2329 661 3680
349 0.075 0.49 0.63559322 0.62218794 0.1951091755 2294 645 3715
350 0.075 0.50 0.64237288 0.63002957 0.2317724209 2245 633 3764
351 0.075 0.51 0.64858757 0.63787119 0.2987154814 2195 622 3814
352 0.075 0.52 0.65875706 0.64507006 0.1799254538 2157 604 3852
353 0.075 0.53 0.66610169 0.65394010 0.2319591773 2101 591 3908
354 0.075 0.54 0.67231638 0.66113896 0.2705353083 2056 580 3953
355 0.075 0.55 0.68418079 0.67026610 0.1651189420 2006 559 4003
356 0.075 0.56 0.68983051 0.67656511 0.1840031679 1967 549 4042
357 0.075 0.57 0.70000000 0.68466384 0.1209422647 1922 531 4087
358 0.075 0.58 0.70508475 0.69250546 0.2021025773 1870 522 4139
359 0.075 0.59 0.71186441 0.70124695 0.2797641636 1814 510 4195
360 0.075 0.60 0.71920904 0.70728885 0.2208298780 1780 497 4229
361 0.075 0.61 0.72824859 0.71525903 0.1777119689 1734 481 4275
362 0.075 0.62 0.73672316 0.72207225 0.1247038493 1696 466 4313
363 0.075 0.63 0.74576271 0.73029952 0.1015523424 1648 450 4361
364 0.075 0.64 0.74915254 0.73736984 0.2109543728 1599 444 4410
365 0.075 0.65 0.75706215 0.74636843 0.2520280732 1543 430 4466
366 0.075 0.66 0.76214689 0.75279599 0.3142879392 1502 421 4507
367 0.075 0.67 0.77457627 0.76269443 0.1918505232 1447 399 4562
368 0.075 0.68 0.78305085 0.77027896 0.1552495188 1403 384 4606
369 0.075 0.69 0.79152542 0.77773493 0.1198974673 1360 369 4649
370 0.075 0.70 0.79717514 0.78377684 0.1272365815 1323 359 4686
371 0.075 0.71 0.80395480 0.78956164 0.0975061643 1290 347 4719
372 0.075 0.72 0.81242938 0.79778892 0.0870490519 1241 332 4768
373 0.075 0.73 0.82033898 0.80614475 0.0920754783 1190 318 4819
374 0.075 0.74 0.82711864 0.81244376 0.0775818707 1153 306 4856
375 0.075 0.75 0.83446328 0.81810001 0.0459967803 1122 293 4887
376 0.075 0.76 0.84180791 0.82568454 0.0456386778 1076 280 4933
377 0.075 0.77 0.84632768 0.83314051 0.0975673519 1026 272 4983
378 0.075 0.78 0.85367232 0.84021082 0.0851204322 984 259 5025
379 0.075 0.79 0.85762712 0.84946651 0.2916165607 919 252 5090
380 0.075 0.80 0.86779661 0.85743669 0.1676713288 875 234 5134
381 0.075 0.81 0.87401130 0.86412135 0.1795553551 834 223 5175
382 0.075 0.82 0.87853107 0.87157732 0.3398241684 784 215 5225
383 0.075 0.83 0.88587571 0.87736213 0.2296867363 752 202 5257
384 0.075 0.84 0.89378531 0.88494665 0.1992932072 707 188 5302
385 0.075 0.85 0.89943503 0.89253117 0.3061222944 658 178 5351
386 0.075 0.86 0.90847458 0.90011570 0.1972802380 615 162 5394
387 0.075 0.87 0.91525424 0.90898573 0.3191465048 558 150 5451
388 0.075 0.88 0.92146893 0.91489909 0.2807647897 523 139 5486
389 0.075 0.89 0.92824859 0.92196940 0.2845250215 480 127 5529
390 0.075 0.90 0.93502825 0.92955393 0.3314554009 433 115 5576
391 0.075 0.91 0.94237288 0.93726700 0.3410040744 386 102 5623
392 0.075 0.92 0.94915254 0.94575138 0.5098477978 332 90 5677
393 0.075 0.93 0.95197740 0.95192184 1.0000000000 289 85 5720
394 0.075 0.94 0.95988701 0.95963491 1.0000000000 243 71 5766
395 0.075 0.95 0.96723164 0.96657668 0.9209819917 202 58 5807
396 0.075 0.96 0.97570621 0.97261859 0.4106125519 170 43 5839
397 0.075 0.97 0.98192090 0.97891760 0.3646202237 132 32 5877
398 0.075 0.98 0.98926554 0.98675922 0.3517022313 84 19 5925
399 0.075 0.99 0.99435028 0.99190127 0.2472343305 53 10 5956
400 0.075 1.00 1.00000000 1.00000000 0.0000000000 0 0 6009
401 0.100 0.01 0.09894837 0.09139992 0.1748343926 5183 1885 504
402 0.100 0.02 0.12380497 0.11633886 0.2279107982 5041 1833 646
403 0.100 0.03 0.14388145 0.13857822 0.4329950509 4910 1791 777
404 0.100 0.04 0.16539197 0.15876077 0.3494461846 4798 1746 889
405 0.100 0.05 0.18546845 0.17688649 0.2421549063 4699 1704 988
406 0.100 0.06 0.20076482 0.19346960 0.3392965597 4602 1672 1085
407 0.100 0.07 0.21128107 0.20825299 0.7132995342 4509 1650 1178
408 0.100 0.08 0.23279159 0.22445044 0.2988935521 4428 1605 1259
409 0.100 0.09 0.25000000 0.23961949 0.2037479604 4346 1569 1341
410 0.100 0.10 0.26912046 0.25414578 0.0702155568 4273 1529 1414
411 0.100 0.11 0.28346080 0.26867207 0.0791130987 4190 1499 1497
412 0.100 0.12 0.29684512 0.28242705 0.0920164036 4111 1471 1576
413 0.100 0.13 0.30592734 0.29155418 0.0961854950 4059 1452 1628
414 0.100 0.14 0.31500956 0.30003857 0.0854983568 4012 1433 1675
415 0.100 0.15 0.32648184 0.31147962 0.0881328106 3947 1409 1740
416 0.100 0.16 0.34034417 0.32202083 0.0384152171 3894 1380 1793
417 0.100 0.17 0.35038241 0.33243347 0.0443224225 3834 1359 1853
418 0.100 0.18 0.36615679 0.34567425 0.0227881594 3764 1326 1923
419 0.100 0.19 0.37428298 0.35338732 0.0207995099 3721 1309 1966
420 0.100 0.20 0.38671128 0.36290012 0.0087306339 3673 1283 2014
421 0.100 0.21 0.39818356 0.37446973 0.0094700190 3607 1259 2080
422 0.100 0.22 0.40774379 0.38513948 0.0139500426 3544 1239 2143
423 0.100 0.23 0.41586998 0.39478082 0.0225016288 3486 1222 2201
424 0.100 0.24 0.42734226 0.40416506 0.0124036186 3437 1198 2250
425 0.100 0.25 0.43785851 0.41174958 0.0049251169 3400 1176 2287
426 0.100 0.26 0.44741874 0.42126237 0.0049872688 3346 1156 2341
427 0.100 0.27 0.45697897 0.43141792 0.0062384063 3287 1136 2400
428 0.100 0.28 0.46558317 0.44183057 0.0113139657 3224 1118 2463
429 0.100 0.29 0.47514340 0.45262887 0.0166675406 3160 1098 2527
430 0.100 0.30 0.48374761 0.46149891 0.0181882054 3109 1080 2578
431 0.100 0.31 0.49521989 0.47114025 0.0106219073 3058 1056 2629
432 0.100 0.32 0.50286807 0.48168145 0.0249207873 2992 1040 2695
433 0.100 0.33 0.51386233 0.49145134 0.0176700129 2939 1017 2748
434 0.100 0.34 0.52198853 0.50096413 0.0261638611 2882 1000 2805
435 0.100 0.35 0.53059273 0.51009127 0.0301387856 2829 982 2858
436 0.100 0.36 0.53537285 0.51690449 0.0510070766 2786 972 2901
437 0.100 0.37 0.54254302 0.52526032 0.0678824821 2736 957 2951
438 0.100 0.38 0.54875717 0.53130222 0.0649644977 2702 944 2985
439 0.100 0.39 0.55640535 0.54017226 0.0860321938 2649 928 3038
440 0.100 0.40 0.56261950 0.54801388 0.1225453597 2601 915 3086
441 0.100 0.41 0.57217973 0.55739812 0.1172961677 2548 895 3139
442 0.100 0.42 0.57743786 0.56433989 0.1653576931 2505 884 3182
443 0.100 0.43 0.58652008 0.57269572 0.1418148028 2459 865 3228
444 0.100 0.44 0.59703633 0.58233706 0.1167707562 2406 843 3281
445 0.100 0.45 0.60659656 0.59107854 0.0964186502 2358 823 3329
446 0.100 0.46 0.61663480 0.59917727 0.0601757813 2316 802 3371
447 0.100 0.47 0.62284895 0.60663324 0.0801960747 2271 789 3416
448 0.100 0.48 0.63049713 0.61563183 0.1077417725 2217 773 3470
449 0.100 0.49 0.63957935 0.62218794 0.0584293388 2185 754 3502
450 0.100 0.50 0.64674952 0.63002957 0.0678384877 2139 739 3548
451 0.100 0.51 0.65344168 0.63787119 0.0879289043 2092 725 3595
452 0.100 0.52 0.66300191 0.64507006 0.0479298582 2056 705 3631
453 0.100 0.53 0.66969407 0.65394010 0.0810463979 2001 691 3686
454 0.100 0.54 0.67543021 0.66113896 0.1122540945 1957 679 3730
455 0.100 0.55 0.68690249 0.67026610 0.0620663291 1910 655 3777
456 0.100 0.56 0.69263862 0.67656511 0.0701802594 1873 643 3814
457 0.100 0.57 0.70315488 0.68466384 0.0356152019 1832 621 3855
458 0.100 0.58 0.70936902 0.69250546 0.0539579893 1784 608 3903
459 0.100 0.59 0.71558317 0.70124695 0.0994416517 1729 595 3958
460 0.100 0.60 0.72275335 0.70728885 0.0734531159 1697 580 3990
461 0.100 0.61 0.73040153 0.71525903 0.0772991239 1651 564 4036
462 0.100 0.62 0.73852772 0.72207225 0.0528153685 1615 547 4072
463 0.100 0.63 0.74808795 0.73029952 0.0344041191 1571 527 4116
464 0.100 0.64 0.75239006 0.73736984 0.0723697620 1525 518 4162
465 0.100 0.65 0.76003824 0.74636843 0.0986781362 1471 502 4216
466 0.100 0.66 0.76577438 0.75279599 0.1141684973 1433 490 4254
467 0.100 0.67 0.77772467 0.76269443 0.0629078951 1381 465 4306
468 0.100 0.68 0.78537285 0.77027896 0.0588836109 1338 449 4349
469 0.100 0.69 0.79302103 0.77773493 0.0528692646 1296 433 4391
470 0.100 0.70 0.79923518 0.78377684 0.0479681019 1262 420 4425
471 0.100 0.71 0.80544933 0.78956164 0.0400109307 1230 407 4457
472 0.100 0.72 0.81405354 0.79778892 0.0328126782 1184 389 4503
473 0.100 0.73 0.82265774 0.80614475 0.0276536611 1137 371 4550
474 0.100 0.74 0.82887189 0.81244376 0.0265198093 1101 358 4586
475 0.100 0.75 0.83604207 0.81810001 0.0140933136 1072 343 4615
476 0.100 0.76 0.84273423 0.82568454 0.0177715158 1027 329 4660
477 0.100 0.77 0.84799235 0.83314051 0.0360351751 980 318 4707
478 0.100 0.78 0.85516252 0.84021082 0.0317172026 940 303 4747
479 0.100 0.79 0.86042065 0.84946651 0.1089546855 879 292 4808
480 0.100 0.80 0.86998088 0.85743669 0.0597391380 837 272 4850
481 0.100 0.81 0.87619503 0.86412135 0.0646686333 798 259 4889
482 0.100 0.82 0.88145315 0.87157732 0.1233524958 751 248 4936
483 0.100 0.83 0.88814532 0.87736213 0.0855161374 720 234 4967
484 0.100 0.84 0.89483748 0.88494665 0.1056436566 675 220 5012
485 0.100 0.85 0.90009560 0.89253117 0.2057770083 627 209 5060
486 0.100 0.86 0.90965583 0.90011570 0.0970444923 588 189 5099
487 0.100 0.87 0.91682600 0.90898573 0.1574532744 534 174 5153
488 0.100 0.88 0.92256214 0.91489909 0.1546570371 500 162 5187
489 0.100 0.89 0.93021033 0.92196940 0.1105122462 461 146 5226
490 0.100 0.90 0.93642447 0.92955393 0.1656624558 415 133 5272
491 0.100 0.91 0.94407266 0.93726700 0.1474335844 371 117 5316
492 0.100 0.92 0.95172084 0.94575138 0.1759466731 321 101 5366
493 0.100 0.93 0.95506692 0.95192184 0.4674320924 280 94 5407
494 0.100 0.94 0.96271511 0.95963491 0.4399803393 236 78 5451
495 0.100 0.95 0.96988528 0.96657668 0.3609463657 197 63 5490
496 0.100 0.96 0.97705545 0.97261859 0.1688109203 165 48 5522
497 0.100 0.97 0.98279159 0.97891760 0.1758839296 128 36 5559
498 0.100 0.98 0.98948375 0.98675922 0.2447468853 81 22 5606
499 0.100 0.99 0.99426386 0.99190127 0.2049974605 51 12 5636
500 0.100 1.00 1.00000000 1.00000000 0.0000000000 0 0 5687
Dboth
1 48
2 59
3 71
4 80
5 87
6 95
7 100
8 113
9 122
10 132
11 137
12 142
13 145
14 150
15 157
16 162
17 169
18 175
19 181
20 184
21 188
22 189
23 193
24 198
25 202
26 205
27 212
28 216
29 219
30 222
31 227
32 228
33 234
34 238
35 243
36 245
37 248
38 251
39 256
40 257
41 264
42 266
43 271
44 277
45 283
46 288
47 291
48 295
49 302
50 304
51 306
52 314
53 317
54 318
55 325
56 327
57 332
58 334
59 338
60 339
61 345
62 350
63 355
64 358
65 363
66 365
67 371
68 375
69 377
70 379
71 379
72 382
73 386
74 388
75 390
76 392
77 394
78 397
79 398
80 401
81 404
82 406
83 407
84 410
85 412
86 415
87 417
88 422
89 424
90 426
91 430
92 431
93 432
94 436
95 438
96 443
97 445
98 449
99 453
100 457
101 98
102 125
103 149
104 167
105 190
106 208
107 216
108 240
109 259
110 279
111 285
112 293
113 297
114 305
115 314
116 329
117 341
118 355
119 366
120 372
121 381
122 387
123 393
124 404
125 412
126 421
127 429
128 435
129 442
130 450
131 462
132 468
133 477
134 486
135 494
136 499
137 504
138 510
139 519
140 523
141 534
142 541
143 549
144 556
145 566
146 576
147 582
148 589
149 600
150 607
151 613
152 625
153 630
154 637
155 647
156 651
157 661
158 665
159 672
160 679
161 688
162 699
163 707
164 711
165 720
166 725
167 736
168 745
169 753
170 758
171 760
172 765
173 776
174 780
175 785
176 789
177 792
178 800
179 802
180 811
181 816
182 819
183 823
184 828
185 835
186 843
187 848
188 855
189 860
190 867
191 873
192 874
193 875
194 881
195 889
196 897
197 901
198 909
199 914
200 920
201 143
202 182
203 215
204 244
205 272
206 296
207 309
208 342
209 369
210 394
211 406
212 421
213 430
214 443
215 460
216 477
217 492
218 512
219 528
220 545
221 558
222 571
223 581
224 597
225 612
226 626
227 635
228 647
229 656
230 668
231 685
232 693
233 710
234 723
235 735
236 741
237 754
238 765
239 776
240 781
241 796
242 804
243 818
244 831
245 843
246 859
247 867
248 878
249 893
250 902
251 913
252 929
253 941
254 950
255 966
256 975
257 990
258 998
259 1007
260 1016
261 1028
262 1042
263 1055
264 1060
265 1072
266 1078
267 1096
268 1107
269 1120
270 1127
271 1135
272 1145
273 1159
274 1166
275 1178
276 1184
277 1190
278 1200
279 1206
280 1220
281 1229
282 1235
283 1244
284 1252
285 1260
286 1270
287 1281
288 1290
289 1298
290 1306
291 1318
292 1324
293 1327
294 1336
295 1349
296 1360
297 1369
298 1381
299 1388
300 1397
301 177
302 223
303 261
304 299
305 334
306 364
307 382
308 421
309 452
310 488
311 510
312 534
313 549
314 565
315 585
316 605
317 622
318 651
319 668
320 689
321 706
322 725
323 738
324 759
325 777
326 796
327 810
328 824
329 836
330 849
331 870
332 880
333 900
334 916
335 930
336 939
337 954
338 966
339 980
340 989
341 1006
342 1017
343 1034
344 1050
345 1067
346 1085
347 1096
348 1109
349 1125
350 1137
351 1148
352 1166
353 1179
354 1190
355 1211
356 1221
357 1239
358 1248
359 1260
360 1273
361 1289
362 1304
363 1320
364 1326
365 1340
366 1349
367 1371
368 1386
369 1401
370 1411
371 1423
372 1438
373 1452
374 1464
375 1477
376 1490
377 1498
378 1511
379 1518
380 1536
381 1547
382 1555
383 1568
384 1582
385 1592
386 1608
387 1620
388 1631
389 1643
390 1655
391 1668
392 1680
393 1685
394 1699
395 1712
396 1727
397 1738
398 1751
399 1760
400 1770
401 207
402 259
403 301
404 346
405 388
406 420
407 442
408 487
409 523
410 563
411 593
412 621
413 640
414 659
415 683
416 712
417 733
418 766
419 783
420 809
421 833
422 853
423 870
424 894
425 916
426 936
427 956
428 974
429 994
430 1012
431 1036
432 1052
433 1075
434 1092
435 1110
436 1120
437 1135
438 1148
439 1164
440 1177
441 1197
442 1208
443 1227
444 1249
445 1269
446 1290
447 1303
448 1319
449 1338
450 1353
451 1367
452 1387
453 1401
454 1413
455 1437
456 1449
457 1471
458 1484
459 1497
460 1512
461 1528
462 1545
463 1565
464 1574
465 1590
466 1602
467 1627
468 1643
469 1659
470 1672
471 1685
472 1703
473 1721
474 1734
475 1749
476 1763
477 1774
478 1789
479 1800
480 1820
481 1833
482 1844
483 1858
484 1872
485 1883
486 1903
487 1918
488 1930
489 1946
490 1959
491 1975
492 1991
493 1998
494 2014
495 2029
496 2044
497 2056
498 2070
499 2080
500 2092#Custom function for doing this for the paper:
pap.enrichment.plotter <- function(df, HiC_col, DE_col, xlab, xmax=0.3, i=c(0.01, 0.025, 0.05, 0.075, 0.1), k=seq(0.01, 1, 0.01), significance=FALSE, recip=FALSE){
enrich.table <- data.frame(DEFDR = c(rep(i[1], 100), rep(i[2], 100), rep(i[3], 100), rep(i[4], 100), rep(i[5], 100)), DHICFDR=rep(k, 5), prop.obs=NA, prop.exp=NA, chisq.p=NA, Dneither=NA, DE=NA, DHiC=NA, Dboth=NA)
for(de.FDR in i){
for(hic.FDR in k){
enrich.table[which(enrich.table$DEFDR==de.FDR&enrich.table$DHICFDR==hic.FDR), 3:9] <- prop.calculator(df[,DE_col], df[,HiC_col], de.FDR, hic.FDR)
}
}
des.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=prop.obs, group=as.factor(DEFDR), color=as.factor(DEFDR))) +geom_line()+ geom_line(aes(y=prop.exp), linetype="dashed", size=0.5) + ggtitle("Enrichment of DC in DE Genes") + xlab(xlab) + ylab("Proportion of DE genes that are DC") + guides(color=guide_legend(title="FDR for DE Genes"))
dhics.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dboth+DHiC), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + geom_line(aes(y=(((((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth)))*(Dneither+DE+DHiC+Dboth))/(DHiC+Dboth))), linetype="dashed") + ylab("Proportion of DC genes that are DE") +xlab(xlab) + ggtitle("Enrichment of DE in DC Genes") + coord_cartesian(xlim=c(0, xmax), ylim=c(0.05, 0.32)) + guides(color=FALSE)
joint.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dneither+DE+DHiC+Dboth), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + ylab("Proportion of ALL Genes both DE & DHi-C") + xlab(xlab) + geom_line(aes(y=((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth))), linetype="dashed") + ggtitle("Enrichment of Joint DE & DHi-C in All Genes")
chisq.p <- ggplot(data=enrich.table, aes(x=DHICFDR, y=-log10(chisq.p), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_point() + geom_hline(yintercept=-log10(0.05), color="red") + ggtitle("Chi-squared Test P-values") + xlab(xlab) + ylab("-log10(chi-squared p-values)") + coord_cartesian(xlim=c(0, xmax), ylim=c(0, 3.2)) + guides(color=guide_legend(title="DE FDR"))
if(recip==TRUE){
des.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=prop.obs, group=as.factor(DEFDR), color=as.factor(DEFDR))) +geom_line()+ geom_line(aes(y=prop.exp), linetype="dashed", size=0.5) + ggtitle("Enrichment of DE in DC Genes") + xlab(xlab) + ylab("Proportion of DC genes that are DE") + guides(color=guide_legend(title="FDR for DC Genes"))
dhics.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dboth+DHiC), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + geom_line(aes(y=(((((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth)))*(Dneither+DE+DHiC+Dboth))/(DHiC+Dboth))), linetype="dashed") + ylab("Proportion of DE genes that are DC") +xlab(xlab) + ggtitle("Enrichment of DC in DE Genes") + coord_cartesian(xlim=c(0, xmax)) + guides(color=FALSE)
joint.enriched <- ggplot(data=enrich.table, aes(x=DHICFDR, y=Dboth/(Dneither+DE+DHiC+Dboth), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_line() + ylab("Proportion of ALL Genes both DE & DHi-C") + xlab(xlab) + geom_line(aes(y=((DE+Dboth)/(Dneither+DE+DHiC+Dboth))*((DHiC+Dboth)/(Dneither+DE+DHiC+Dboth))), linetype="dashed") + ggtitle("Enrichment of Joint DE & DHi-C in All Genes")
chisq.p <- ggplot(data=enrich.table, aes(x=DHICFDR, y=-log10(chisq.p), group=as.factor(DEFDR), color=as.factor(DEFDR))) + geom_point() + geom_hline(yintercept=-log10(0.05), color="red") + ggtitle("Chi-squared Test P-values") + xlab(xlab) + ylab("-log10(chi-squared p-values)") + coord_cartesian(xlim=c(0, xmax)) + guides(color=guide_legend(title="DC FDR"))
}
if(significance==TRUE){
return(chisq.p)
}
else{
return(dhics.enriched)
}
}
#FIG6
FIG6A <- pap.enrichment.plotter(gene.hic.filt, "min_FDR.H", "adj.P.Val", "Minimum FDR of Contacts", xmax=1) #FIG6AWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrectFIG6B <- pap.enrichment.plotter(gene.hic.filt, "min_FDR.H", "adj.P.Val", "Minimum FDR of Contacts", xmax=1, significance = TRUE) #FIG6BWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect#FIG6C <- pap.enrichment.plotter(gene.hic.filt, "weighted_Z.ALLvar.H", "adj.P.Val", "FDR for Weighted P-val Combination", xmax=1) #FIG6C
#FIG6D <- pap.enrichment.plotter(gene.hic.filt, "weighted_Z.ALLvar.H", "adj.P.Val", "FDR for Weighted P-val Combination", xmax=1, significance = TRUE) #FIG6D
FIG6 <- plot_grid(FIG6A, FIG6B, labels=c("A", "B"), align="h", rel_widths=c(1, 1.2))
save_plot("~/Desktop/FIG6.tiff", FIG6, nrow=1, ncol=2) #Good again, but needs to be copied into photoshop to reduce size!
#FIGS19
FIGS19A <- pap.enrichment.plotter(gene.hic.filt, "adj.P.Val", "min_FDR.H", "Minimum FDR of Genes", xmax=1, recip=TRUE) #FIGS19AWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrectFIGS19B <- pap.enrichment.plotter(gene.hic.filt, "adj.P.Val", "min_FDR.H", "Minimum FDR of Genes", xmax=1, significance = TRUE, recip=TRUE) #FIGS19BWarning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrect
Warning in chisq.test(mytable): Chi-squared approximation may be incorrectFIGS19 <- plot_grid(FIGS19A, FIGS19B, labels=c("A", "B"), align="h", rel_widths=c(1, 1.2))
save_plot("~/Desktop/FIGS19.png", FIGS19, nrow=1, ncol=2)Now that I’ve seen some enrichment of differential contact (DC) in differential expression (DE) based on my linear modeling results from before, I would like to further quantify this effect. In order to do so, I now extract log2 observed/expected homer-normalized contact frequency values and RPKM expression values for each gene/bin set, so I can look at correlations of these values and assess their explanatory power.
###Contact Frequency Extraction In this section, I proceed to create a function in order to extract the Hi-C interaction frequency values for the different types of summaries I’ve made gene overlaps with above. This allows for operations to be performed separately utilizing different summaries of Hi-C contacts.
#Get a df with the H and C coordinates of the hits, and the IF values from homer. This subset df makes things easier to extract.
contacts <- data.frame(h1=data.filtered$H1, h2=data.filtered$H2, c1=data.filtered$C1, c2=data.filtered$C2, A_21792_HIC=data.filtered$`A-21792_norm`, B_28126_HIC=data.filtered$`B-28126_norm`, C_3649_HIC=data.filtered$`C-3649_norm`, D_40300_HIC=data.filtered$`D-40300_norm`, E_28815_HIC=data.filtered$`E-28815_norm`, F_28834_HIC=data.filtered$`F-28834_norm`, G_3624_HIC=data.filtered$`G-3624_norm`, H_3651_HIC=data.filtered$`H-3651_norm`, stringsAsFactors = FALSE)
#Now ensure first member of a pair is always lower than second:
newH1 <- as.numeric(gsub(".*-", "", contacts$h1))
newH2 <- as.numeric(gsub(".*-", "", contacts$h2))
lower.HID <- ifelse(newH1<newH2, contacts$h1, contacts$h2)
higher.HID2 <- ifelse(newH1<newH2, contacts$h2, contacts$h1)
contacts$hpair <- paste(lower.HID, higher.HID2, sep="_")
newC1 <- as.numeric(gsub(".*-", "", contacts$c1))
newC2 <- as.numeric(gsub(".*-", "", contacts$c2))
lower.CID <- ifelse(newC1<newC2, contacts$c1, contacts$c2)
higher.CID2 <- ifelse(newC1<newC2, contacts$c2, contacts$c1)
contacts$cpair <- paste(lower.CID, higher.CID2, sep="_")
#A function that takes a dataframe (like gene.hic.filt) and two columns from the dataframe to create a pair vector for the given interaction. First ensures the first bin in a pair is always lowest to make this easier. Then extracts the IF values for that vector from the contacts df created above. This provides me with the appropriate Hi-C data values for the different bin classes we're examining here, so that I can later test them with linear modeling to quantify their effect on expression.
IF.extractor <- function(dataframe, col1, col2, contacts, species, strand=FALSE){
new1 <- as.numeric(gsub(".*-", "", dataframe[,col1]))
if(strand==FALSE){#In the case where I'm not worried about strand, I just work with the second column selected.
new2 <- as.numeric(gsub(".*-", "", dataframe[,col2]))
lower1 <- ifelse(new1<new2, dataframe[,col1], dataframe[,col2])
higher2 <- ifelse(new1<new2, dataframe[,col2], dataframe[,col1]) #Fix all the columns first
if(species=="H"){ #Then depending on species create the pair column and merge to contact info.
dataframe[,"hpair"] <- paste(lower1, higher2, sep="_")
finaldf <- left_join(dataframe, contacts[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "hpair")], by="hpair")
}
else if(species=="C"){
dataframe[,"cpair"] <- paste(lower1, higher2, sep="_")
finaldf <- left_join(dataframe, contacts[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "cpair")], by="cpair")
}
}
else if(strand==TRUE){#If dealing with upstream and downstream hits, need to do things separately for genes on the + and - strand.
if(species=="H"){
US <- ifelse(dataframe[,"Hstrand.H"]=="+", dataframe[,"US_bin.H"], dataframe[,"DS_bin.H"]) #Obtain upstream bins depending on strand.
new2 <- as.numeric(gsub(".*-", "", US)) #Now rearrange the pairs to ensure regardless of stream we can find the pair (first mate lower coordinates than 2nd).
lower1 <- ifelse(new1<new2, dataframe[,col1], US)
higher2 <- ifelse(new1<new2, US, dataframe[,col1])
dataframe[,"hpair"] <- paste(lower1, higher2, sep="_")
dataframe[,"USFDR"] <- ifelse(dataframe[,"Hstrand.H"]=="+", dataframe[,"US_FDR.H"], dataframe[,"DS_FDR.H"])
finaldf <- left_join(dataframe, contacts[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "hpair")], by="hpair")
}
else if(species=="C"){
US <- ifelse(dataframe[,"Hstrand.C"]=="+", dataframe[,"US_bin.C"], dataframe[,"DS_bin.C"]) #Obtain upstream bins depending on strand.
new2 <- as.numeric(gsub(".*-", "", US)) #Now rearrange the pairs to ensure regardless of stream we can find the pair (first mate lower coordinates than 2nd).
lower1 <- ifelse(new1<new2, dataframe[,col1], US)
higher2 <- ifelse(new1<new2, US, dataframe[,col1])
dataframe[,"cpair"] <- paste(lower1, higher2, sep="_")
dataframe[,"USFDR"] <- ifelse(dataframe[,"Hstrand.C"]=="+", dataframe[,"US_FDR.C"], dataframe[,"DS_FDR.C"])
finaldf <- left_join(dataframe, contacts[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "cpair")], by="cpair")
}
}
#before finally returning, remove rows where we don't have full Hi-C data.
finaldf <- finaldf[complete.cases(finaldf[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC")]),]
return(finaldf)
}
#Now I can use the IF.extractor function to make a number of different dataframes for actually testing different IF values with the RPKM expression values.
h_minFDR <- IF.extractor(gene.hic.filt, "HID", "min_FDR_bin.H", contacts, "H")
c_minFDR <- IF.extractor(gene.hic.filt, "CID", "min_FDR_bin.C", contacts, "C")
h_maxB <- IF.extractor(gene.hic.filt, "HID", "max_B_bin.H", contacts, "H")
c_maxB <- IF.extractor(gene.hic.filt, "CID", "max_B_bin.C", contacts, "C")
h_US <- IF.extractor(gene.hic.filt, "HID", "US_bin.H", contacts, "H", TRUE)
c_US <- IF.extractor(gene.hic.filt, "CID", "US_bin.C", contacts, "C", TRUE)
#Write these out so they can be permuted upon on midway2.
fwrite(h_minFDR, "data/old_mediation_permutations/HiC_covs/h_minFDR", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_minFDR, "data/old_mediation_permutations/HiC_covs/c_minFDR", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(h_maxB, "data/old_mediation_permutations/HiC_covs/h_maxB", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_maxB, "data/old_mediation_permutations/HiC_covs/c_maxB", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(h_US, "data/old_mediation_permutations/HiC_covs/h_US", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_US, "data/old_mediation_permutations/HiC_covs/c_US", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
#Now the same thing, but subsetting down to ONLY the genes that show evidence for DE at 5% FDR.
h_minFDR_DE <- filter(h_minFDR, adj.P.Val<=0.05)
c_minFDR_DE <- filter(c_minFDR, adj.P.Val<=0.05)
h_maxB_DE <- filter(h_maxB, adj.P.Val<=0.05)
c_maxB_DE <- filter(c_maxB, adj.P.Val<=0.05)
h_US_DE <- filter(h_US, adj.P.Val<=0.05)
c_US_DE <- filter(c_US, adj.P.Val<=0.05)
fwrite(h_minFDR_DE, "data/old_mediation_permutations/HiC_covs/h_minFDR_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_minFDR_DE, "data/old_mediation_permutations/HiC_covs/c_minFDR_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(h_maxB_DE, "data/old_mediation_permutations/HiC_covs/h_maxB_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_maxB_DE, "data/old_mediation_permutations/HiC_covs/c_maxB_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(h_US_DE, "data/old_mediation_permutations/HiC_covs/h_US_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)
fwrite(c_US_DE, "data/old_mediation_permutations/HiC_covs/c_US_DE", quote = FALSE, sep = "\t", na = "NA", col.names = TRUE)Now I use these data frames and join each again on the RPKM table by genes, in order to obtain RPKM expression values for each gene-Hi-C bin set.
###Merging contact frequency values with expression values. In this next section I join each of the data frames created above on the RPKM table by genes, in order to have a merged table with expression values and contact frequencies for each gene-Hi-C bin set. I move then to explore correlations between the values.
#Join all of the previously-made Hi-C interaction frequency tables to the RPKM table by genes, to obtain RPKM values in concert with contact frequency values.
RPKM <- as.data.frame(weighted.data$E)
RPKM$genes <- rownames(RPKM)
hmin <- left_join(h_minFDR, RPKM, by="genes")
cmin <- left_join(c_minFDR, RPKM, by="genes")
hmaxB <- left_join(h_maxB, RPKM, by="genes")
cmaxB <- left_join(c_maxB, RPKM, by="genes")
hUS <- left_join(h_US, RPKM, by="genes")
cUS <- left_join(c_US, RPKM, by="genes")
#Get the same thing filtered for only DE genes.
hminDE <- filter(hmin, adj.P.Val<=0.05)
cminDE <- filter(cmin, adj.P.Val<=0.05)
hmaxBDE <- filter(hmaxB, adj.P.Val<=0.05)
cmaxBDE <- filter(cmaxB, adj.P.Val <=0.05)
hUSDE <- filter(hUS, adj.P.Val<=0.05)
cUSDE <- filter(cUS, adj.P.Val<=0.05)
hmin_noDE <- filter(hmin, adj.P.Val>0.05) #Pull out specific set of non-DE hits.
#Extract contacts and expression for the contact with the lowest FDR from linear modeling.
mycontacts <- hmin[,61:68]
myexprs <- hmin[,69:76]
DEcontacts <- hminDE[,61:68]
DEexprs <- hminDE[,69:76]
nonDEcontacts <- hmin_noDE[,61:68]
nonDEexprs <- hmin_noDE[,69:76]
#Extract contacts and expression stratified by mean interspecies expression quantiles.
quant1contactexpr <- filter(hmin, AveExpr<=quantile(hmin$AveExpr)[2]) %>% select(., A_21792_HIC, B_28126_HIC, C_3649_HIC, D_40300_HIC, E_28815_HIC, F_28834_HIC, G_3624_HIC, H_3651_HIC, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")
quant2contactexpr <- filter(hmin, AveExpr>quantile(hmin$AveExpr)[2]&AveExpr<=quantile(hmin$AveExpr)[3]) %>% select(., A_21792_HIC, B_28126_HIC, C_3649_HIC, D_40300_HIC, E_28815_HIC, F_28834_HIC, G_3624_HIC, H_3651_HIC, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")
quant3contactexpr <- filter(hmin, AveExpr>quantile(hmin$AveExpr)[3]&AveExpr<=quantile(hmin$AveExpr)[4]) %>% select(., A_21792_HIC, B_28126_HIC, C_3649_HIC, D_40300_HIC, E_28815_HIC, F_28834_HIC, G_3624_HIC, H_3651_HIC, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")
quant4contactexpr <- filter(hmin, AveExpr>quantile(hmin$AveExpr)[4]&AveExpr<=quantile(hmin$AveExpr)[5]) %>% select(., A_21792_HIC, B_28126_HIC, C_3649_HIC, D_40300_HIC, E_28815_HIC, F_28834_HIC, G_3624_HIC, H_3651_HIC, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")
#Function for calculating gene-wise correlations.
cor.calc <- function(hicdata, exprdata){
cor.exp <- vector(length=nrow(hicdata))
for(i in 1:nrow(hicdata)){
cor.exp[i] <- cor(as.numeric(hicdata[i,]), as.numeric(exprdata[i,]))
}
return(cor.exp)
}
#Calculate correlations for different sets! I've commented out the species-specific calculations here because they just aren't that interesting. If you look at DE dynamics within each species you do NOT get a gorgeous bimodal as you do for across--looks more uniform and messy.
fullcors <- data.frame(cor=cor.calc(mycontacts, myexprs), type="all")
fullDEcors <- data.frame(cor=cor.calc(DEcontacts, DEexprs), type="DE")
fullnoDEcors <- data.frame(cor=cor.calc(nonDEcontacts, nonDEexprs), type="non-DE")
quant1cor <- data.frame(cor=cor.calc(quant1contactexpr[,1:8], quant1contactexpr[,9:16]), type="quant1")
quant2cor <- data.frame(cor=cor.calc(quant2contactexpr[,1:8], quant2contactexpr[,9:16]), type="quant2")
quant3cor <- data.frame(cor=cor.calc(quant3contactexpr[,1:8], quant3contactexpr[,9:16]), type="quant3")
quant4cor <- data.frame(cor=cor.calc(quant4contactexpr[,1:8], quant4contactexpr[,9:16]), type="quant4")
# humcors <- data.frame(cor=cor.calc(mycontacts[,c(1:2, 5:6)], myexprs[,c(1:2, 5:6)]), type="h_ALL")
# humDEcors <- data.frame(cor=cor.calc(DEcontacts[,c(1:2, 5:6)], DEexprs[,c(1:2, 5:6)]), type="h_DE")
# humnoDEcors <- data.frame(cor=cor.calc(nonDEcontacts[,c(1:2, 5:6)], nonDEexprs[,c(1:2, 5:6)]), type="h_non-DE")
# chimpcors <- data.frame(cor=cor.calc(mycontacts[,c(3:4, 7:8)], myexprs[,c(3:4, 7:8)]), type="c_ALL")
# chimpDEcors <- data.frame(cor=cor.calc(DEcontacts[,c(3:4, 7:8)], DEexprs[,c(3:4, 7:8)]), type="c_DE")
# chimpnoDEcors <- data.frame(cor=cor.calc(nonDEcontacts[,c(3:4, 7:8)], nonDEexprs[,c(3:4, 7:8)]), type="c_non-DE")
#Combine these dfs to plot in one gorgeous ggplot!
ggcors <- rbind(fullcors, fullDEcors, fullnoDEcors, quant1cor, quant2cor, quant3cor, quant4cor)#, humcors, humDEcors, humnoDEcors, chimpcors, chimpDEcors, chimpnoDEcors)
#First without the expression quantiles, then with.
ggplot(data=filter(ggcors, type=="all"|type=="DE"|type=="non-DE")) + stat_density(aes(x=cor, group=type, color=type, y=..scaled..), position="identity", geom="line") + ggtitle("Correlation b/t RPKM Expression and Hi-C Contact Frequency") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + scale_color_manual("Gene Set", values=c("red", "blue", "green"), labels=c("All genes", "DE genes", "non-DE genes"))
ggplot(data=ggcors) + stat_density(aes(x=cor, group=type, color=type, y=..scaled..), position="identity", geom="line") + ggtitle("Correlation b/t RPKM Expression and Hi-C Contact Frequency") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + scale_color_manual("Gene Set", values=c("red", "blue", "green", "purple", "orange", "yellow", "brown"), labels=c("All genes", "DE genes", "non-DE genes", "expression.quant1", "expression.quant2", "expression.quant3", "expression.quant4"))
ggplot(data=filter(ggcors, type=="DE"|type=="non-DE")) + stat_density(aes(x=cor, group=type, color=type, y=..scaled..), position="identity", geom="line") + ggtitle("Correlations between Expression and Hi-C Contact Frequency") + xlab("Pearson Correlations, Expression and Hi-C Contact Frequency") + ylab("Density") + scale_color_manual("Gene Set", values=c("red", "blue"), labels=c("DE genes", "non-DE genes")) + theme(plot.title=element_text(hjust=0.2))
###This looks great, showing strong bimodal distribution for the DE genes and broader distributions with a peak at 0 for the non-DE set and the full set. Also see no difference in terms of the distribution of correlations on individual sets of genes stratified by quantiles of expression (mean b/t species). Note that I've done that on just DE genes as well and see slightly shorter peaks on the bimodality for the middle quantiles of expression, which makes sense (extreme unlikely to be as severe b/t species). Also, to assess if this is legit at all, I now re-run this correlation analysis after permuting the Hi-C values. I shuffle sample IDs on a gene-by-gene basis to accomplish this:
cor.permuter <- function(hicdata, exprdata, nperm){
result <- data.frame(cor=NA, type=rep(1:nperm, each=nrow(exprdata)))
for(perm in 1:nperm){
permute <- hicdata
for(row in 1:nrow(hicdata)){
permute[row,] <- sample(hicdata[row,])
}
myindices <- which(result$type==perm)
result[myindices,1] <- cor.calc(permute, exprdata)
}
return(result)
}
#Just do it with 10 permutations to see the general effect quickly:
full.perm <- cor.permuter(mycontacts, myexprs, 10)
DE.perm <- cor.permuter(DEcontacts, DEexprs, 10)
nonDE.perm <- cor.permuter(nonDEcontacts, nonDEexprs, 10)
#Now visualize.
#FIGS14
ggplot(data=filter(ggcors, type=="all"|type=="DE"|type=="non-DE")) + stat_density(aes(x=cor, group=type, color=type, y=..scaled..), position="identity", geom="line") + stat_density(data=full.perm, aes(x=cor, group=type), geom="line", linetype="dotted", position="identity") + stat_density(data=DE.perm, aes(x=cor, group=type), geom="line", linetype="dashed", position="identity") + stat_density(data=nonDE.perm, aes(x=cor, group=type), geom="line", linetype="twodash", position="identity") + ggtitle("Correlation b/t RPKM Expression and Hi-C Contact Frequency") + xlab("Pearson Correlations, RPKM Expression & Hi-C Contact Frequency") + ylab("Density") + scale_color_manual("Gene Set", values=c("red", "blue", "green"), labels=c("All genes", "DE genes", "non-DE genes")) + theme(plot.title=element_text(hjust=0.3))#FIGS14
#See that permuted datasets have a tighter correlation distribution with a strong peak at 0. Reassuring. In the future I will repeat this analysis on Midway2, running 10000 permutations to see the full range of permuted data possible.
####Deprecated, for use in species-specific analysis.
# plotfull <- data.frame(fullcors=fullcors, Hcors=humcors, Ccors=chimpcors)
# plotDE <- data.frame(fullcors=fullDEcors[,1], Hcors=humDEcors[,1], Ccors=chimpDEcors[,1])
#
# ggplot(data=plotfull) + stat_density(aes(x=fullcors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, all genes")
# ggplot(data=plotfull) + stat_density(aes(x=Hcors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, all genes, Humans")
# ggplot(data=plotfull) + stat_density(aes(x=Ccors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, all genes, Chimps")
#
# ggplot(data=plotDE) + stat_density(aes(x=fullcors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, DE genes")
# ggplot(data=plotDE) + stat_density(aes(x=Hcors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, DE genes, Humans")
# ggplot(data=plotDE) + stat_density(aes(x=Ccors), geom="line") + xlab("Pearson Correlations b/t RPKM Expression and Hi-C Contact Frequency") + ylab("Density") + ggtitle("Correlations between Hi-C and Expression, DE genes, Chimps")Now that I’ve seen some nice effects in the correlation between expression and contact for different sets of genes, and for permutations on those sets, I move to see what kind of quantitiative explanatory power differential contact (DC) might actually have for differential expression (DE).
###How well does Hi-C data explain expression data? In this next section I “regress out” the effect of Hi-C contacts from their overlapping genes’ RPKM expression values, comparing a linear model run on the base values to one run on the residuals of expression after regressing out Hi-C data. Comparing the p-values before and after this regression can give some sense of whether the DE is being driven by differential Hi-C contacts (DC).
###A function to calculate gene-wise correlations between Hi-C data and expression data, but for spearman correlations. Pearson correlation calculator function is in the chunk above.
cor.calc.spear <- function(hicdata, exprdata){
cor.exp <- vector(length=nrow(hicdata))
for(i in 1:nrow(hicdata)){
cor.exp[i] <- cor(as.numeric(hicdata[i,]), as.numeric(exprdata[i,]), method="spearman")
}
return(cor.exp)
}
###A function to permute a Hi-C df by going gene-by-gene (row-by-row) and shuffling all sample IDs.
shuffler <- function(hicdata){
for(row in 1:nrow(hicdata)){
hicdata[row,] <- sample(hicdata[row,])
}
return(hicdata)
}
###A function that runs a linear model, both with and without Hi-C corrected expression values, and returns a dataframe of hit classes (DE or not before and after correction). Also spits back out p-values before and after correction, as well as correlations between Hi-C data and expression data. Since the former is a one-row data frame of 4 points and the latter is a 3-column data frame with the number of genes rows, returns a list.
lmcorrect <- function(voom.obj, exprs, cov_matrix, meta_df){
mygenes <- cov_matrix$genes #Pull out the relevant genes here; used for subsetting the voom.obj in a bit.
cov_matrix <- cov_matrix[, -9] #Remove genes from the cov matrix
hic_present <- sapply(1:nrow(cov_matrix), function(i) !any(is.na(cov_matrix[i,]))) #First, remove any rows w/ missing Hi-C data.
exprs <- data.matrix(exprs[hic_present,]) #Filter expression with this
cov_matrix <- data.matrix(cov_matrix[hic_present,]) #Filter Hi-C data with this
#Now, prepare to run the actual models. First run a model w/ Hi-C as a covariate to evaluate
SP <- factor(meta_df$SP,levels = c("H","C"))
design <- model.matrix(~0+SP)
colnames(design) <- c("Human", "Chimp")
resid_hic <- array(0, dim=c(nrow(exprs), ncol(cov_matrix))) #Initialize a dataframe for storing the residuals.
for(i in 1:nrow(exprs)){#Loop through rows of the expression df, running linear modeling w/ Hi-C to obtain residuals.
resid_hic[i,] <- lm(exprs[i,]~cov_matrix[i,])$resid
}
mycon <- makeContrasts(HvC = Human-Chimp, levels = design)
#Filter the voom object to only contain genes that had Hi-C information here.
good.indices <- which(rownames(voom.obj$E) %in% mygenes)
voom.obj <- voom.obj[good.indices,]
#Now, replace the RPKM values in the voom object for after linear modeling with the residuals.
voom.obj.after <- voom.obj
voom.obj.after$E <- resid_hic
lmFit(voom.obj, design=design) %>% eBayes(.) %>% contrasts.fit(., mycon) %>% eBayes(.) %>% topTable(., coef = 1, adjust.method = "BH", number = Inf, sort.by="none") -> fit_before
lmFit(voom.obj.after, design=design) %>% eBayes(.) %>% contrasts.fit(., mycon) %>% eBayes(.) %>% topTable(., coef = 1, adjust.method = "BH", number = Inf, sort.by="none") -> fit_after
result.DE.cats <- data.frame(DEneither=sum(fit_before$adj.P.Val>0.05&fit_after$adj.P.Val>0.05), DEbefore=sum(fit_before$adj.P.Val<=0.05&fit_after$adj.P.Val>0.05), DEafter=sum(fit_before$adj.P.Val>0.05&fit_after$adj.P.Val<=0.05), DEboth=sum(fit_before$adj.P.Val<=0.05&fit_after$adj.P.Val<=0.05))
result.DE.stats <- data.frame(cor.pear=cor.calc(cov_matrix, exprs), cor.spear=cor.calc.spear(cov_matrix, exprs), pval.before=fit_before$adj.P.Val, pval.after=fit_after$adj.P.Val)
result <- list("categories"=result.DE.cats, "stats"=result.DE.stats)
return(result)
}
#Proceed to use the function on the different dataframes I've created with different sets of overlapping Hi-C contacts:
h_minFDR_pvals <- lmcorrect(weighted.data, hmin[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], hmin[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
c_minFDR_pvals <- lmcorrect(weighted.data, cmin[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], cmin[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
h_maxB_pvals <- lmcorrect(weighted.data, hmaxB[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], hmaxB[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
c_maxB_pvals <- lmcorrect(weighted.data, cmaxB[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], cmaxB[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
h_US_pvals <- lmcorrect(weighted.data, hUS[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], hUS[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
c_US_pvals <- lmcorrect(weighted.data, cUS[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], cUS[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], meta.data)
#I now proceed to visualize the difference in p-values for expression before and after "correcting" for the Hi-C data. I am hoping to see many hits in the bottom right quadrant of the following plots, indicating genes that showed up as DE before Hi-C correction, but not after. I also expect that p-values falling farther away from the null line of expectation for p-values being identical between models will have higher correlations, which I color here for the pearson correlation (results look similar for spearman).
ggplot(data=h_minFDR_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.pear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C contact frequency") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-1.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-1.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-1.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=h_minFDR_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE Before vs. After Regressing out Hi-C Contact Frequency") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression)") + theme(plot.title=element_text(hjust=1)) #Slightly cleaner, for MindBytes 2018 poster<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-2.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-2.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-2.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=h_maxB_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.pear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C max beta (H)") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-3.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-3.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-3.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=h_US_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.spear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C US bin FDR (H)") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-4.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-4.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-4.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=c_minFDR_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.pear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C min. FDR (C)") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-5.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-5.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-5.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=c_maxB_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.pear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C max beta (C)") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-6.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-6.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-6.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
ggplot(data=c_US_pvals$stats, aes(x=-log10(pval.before), y=-log10(pval.after), color=abs(cor.pear))) + geom_point(size=0.01) + geom_hline(yintercept=-log10(0.05), color="red") + geom_vline(xintercept=-log10(0.05), color="red") + geom_abline(slope=1, intercept=0, color="green", linetype="dashed") + ggtitle("Evidence for DE before vs. after regressing out Hi-C US bin FDR (C)") + xlab("-log10(p-value of DE before Hi-C regression)") + ylab("-log10(p-value of DE after Hi-C regression")<img src=“figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-7.png" width=“672” style=“display: block; margin: auto;” />
| Version | Author | Date |
|---|---|---|
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/6f6db11526b876a7407f78b31561a771505dea5e/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-7.png" target="_blank">6f6db11 | Ittai Eres | 2019-04-23 |
| <a href=“https://github.com/IttaiEres/HiCiPSC/blob/a02a6022720868acae9f2e9839a496bbd73295d9/docs/figure/gene_expression.Rmd/Mediation effect by”regressing out" Hi-C effects from Expression Data-7.png" target="_blank">a02a602 | Ittai Eres | 2019-03-14 |
Critically, here I see that “regressing out” Hi-C data and trying to model expression again moves many genes from being significantly differentially expressed (at FDR of 5%) to no longer showing differential expression. Seeing most of the hits in the bottom right corner of these visualizations is what confirms this. Reassuringly, I also see stronger correlations between RPKM expression values and normalized Hi-C interaction frequency values for points farther away from the diagonal green line of expectation. However, since this is not a statistical test, I have no assessment of significance. To accomplish this, I compare these results to running the same kind of analysis on permuted data in the next section.
###Permutation Visualizations Here, I visualize the difference in classes of DE hits changing (either gaining, losing, or maintaining DE status) after “regressing out” the effect of Hi-C from expression. I show distributions of these percentages across 10000 permutations of the data, as compared to the observed percentages.
##Visualization of the permutations!
perm.vis <- function(categories.df.file, df, metadata, hictype, DE=FALSE){
if(DE==TRUE){expected1 <- readRDS(paste("data/old_mediation_permutations/perm_results/DE/batch1/", categories.df.file, sep=""))
expected2 <- readRDS(paste("data/old_mediation_permutations/perm_results/DE/batch2/", categories.df.file, sep=""))
observed <- lmcorrect(weighted.data, df[which(df$adj.P.Val<=0.05), c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], df[which(df$adj.P.Val<=0.05), c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], metadata)
}
else{expected1 <- readRDS(paste("data/old_mediation_permutations/perm_results/batch1/", categories.df.file, sep=""))
expected2 <- readRDS(paste("data/old_mediation_permutations/perm_results/batch2/", categories.df.file, sep=""))
observed <- lmcorrect(weighted.data, df[,c("A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651")], df[,c("A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC", "genes")], metadata)
}
expected <- c(expected1[1:5000], expected2[1:5000])
expected[[10001]] <- rbind(expected1[[5001]], expected2[[5001]])
expected[[10001]] <- ((expected[[10001]])/sum(expected[[10001]][1,]))*100
for(i in 1:(length(expected)-1)){
expected[[i]]$type <- i
expected[[i]]$data <- "expected"
}
observed$categories <- (observed$categories/sum(observed$categories[1,]))*100
observed$stats$type <- 10001
observed$stats$data <- "observed"
expected[[10001]]$data <- "permutations"
expected[[10001]][10001,] <- c(observed$categories, "observation")
deneitherbox <- ggplot(data=expected[[10001]], aes(x="", y=DEneither)) + geom_boxplot(aes(color="Expected"), show.legend=FALSE) + geom_point(aes(y=observed$categories$DEneither, color="Observed"), size=3) + ggtitle("Percent of genes with no evidence for DE regardless") + ylab("% genes not DE in either") + xlab(paste("10000 Permutations of Hi-C data ", hictype, sep="")) + scale_color_manual(values=c("blue", "red"), guide=FALSE) +guides(color=guide_legend("Data", override.aes = list(shape=c(16, 16))))
debeforebox <- ggplot(data=expected[[10001]], aes(x="", y=DEbefore)) + geom_boxplot(aes(color="Expected"), show.legend=FALSE) + geom_point(aes(y=observed$categories$DEbefore, color="Observed"), size=3) + ggtitle("Percent of genes with reduced evidence for DE after Hi-C correction") + ylab("% genes DE before, but not after, Hi-C correction") + xlab(paste("10000 Permutations of Hi-C data ", hictype, sep="")) + scale_color_manual(values=c("blue", "red"), guide=FALSE) +guides(color=guide_legend("Data", override.aes = list(shape=c(16, 16))))
deafterbox <- ggplot(data=expected[[10001]], aes(x="", y=DEafter)) + geom_boxplot(aes(color="Expected"), show.legend=FALSE) + geom_point(aes(y=observed$categories$DEafter, color="Observed"), size=3) + ggtitle("Percent of genes with increased evidence for DE after Hi-C correction") + ylab("% genes DE after, but not before, Hi-C correction") + xlab(paste("10000 Permutations of Hi-C data ", hictype, sep="")) + scale_color_manual(values=c("blue", "red"), guide=FALSE) +guides(color=guide_legend("Data", override.aes = list(shape=c(16, 16))))
debothbox <- ggplot(data=expected[[10001]], aes(x="", y=DEboth)) + geom_boxplot(aes(color="Expected"), show.legend=FALSE) + geom_point(aes(y=observed$categories$DEboth, color="Observed"), size=3) + ggtitle("Percent of genes with evidence for DE regardless") + ylab("% genes DE in both") + xlab(paste("10000 Permutations of Hi-C data ", hictype, sep="")) + scale_color_manual(values=c("blue", "red"), guide=FALSE) +guides(color=guide_legend("Data", override.aes = list(shape=c(16, 16))))
print(deneitherbox)
print(debeforebox)
print(deafterbox)
print(debothbox)
}
perm.vis("permout_h_minFDR", hmin, meta.data, "(min FDR contact, Humans)")
perm.vis("permout_h_maxB", hmaxB, meta.data, "(max beta contact, Humans)")
perm.vis("permout_h_US", hUS, meta.data, "(upstream contact, Humans)")
perm.vis("permout_c_minFDR", cmin, meta.data, "(min FDR contact, Chimps)")
perm.vis("permout_c_maxB", cmaxB, meta.data, "(max beta contact, Chimps)")
perm.vis("permout_c_US", cUS, meta.data, "(upstream contact, Chimps)")
perm.vis("permout_h_minFDR_DE", hminDE, meta.data, "(min FDR contact, Humans)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
perm.vis("permout_h_maxB_DE", hmaxBDE, meta.data, "(max beta contact, Humans)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
perm.vis("permout_h_US_DE", hUSDE, meta.data, "(upstream contact, Humans)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
perm.vis("permout_c_minFDR_DE", cminDE, meta.data, "(min FDR contact, Chimps)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
perm.vis("permout_c_maxB_DE", cmaxBDE, meta.data, "(max beta contact, Chimps)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
perm.vis("permout_c_US_DE", cUSDE, meta.data, "(upstream contact, Chimps)", DE=TRUE)
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
| Version | Author | Date |
|---|---|---|
| a02a602 | Ittai Eres | 2019-03-14 |
The permutations reveal that the observed data does appear pretty significant: it falls well outside the range of the distributions in each of the categories. The most important is showing that many more genes lose their DE status after regressing out Hi-C data in the observed case as compared to the permutations.
#Differences in effect size/sign changes This next section would be to look at differences in effect size and sign changes when doing the two separate lms (one before and one after regressing out Hi-C data from RPKM expression). Unfortunately the ash results I get have all their svalues as 1 so this is boring/not interesting right now. Hence it is not included. But let’s give it another go!
design <- model.matrix(~meta.data$SP+meta.data$SX)
exprs <- select(hmin, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651", genes) #Should be done on just DE genes, filter with adj.P.Val<=0.05
hic <- select(hmin,"A_21792_HIC", "B_28126_HIC", "C_3649_HIC", "D_40300_HIC", "E_28815_HIC", "F_28834_HIC", "G_3624_HIC", "H_3651_HIC")
present <- sapply(1:nrow(hic), function(i) !any(is.na(hic[i,])))
#Also test with log2 CPM instead of RPKM:
exprs.2 <- as.data.frame(lcpms)
exprs.2$genes <- rownames(exprs.2)
exprs.2 <- exprs.2[,c(8, 6, 1, 4, 7, 5, 2, 3, 9)]
exprs.2 <- exprs.2[(which(exprs.2$genes %in% exprs$genes)),]
genes <- exprs$genes
genes2 <- exprs.2$genes
DEgenes <- filter(hmin, adj.P.Val<=0.05) %>% select(., genes)
myDEindices <- which(genes %in% DEgenes[,1])
myDEindices.2 <- which(genes2 %in% DEgenes[,1])
exprs <- exprs[,-9]
exprs.2 <- exprs.2[,-9]
exprs <- data.matrix(exprs[present,])
exprs.2 <- data.matrix(exprs.2[present,])
hic <- data.matrix(hic[present,])
#designy <- rbind(as.vector(as.factor(c("human", "human", "chimp", "chimp", "human", "human", "chimp", "chimp"))), as.vector(as.factor(meta.data$SX)))
designy <- as.factor(c("human", "human", "chimp", "chimp", "human", "human", "chimp", "chimp"))
colnames(hic) <- colnames(exprs) <- colnames(exprs.2)
hic <- as.data.table(hic)
exprs <- as.data.table(exprs)
exprs.2 <- as.data.table(exprs.2)
#For further specifics on the function used for the mediation below, please see: https://github.com/jhsiao999/mediation/blob/master/pkg/R/mediate.test.regressing.R
source("code/mediate.test.regressing.R")
test <- mediate.test.regressing(exprs, designy, hic, cov=as.factor(meta.data$SX))
Attaching package: 'assertthat'The following object is masked from 'package:tibble':
has_name#test <- mediate.test.regressing(exprs.2, designy, hic) Gives extremely similar results, 222/1401 genes significantly mediated (16%), and still 0 of non-DE genes.
#Shrink dat variance!
vshrDE <- vash(test$d_se[myDEindices], df=4)
vshrnonDE <- vash(test$d_se[-myDEindices], df=4)
vshrink <- vash(test$d_se, df=4)
#Separate out DE from non-DE genes:
DEtest <- test[myDEindices,]
nonDEtest <- test[-myDEindices,]
#Ash on DE genes
ash_reg <- ash(as.vector(DEtest$d), as.vector(vshrDE$sd.post), mixcompdist = "normal")
summary(ash_reg$result$svalue<=0.05) Mode FALSE TRUE
logical 1117 284 ind_sig <- (ash_reg$result$svalue < 0.05)
ind_sig[ind_sig == TRUE] <- "red"
ind_sig[ind_sig == FALSE] <- "black"
#FIG15A
plot(x=ash_reg$result$betahat, y=ash_reg$result$sebetahat, ylab="Standard error of the difference", xlab="Difference in Effect Size from 2 Linear Models", main="Difference of Effect Size in DE Genes", pch=16, cex=0.6, col=ind_sig, xlim=c(-4.5, 4.5), ylim=c(0, 3))
legend("topleft", legend=c("s < 0.05", "s >= 0.05"), col=c("red", "black"), pch=16:16, cex=0.8)
plot(ash_reg$result$betahat, -log10(ash_reg$result$svalue))
#Now on non-DE genes
ash_reg_nonDE <- ash(as.vector(nonDEtest$d), as.vector(vshrnonDE$sd.post), mixcompdist = "normal") #Now it works with this design coding. Use 6 df, as 2-group comparison and 8 individual samples (samples - 2)
summary(ash_reg_nonDE$result$svalue<=0.05) Mode FALSE
logical 6363 ind_sig <- (ash_reg_nonDE$result$svalue < 0.05)
ind_sig[ind_sig == TRUE] <- "red"
ind_sig[ind_sig == FALSE] <- "black"
#FIG15B
plot(x=ash_reg_nonDE$result$betahat, y=ash_reg_nonDE$result$sebetahat, ylab="Standard error of the difference", xlab="Difference in Effect Size from 2 Linear Models", main="Difference of Effect Size in non-DE Genes", pch=16, cex=0.6, col=ind_sig, xlim=c(-4.5, 4.5), ylim=c(0, 3))
legend("topleft", legend=c("s < 0.05", "s >= 0.05"), col=c("red", "black"), pch=16:16, cex=0.8)
plot(ash_reg_nonDE$result$betahat, -log10(ash_reg_nonDE$result$svalue))
#Now on the full set, for comparison's sake.
ash_reg_full <- ash(as.vector(test$d), as.vector(vshrink$sd.post), mixcompdist = "normal") #Now it works with this design coding. Use 6 df, as 2-group comparison and 8 individual samples (samples - 2)
summary(ash_reg_full$result$svalue<=0.05) #Incredible result, looks too good to be true. Mode FALSE TRUE
logical 7720 44 ind_sig <- (ash_reg_full$result$svalue < 0.05)
ind_sig[ind_sig == TRUE] <- "red"
ind_sig[ind_sig == FALSE] <- "black"
plot(x=ash_reg_full$result$betahat, y=ash_reg_full$result$sebetahat, ylab="Standard error of the difference", xlab="Difference in effect size from 2 lms", main="Difference of effect size (DE genes)", pch=16, cex=0.6, col=ind_sig)#, xlim=c(-2.5, 2.5), ylim=c(0, 1.3))
legend("topleft", legend=c("s < 0.05", "s >= 0.05"), col=c("red", "black"), pch=16:16, cex=0.8)
plot(ash_reg_full$result$betahat, -log10(ash_reg_full$result$svalue))
#Look at effect size shrinkage in full set ash vs. separated DE/nonDE ash:
plot(c(ash_reg$result$PosteriorMean, ash_reg_nonDE$result$PosteriorMean), ash_reg_full$result$PosteriorMean)
#Look at this just in DE:
plot(ash_reg$result$PosteriorMean, ash_reg_full$result$PosteriorMean[myDEindices])
#Look at this just in non-DE:
plot(ash_reg_nonDE$result$PosteriorMean, ash_reg_full$result$PosteriorMean[-myDEindices])
#{r, include=FALSE, echo=FALSE, Work for getting example regions. Useful for pyGenomeTracks} # #Grab genes that were significantly mediated: # therows <- which(ash_reg$result$svalue<=0.05) # besthits <- myDEindices[therows] # mygenes <- genes[besthits] # # ###### START EXAMPLE # #Figure out where some of these are to pull out good examples, try to find a dense region of them # myhits <- which(ash_reg$result$svalue<=0.05) #These SHOULD line up with DEtest indices... # realhits <- myDEindices[myhits] #Now go back to exprs original with these indices to get the gene names! # exprs <- select(hmin, "A-21792", "B-28126", "C-3649", "D-40300", "E-28815", "F-28834", "G-3624", "H-3651", genes) #Should be done on just DE genes, filter with adj.P.Val<=0.05 # demgenes <- exprs[realhits,9] # testcases <- filter(gene.hic.filt, genes %in% demgenes) # table(testcases$genechr.H) # testcases$sval <- ash_reg$result$svalue[myhits] # testcases <- testcases[order(testcases$sval),] # # #Now, pull out expression values for those suckers: # options(scipen = 999) # mytest <- filter(humgenes, genes %in% testcases$genes) # mytest <- mytest[(mytest$genes %in% weighted.data$genes$genes),] # mean.species.expression <- as.data.frame(weighted.data$E) #Now, need to pull out the mean expression values for all those genes! # mean.species.expression$genes <- rownames(mean.species.expression) # mean.species.expression$Hmean <- rowMeans(mean.species.expression[,c(1:2,5:6)]) # mean.species.expression$Cmean <- rowMeans(mean.species.expression[,c(3:4,7:8)]) # mean.species.expression <- mean.species.expression[,(-1:-8)] # # left_join(testcases, mean.species.expression, by="genes") -> filt.example # filter(filt.example, min_FDR.H<=0.05|min_FDR.C<=0.05) -> final.example # final.example$Hmean-final.example$Cmean # #gonna end up pulling 48th row example from final.example. Pull out its contact frequency vals for arc diagram creation: # final.example[48,] # which((data.filtered$H1=="chr17-64470000"|data.filtered$H2=="chr17-64470000")&(data.filtered$C1=="chr17-63970000"|data.filtered$C2=="chr17-63970000")) # which((data.filtered$H1=="chr17-64470000"&data.filtered$H2=="chr17-64430000")|(data.filtered$H2=="chr17-64470000"&data.filtered$H1=="chr17-64430000")) # # #Check on what other contacts might be significant in the region # QC <- filter(data.filtered, Hchr=="Chr. 17"&sp_BH_pval<=0.05) # QC$H1 <- gsub(".*-", "", QC$H1) # QC$H2 <- gsub(".*-", "", QC$H2) # which((QC$H1>=64400000&QC$H1<=64500000)&(QC$H2>=64400000&QC$H2<=64500000)) # QC[c(218,323,397),] # # # #filter(example, Hstart>=140500000&Hstart<=141500000) -> filt.example # hgenebed <- select(filt.example, genechr.H, genepos.H, Hmean) # cgenebed <- select(filt.example, genechr.C, genepos.C, Cmean) # hgenebed$end <- hgenebed$genepos.H+5000 # cgenebed$end <- cgenebed$genepos.C+5000 # hgenebed <- hgenebed[,c(1,2,4,3)] # cgenebed <- cgenebed[,c(1,2,4,3)] # cgenebed <- cgenebed[complete.cases(cgenebed),] # # fwrite(hgenebed, "~/Desktop/Hi-C/pyGenomeTracks/filt.h.genes.bed", quote=FALSE, sep="\t", col.names =FALSE) # fwrite(cgenebed, "~/Desktop/Hi-C/pyGenomeTracks/filt.c.genes.bed", quote=FALSE, sep="\t", col.names =FALSE) # # #Write out the appropriate columns from data.filtered to make an "arcs" file for visualization: # harcs <- filter(data.filtered, sp_beta>0, sp_BH_pval<=0.05) %>% select(., H1, H2, Hmean) #Note chr notation different due to other things changed; check on this later. # harcs$chr <- gsub("-.*", "", harcs$H1) # harcs$H1start <- as.numeric(gsub(".*-", "", harcs$H1)) # harcs$H1end <- harcs$H1start+10000 # harcs$H2start <- as.numeric(gsub(".*-", "", harcs$H2)) # harcs$H2end <- harcs$H2start+10000 # harcs <- harcs[,c(4:6, 4, 7:8, 3)] # harcs <- format(harcs, scientific=FALSE) # fwrite(harcs, "~/Desktop/Hi-C/pyGenomeTracks/RNAseq/humans.arcs", quote=FALSE, sep="\t", col.names=FALSE) # # #Prep the same thing for chimpanzees: # carcs <- filter(data.filtered, sp_beta<0, sp_BH_pval<=0.05) %>% select(., C1, C2, Cmean) #Note chr notation different due to other things changed; check on this later. # carcs$chr <- gsub("-.*", "", carcs$C1) # carcs$C1start <- as.numeric(gsub(".*-", "", carcs$C1)) # carcs$C1end <- carcs$C1start+10000 # carcs$C2start <- as.numeric(gsub(".*-", "", carcs$C2)) # carcs$C2end <- carcs$C2start+10000 # carcs <- carcs[,c(4:6, 4, 7:8, 3)] # carcs <- format(carcs, scientific=FALSE) # fwrite(carcs, "~/Desktop/Hi-C/pyGenomeTracks/RNAseq/chimps.arcs", quote=FALSE, sep="\t", col.names=FALSE) # # # #Now add in an extra column placeholder so strand is in the right column for bedtools, and rearrange columns as well for bedtools' ease. # humgenes$placeholder <- "." # chimpgenes$placeholder <- "." # humgenes$Hend <- humgenes$Hstart+1 # chimpgenes$Cend <- chimpgenes$Cstart+1 # humgenes <- humgenes[,c(2:3, 6, 1, 5, 4)] # chimpgenes <- chimpgenes[,c(2:3, 6, 1, 5, 4)] # # #Turn off scientific notation for writing the file out for bedtools to be able to use correctly. # options(scipen=999) # # ####### END EXAMPLE # #
{r, include=FALSE, echo=FALSE} # #Simple plotting of logFC vs logFC. A positive logFC from the expression data indicates higher expression in humans. # #First, perform for entire set of genes, on non-DE and DE genes separately. # mydat <- select(gene.hic.filt, logFC, min_FDR_B.H, adj.P.Val, AveExpr) # ggplot(mydat) + geom_point(aes(x=logFC, y=min_FDR_B.H, color=adj.P.Val)) # ggplot(filter(mydat, adj.P.Val<=0.05)) + geom_point(aes(x=logFC, y=min_FDR_B.H)) + xlab("LogFC, Expression") + ylab("LogFC, Contact") + ggtitle("LogFC Expression vs. LogFC Contact for DE Genes") # ggplot(filter(mydat, adj.P.Val>0.05)) + geom_point(aes(x=logFC, y=min_FDR_B.H)) + xlab("LogFC, Expression") + ylab("LogFC, Contact") + ggtitle("LogFC Expression vs. LogFC Contact for non-DE Genes") # # #Now, take a look at what happens if I stratify things by which species it's more highly expressed in, and its DE status, and calculate R-squareds. Should do this with actual mean expression values per species # human.high.DE <- filter(mydat, logFC>0, adj.P.Val<=0.05) # human.high.nonDE <- filter(mydat, logFC>0, adj.P.Val>0.05) # chimp.high.DE <- filter(mydat, logFC<0, adj.P.Val<=0.05) # chimp.high.nonDE <- filter(mydat, logFC<0, adj.P.Val>0.05) # summary(lm(human.high.nonDE$logFC~human.high.nonDE$min_FDR_B.H))$adj.r.squared # summary(lm(human.high.DE$logFC~human.high.DE$min_FDR_B.H))$adj.r.squared # summary(lm(chimp.high.nonDE$logFC~chimp.high.nonDE$min_FDR_B.H))$adj.r.squared # summary(lm(chimp.high.DE$logFC~chimp.high.DE$min_FDR_B.H))$adj.r.squared # # # #Now, try out the suggested regression. # new.weighted.data <- weighted.data # new.weighted.data$E <- dge_final$lcpm_counts[,c(8, 6, 1, 4, 7, 5, 2, 3)] # # #Now, need to subset this down to the genes we have contact frequency for (from hmin table, created far below) # my.hmin <- select(hmin, genes, A_21792_HIC, B_28126_HIC, C_3649_HIC, D_40300_HIC, E_28815_HIC, F_28834_HIC, G_3624_HIC, H_3651_HIC) # deez.genes <- left_join(new.weighted.data$genes, my.hmin) # good.indices <- which(complete.cases(deez.genes)) # new.weighted.data <- new.weighted.data[good.indices,] # new.weighted.data$HiC <- deez.genes[good.indices, 3:10] # # meta.exp.data <- data.frame("SP"=c("C", "C", "C", "C", "H", "H", "H", "H"), "SX"=c("M","M" ,"F","F","F", "M","M","F")) # SP <- factor(meta.exp.data$SP,levels = c("H","C")) # # # for(row in 1:nrow(new.weighted.data$E)){ # lmFit() # } # exp.design <- model.matrix(~0+SP+new.weighted.data$HiC+SP:new.weighted.data$HiC)#(~1+meta.exp.data$SP+meta.exp.data$SX) But where does freq come from? # colnames(exp.design) <- c("Human", "Chimp") # weighted.data <- voom(new.weighted.data, exp.design, plot=TRUE, normalize.method = "cyclicloess") # # ##Obtain rest of LM results, with particular eye to DE table! # vfit <- lmFit(weighted.data, exp.design) # efit <- eBayes(vfit) # # mycon <- makeContrasts(HvC = Human-Chimp, levels = exp.design) # diff_species <- contrasts.fit(efit, mycon) # finalfit <- eBayes(diff_species) # detable <- topTable(finalfit, coef = 1, adjust.method = "BH", number = Inf, sort.by="none") #
sessionInfo()R version 3.4.0 (2017-04-21)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: OS X El Capitan 10.11.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid compiler stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] assertthat_0.2.1 VennDiagram_1.6.20 futile.logger_1.4.3
[4] glmnet_2.0-16 foreach_1.4.4 Matrix_1.2-15
[7] medinome_0.0.1 vashr_0.99.1 qvalue_2.10.0
[10] SQUAREM_2017.10-1 ashr_2.2-32 forcats_0.4.0
[13] purrr_0.3.2 readr_1.3.1 tibble_2.1.1
[16] tidyverse_1.2.1 edgeR_3.20.9 RColorBrewer_1.1-2
[19] heatmaply_0.15.2 viridis_0.5.1 viridisLite_0.3.0
[22] stringr_1.4.0 gplots_3.0.1.1 Hmisc_4.2-0
[25] Formula_1.2-3 survival_2.44-1 lattice_0.20-38
[28] dplyr_0.8.0.1 plotly_4.8.0 cowplot_0.9.4
[31] ggplot2_3.1.0 reshape2_1.4.3 data.table_1.12.0
[34] tidyr_0.8.3 plyr_1.8.4 limma_3.34.9
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 class_7.3-15 modeltools_0.2-22
[4] mclust_5.4.3 rprojroot_1.3-2 htmlTable_1.13.1
[7] base64enc_0.1-3 fs_1.2.7 rstudioapi_0.10
[10] flexmix_2.3-15 mvtnorm_1.0-8 lubridate_1.7.4
[13] xml2_1.2.0 codetools_0.2-16 splines_3.4.0
[16] pscl_1.5.2 doParallel_1.0.14 robustbase_0.92-8
[19] knitr_1.22 jsonlite_1.6 workflowr_1.2.0
[22] broom_0.5.1 cluster_2.0.7-1 kernlab_0.9-27
[25] httr_1.4.0 backports_1.1.3 lazyeval_0.2.2
[28] cli_1.1.0 formatR_1.6 acepack_1.4.1
[31] htmltools_0.3.6 tools_3.4.0 gtable_0.3.0
[34] glue_1.3.1 Rcpp_1.0.1 cellranger_1.1.0
[37] trimcluster_0.1-2.1 gdata_2.18.0 nlme_3.1-137
[40] iterators_1.0.10 fpc_2.1-11.1 xfun_0.5
[43] rvest_0.3.2 gtools_3.8.1 dendextend_1.10.0
[46] DEoptimR_1.0-8 MASS_7.3-51.1 scales_1.0.0
[49] TSP_1.1-6 hms_0.4.2 parallel_3.4.0
[52] lambda.r_1.2.3 yaml_2.2.0 gridExtra_2.3
[55] rpart_4.1-13 latticeExtra_0.6-28 stringi_1.4.3
[58] gclus_1.3.2 checkmate_1.9.1 seriation_1.2-3
[61] caTools_1.17.1.2 truncnorm_1.0-8 rlang_0.3.3
[64] pkgconfig_2.0.2 prabclus_2.2-7 bitops_1.0-6
[67] evaluate_0.13 labeling_0.3 htmlwidgets_1.3
[70] tidyselect_0.2.5 magrittr_1.5 R6_2.4.0
[73] generics_0.0.2 pillar_1.3.1 haven_2.1.0
[76] whisker_0.3-2 foreign_0.8-71 withr_2.1.2
[79] mixsqp_0.1-97 nnet_7.3-12 modelr_0.1.4
[82] crayon_1.3.4 futile.options_1.0.1 KernSmooth_2.23-15
[85] rmarkdown_1.12 locfit_1.5-9.1 readxl_1.3.1
[88] git2r_0.25.2 digest_0.6.18 diptest_0.75-7
[91] webshot_0.5.1 stats4_3.4.0 munsell_0.5.0
[94] registry_0.5-1