Last updated: 2019-05-01

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File Version Author Date Message
Rmd f4f5b21 brimittleman 2019-05-01 add number of peak per gene analysis
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Rmd f587970 brimittleman 2019-05-01 fix join
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Rmd f9b8195 brimittleman 2019-04-30 understand usage of new pas

library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✖ dplyr::filter() masks stats::filter()
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library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started

These results have 30k more PAS than the previous runs. I also see a confusing shift in mean usage for all of the PAS. I want to compare the distribution of usage for different sets of individuals to see if there is something inherently different about the 15 new individuals.

New vs old peaks

I want to compare the usage of the new peaks compared to the overall mean usage. To do this I need to seperate the new and old PAS.

newPAS5perc=read.table("../data/PAS/APAPAS_GeneLocAnno.5perc.bed", stringsAsFactors = F, col.names = c("chr", "start","end", "ID", "score", "strand"))
oldPAS5perc=read.table("../../threeprimeseq/data/peaks4DT/APAPAS_5percCov_fixedStrand.bed", stringsAsFactors = F, col.names = c("chr", "start", "end", "ID", "score", "strand"))

uniqnew=newPAS5perc %>% anti_join(oldPAS5perc, by=c("chr", "start", "end"))

Pull in the usage of the peaks:

Total

totalPeakUs=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "loc", "strand", "peak"))
Warning: Expected 4 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
ind=colnames(totalPeakUs)[8:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric", col.names = ind)


#numeric with anno
totalPeak=as.data.frame(cbind(totalPeakUs[,1:7], totalPeakUs_CountNum))

totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)

#append mean to anno
TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:7],mean=totalPeakUs_CountNum_mean))
uniqnewPasnum=uniqnew  %>% separate(ID ,into=c("peaknum", "geneloc"),sep=":") %>% mutate(peak=paste("peak", peaknum, sep="")) %>% select(peak)

Filter these inthe mean usage:

TotalPeakUSMeanClass= TotalPeakUSMean %>% mutate(New=ifelse(peak %in% uniqnewPasnum$peak,"new", "original")) %>% mutate(Cutoff=ifelse(mean>=.05, "Yes", "No"))

mean(TotalPeakUSMean$mean)
[1] 0.2378282

Plot:

ggplot(TotalPeakUSMeanClass, aes(y=mean,x=New)) + geom_violin() + geom_hline(yintercept = mean(TotalPeakUSMean$mean), col="red") +  geom_hline(yintercept = .05, col="Blue")

Version Author Date
03ea0f7 brimittleman 2019-05-01
ac656aa brimittleman 2019-04-30
TotalPeakUSMeanClass_newonly= TotalPeakUSMeanClass %>% filter(New=="new")

ggplot(TotalPeakUSMeanClass_newonly, aes(y=mean, x="")) + geom_violin() + geom_hline(yintercept = mean(TotalPeakUSMean$mean), col="red") +  geom_hline(yintercept = .05, col="Blue") + labs(x="", y="Mean Usage", title="Mean Usage of New PAS")

ggplot(TotalPeakUSMeanClass_newonly, aes(x=mean)) + geom_histogram(bins=50) + geom_vline(xintercept = mean(TotalPeakUSMean$mean), col="red")

This shows me the new peaks are the peaks that barely passed the cuttoff before.

write out file with information about new and old peaks

Peak_newOld=TotalPeakUSMeanClass %>% select(-mean)
write.table(Peak_newOld, file="../data/peaks_5perc/NewVOldPeaks.txt", col.names = T, row.names = F, quote=F)

Distirubution in different sets of 15 ind

I want to see if the new 15 individuals are driving the change in the peak mean distribution. I want to make a function that take a vector of individuals, filters the usage dataframe and plots the histogram.

Total:

First I will upload the usage dataframe.

totCounts=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.fc", stringsAsFactors = F, header = T)
ind=colnames(totCounts)[2:55]
totUsage=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Total.CountsOnlyNumeric", stringsAsFactors = F, header = F,col.names = ind)



batch1.2.3=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>%  select(line, batch) %>% filter(batch != 4)
oldind=batch1.2.3$line

batch4=read.table("../data/MetaDataSequencing.txt", header=T,stringsAsFactors = F)%>% filter(fraction=="total") %>%  select(line, batch) %>% filter(batch == 4)

newInd=batch4$line
UsageHist= function(indVec,title,totUsage=totUsage){
  totUsage_ind=totUsage %>% select(indVec)
  meanVec=rowMeans(totUsage_ind)
  hist(meanVec, main=title,xlab="Mean Usage")
}

RUn this for different itterations of individuals:

Pick 15 random individuals from old:

sampl1=sample(oldind, 15)
sampl2=sample(oldind, 15)
sampl3=sample(oldind, 15)
sampl4=sample(oldind, 15)

png("../output/newtot.png")
par(mfrow=c(3,2))
UsageHist(indVec=newInd,title="Total Usage New (15ind)",totUsage=totUsage)
UsageHist(indVec=oldind,title="Total Usage Old (39 ind)",totUsage=totUsage)
UsageHist(indVec=sampl1,title="Total Usage Sample 15 Old",totUsage=totUsage)
UsageHist(indVec=sampl2,title="Total Usage Sample 15 Old",totUsage=totUsage)
UsageHist(indVec=sampl3,title="Total Usage Sample 15 Old",totUsage=totUsage)
UsageHist(indVec=sampl4,title="Total Usage Sample 15 Old",totUsage=totUsage)
dev.off()
png 
  2 

Nuclear

nucCounts=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.fc", stringsAsFactors = F, header = T)
ind=colnames(nucCounts)[2:55]
nucUsage=read.table("../data/phenotype/APApeak_Phenotype_GeneLocAnno.Nuclear.CountsOnlyNumeric", stringsAsFactors = F, header = F,col.names = ind)
UsageHist_nuc= function(indVec,title,nucUsage=nucUsage){
  nucUsage_ind=nucUsage %>% select(indVec)
  meanVec=rowMeans(nucUsage_ind)
  hist(meanVec, main=title,xlab="Mean Usage")
}
png("../output/newnuc.png")
par(mfrow=c(3,2))
UsageHist_nuc(indVec=newInd,title="Nuclear Usage New (15ind)",nucUsage=nucUsage)
UsageHist_nuc(indVec=oldind,title="Nuclear Usage Old (39ind)",nucUsage=nucUsage)
UsageHist_nuc(indVec=sampl1,title="Nuclear Usage Sample 15 Old",nucUsage=nucUsage)
UsageHist_nuc(indVec=sampl2,title="Nuclear Usage Sample 15 Old",nucUsage=nucUsage)
UsageHist_nuc(indVec=sampl3,title="Nuclear Usage Sample 15 Old",nucUsage=nucUsage)
UsageHist_nuc(indVec=sampl4,title="Nuclear Usage Sample 15 Old",nucUsage=nucUsage)
dev.off()
png 
  2 

Old

oldtotalCount=read.table("../../threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc",header=T,stringsAsFactors = F)
indOld=colnames(oldtotalCount)[2:56]
oldtotalUsage=read.table("../../threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnlyNumeric.txt",col.names = indOld,stringsAsFactors = F)
png("../output/oldtot.png")
par(mfrow=c(3,2))
UsageHist(indVec=newInd,title="Old total Usage (15ind)",totUsage=oldtotalUsage)
UsageHist(indVec=oldind,title="Old total Usage (39ind)",totUsage=oldtotalUsage)
UsageHist(indVec=sampl1,title="Old total Usage sample 15 ind",totUsage=oldtotalUsage)
UsageHist(indVec=sampl2,title="Old total Usage sample 15 ind",totUsage=oldtotalUsage)
UsageHist(indVec=sampl3,title="Old total Usage sample 15 ind",totUsage=oldtotalUsage)
UsageHist(indVec=sampl4,title="Old total Usage sample 15 ind",totUsage=oldtotalUsage)
dev.off()
png 
  2 
oldnuclearCount=read.table("../../threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc",header=T,stringsAsFactors = F)
indOldN=colnames(oldnuclearCount)[2:56]
oldnuclearUsage=read.table("../../threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnlyNumeric.txt",col.names = indOldN,stringsAsFactors = F)
png("../output/oldnuc.png")
par(mfrow=c(3,2))
UsageHist_nuc(indVec=newInd,title="Old nuclear Usage (15ind)",nucUsage=oldnuclearUsage)
UsageHist_nuc(indVec=oldind,title="Old nuclear Usage (39ind)",nucUsage=oldnuclearUsage)
UsageHist_nuc(indVec=sampl1,title="Old nuclear Usage sample 15 ind",nucUsage=oldnuclearUsage)
UsageHist_nuc(indVec=sampl2,title="Old nuclear Usage sample 15 ind",nucUsage=oldnuclearUsage)
UsageHist_nuc(indVec=sampl3,title="Old nuclear Usage sample 15 ind",nucUsage=oldnuclearUsage)
UsageHist_nuc(indVec=sampl4,title="Old nuclear Usage sample 15 ind",nucUsage=oldnuclearUsage)
dev.off()
png 
  2 

Usage of new peaks in old data

I have the old usage. I want to filter the new peaks from this:

oldtotusage_anno=as.data.frame(cbind(chrom=oldtotalCount$chrom,oldtotalUsage )) %>% separate(chrom, into=c("chr", "start", "end", "peakID"), sep=":")

TotalPeakUSMeanClass_newonly=TotalPeakUSMeanClass %>% filter(New=="new")

oldtotusage_anno_new=oldtotusage_anno %>% semi_join(TotalPeakUSMeanClass_newonly, by=c("chr", "start", "end")) %>% select(-chr, -start,-end,-peakID)


oldtotusage_anno_new_mean=rowMeans(oldtotusage_anno_new)

plot(oldtotusage_anno_new_mean, main="Total Usage of new peaks in old data", ylab="old usage means Percent")

length(oldtotusage_anno_new_mean)
[1] 14642

This shows me that we called 14 thousand of the new peaks in the old set but they were all super low coverage.

Where are the new peaks, genes with a lot of peaks? or genes with less peaks

newPAS5perc_pergene=newPAS5perc %>% separate(ID, into=c("peaknum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_") %>% group_by(gene) %>% summarise(nPeak=n())

ggplot(newPAS5perc_pergene,aes(x=nPeak)) + geom_histogram(bins=100)

nrow(newPAS5perc_pergene)
[1] 15456

Look at which genes the new peaks are in.

uniqnew_genes=uniqnew %>% separate(ID, into=c("peaknum", "geneID"), sep=":") %>% separate(geneID, into=c("gene", "loc"),sep="_") %>% group_by(gene) %>% summarise(npeakadded=n())


ggplot(uniqnew_genes, aes(x=npeakadded)) + geom_histogram(bins=100) + labs(title="Number of peaks added per gene\n(added peak in 10827 genes of 15456 genes)", x="Number of Peaks", y="Number of Genes")

Look at these genes compared to distribution for number of peaks in all peaks before filter

allPAS=read.table("../data/assignedPeaks/APApeaks.ALLChrom.Filtered.Named.GeneLocAnnoPARSED.SAF", header = T, stringsAsFactors = F) %>% separate(GeneID, into = c("peak", "Chrom", "Peakstart", "PeakEnd", "strand", "geneid"),sep=":") %>% separate(geneid, into=c("gene", "loc"), sep="_") %>% group_by(gene) %>% summarise(nPeakAll=n()) %>% mutate(AddedPeak=ifelse(gene %in% uniqnew_genes$gene, "yes", "no"))
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [14735,
14736, 14737, 14738].
ggplot(allPAS, aes(x=nPeakAll))+ geom_histogram(bins = 100) + facet_grid(~AddedPeak)

Mean number of peaks for genes where we added, this could help us understand the distribution shift:

allPAS_withnewpas=allPAS %>% filter(AddedPeak=="yes")

summary(allPAS_withnewpas$nPeakAll)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   3.000   6.000   9.725  12.000 201.000 

Look at n peaks per gene in old data and npeaks per gene in new- join and subtract

#newPAS5perc_pergene

oldPAS5perc_pergene= oldPAS5perc%>% separate(ID, into=c("gene", "peaknum"), sep=":") %>% group_by(gene) %>% summarise(nPeakOld=n())

nrow(oldPAS5perc_pergene)
[1] 15219

Join:

npeakpergenebot=oldPAS5perc_pergene %>% full_join(newPAS5perc_pergene, by="gene") %>% replace_na(list(nPeakOld = 0, nPeak = 0)) %>% mutate(NewMinOld=nPeak-nPeakOld)


ggplot(npeakpergenebot, aes(x=nPeakOld, y=nPeak)) + geom_point() + geom_smooth(method="lm") + annotate("text", label="r2=0.4447", x=12, y=2) + labs(title="Number of Peaks in old vs new data",y="Number of peaks new data", x="Number of peaks old data")

Correlation between number of peaks before and after:

summary(lm(npeakpergenebot$nPeak~npeakpergenebot$nPeakOld))

Call:
lm(formula = npeakpergenebot$nPeak ~ npeakpergenebot$nPeakOld)

Residuals:
     Min       1Q   Median       3Q      Max 
-10.1193  -1.6746  -1.3302   0.9475  17.3254 

Coefficients:
                         Estimate Std. Error t value Pr(>|t|)    
(Intercept)               0.98570    0.04322    22.8   <2e-16 ***
npeakpergenebot$nPeakOld  1.34447    0.01201   112.0   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 3.185 on 15650 degrees of freedom
Multiple R-squared:  0.4448,    Adjusted R-squared:  0.4447 
F-statistic: 1.254e+04 on 1 and 15650 DF,  p-value: < 2.2e-16
summary(npeakpergenebot$NewMinOld)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 -6.000   0.000   1.000   1.988   3.000  20.000 
ggplot(npeakpergenebot, aes(x=NewMinOld)) + geom_histogram(bins=100)


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] workflowr_1.3.0 forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   reshape2_1.4.3   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4