Last updated: 2020-04-03
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Knit directory: Comparative_APA/analysis/
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html | f161386 | brimittleman | 2020-01-18 | Build site. |
Rmd | 289ba9b | brimittleman | 2020-01-18 | add expression cutoff code |
html | 5eef3eb | brimittleman | 2020-01-16 | Build site. |
Rmd | 5c24c0c | brimittleman | 2020-01-16 | add cutoff code files |
In this analysis I will use feature counts to count all the 3’ seq reads in each gene. I will then use a similar pipeline to the RNA seq to establish a cutoff for normalized expression. This will be used as a data filter on the pas for the humans and chimps.
library(workflowr)
This is workflowr version 1.6.0
Run ?workflowr for help getting started
library("gplots")
Attaching package: 'gplots'
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library("R.utils")
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library(tidyverse)
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library("edgeR")
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I will sum over all PAS for a psuedo gene count. I will use the full set of PAS before cutting to 5%.
Human nuclear only
humanPAS=read.table("../Human/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Human_fixed.fc", header=T, stringsAsFactors = F) %>%
separate(Geneid, into=c("disc","PAS","chrom", "start","end","strand","geneid"), sep=":") %>%
separate(geneid,into=c("gene","loc"),sep="_") %>%
dplyr::select(gene,contains("_N")) %>%
gather(key="ind", value="count", -gene) %>%
group_by(ind, gene) %>%
summarize(GeneCount=sum(count)) %>%
spread(ind, GeneCount)
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [48528,
48529, 48530, 92432].
chimpPAS=read.table("../Chimp/data/CleanLiftedPeaks_FC/ALLPAS_postLift_LocParsed_Chimp_fixed.fc", header=T, stringsAsFactors = F) %>%
separate(Geneid, into=c("disc","PAS","chrom", "start","end","strand","geneid"), sep=":") %>%
separate(geneid,into=c("gene","loc"),sep="_") %>%
dplyr::select(gene,contains("_N")) %>%
gather(key="ind", value="count", -gene) %>%
group_by(ind, gene) %>%
summarize(GeneCount=sum(count)) %>%
spread(ind, GeneCount)
Warning: Expected 2 pieces. Additional pieces discarded in 4 rows [48528,
48529, 48530, 92432].
Join these together:
metadata=read.table("../data/metadata_HCpanel.txt",header = T) %>% mutate(id2=ifelse(grepl("pt", ID), ID, paste("X", ID, sep=""))) %>% filter(Fraction=="Nuclear")
order=c(metadata$id2[1:10], "pt30_N", "pt91_N")
BothbyGene= chimpPAS %>% inner_join(humanPAS,by="gene") %>% dplyr::select(gene,order)
#count matrix:
Genematrix=as.matrix(BothbyGene %>% column_to_rownames(var="gene"))
colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)
pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))
labels <- paste(metadata$Species,metadata$Line, sep=" ")
# Clustering (original code from Julien Roux)
cors <- cor(Genematrix, method="spearman", use="pairwise.complete.obs")
heatmap.2( cors, scale="none", col = colors, margins = c(12, 12), trace='none', denscol="white", labCol=labels, ColSideColors=pal[as.integer(as.factor(metadata$Species))], RowSideColors=pal[as.integer(as.factor(metadata$Collection))+9], cexCol = 0.2 + 1/log10(15), cexRow = 0.2 + 1/log10(15))
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
Look at the correlation between this and expression:
nameID=read.table("../../genome_anotation_data/ensemble_to_genename.txt",sep="\t", header = T, stringsAsFactors = F,col.names = c("Geneid","gene","source")) %>% dplyr::select(-source)
HumanCounts=read.table("../Human/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)
ChimpCounts=read.table("../Chimp/data/RNAseq/ExonCounts/RNAseqOrthoExon.fixed.fc", header = T, stringsAsFactors = F) %>% dplyr::select(-Chr,-Start,-End,-Strand, -Length)
counts_genes=HumanCounts %>% inner_join(ChimpCounts,by="Geneid") %>% inner_join(nameID, by="Geneid") %>% dplyr::select(-Geneid)
counts_genes_nog=counts_genes %>% dplyr::select(-gene)
ExpMean=as.data.frame(cbind(gene=counts_genes$gene, meanExp=rowMeans(counts_genes_nog)))
ThreeMean=as.data.frame(cbind(gene=BothbyGene$gene, meanThree=rowMeans(Genematrix)))
ExpandThree=ExpMean %>% inner_join(ThreeMean,by="gene")
Warning: Column `gene` joining factors with different levels, coercing to
character vector
Plot this:
ExpandThree$meanExp=as.numeric(as.character(ExpandThree$meanExp))
ExpandThree$meanThree=as.numeric(as.character(ExpandThree$meanThree))
ggplot(ExpandThree,aes(x=log10(meanExp),y=log10(meanThree)))+ geom_point() + geom_smooth(method="lm")
Warning: Removed 85 rows containing non-finite values (stat_smooth).
Version | Author | Date |
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f161386 | brimittleman | 2020-01-18 |
This looks pretty good. I can treat the psuedo threeprime as expression to find an expression cuttoff. Next I will normalize and standardize the sum gene counts.
Log2
log_counts_genes <- as.data.frame(log2(Genematrix))
head(log_counts_genes)
X18498_N X18499_N X18502_N X18504_N X18510_N X18523_N X18358_N
A1BG 8.651052 6.906891 7.826548 6.339850 7.098032 8.184875 9.348728
A1BG-AS1 6.906891 5.209453 6.686501 5.087463 6.491853 6.189825 7.257388
A2M 4.807355 1.584963 1.000000 4.169925 2.000000 2.000000 9.257388
A4GALT 8.348728 3.000000 5.426265 4.807355 6.643856 7.434628 6.629357
AAAS 7.189825 5.285402 7.149747 6.930737 7.169925 6.539159 7.044394
AACS 9.211888 9.084808 8.455327 9.038919 9.721099 9.539159 9.449149
X3622_N X3659_N X4973_N pt30_N pt91_N
A1BG 7.055282 8.581201 6.044394 8.379378 7.693487
A1BG-AS1 4.700440 6.658211 3.321928 4.643856 3.807355
A2M 9.881114 10.090112 8.392317 8.588715 8.134426
A4GALT 2.000000 6.714246 1.584963 2.000000 2.000000
AAAS 7.357552 7.599913 6.965784 7.539159 7.139551
AACS 8.607330 8.535275 8.422065 8.562242 8.483816
plotDensities(log_counts_genes, col=pal[as.numeric(metadata$Species)], legend="topright")
Version | Author | Date |
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f161386 | brimittleman | 2020-01-18 |
CPM
cpm <- cpm(Genematrix, log=TRUE)
plotDensities(cpm, col=pal[as.numeric(metadata$Species)], legend="topright")
Version | Author | Date |
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f161386 | brimittleman | 2020-01-18 |
Use log2 cmp:
## Create edgeR object (dge) to calculate TMM normalization
dge_original <- DGEList(counts=as.matrix(Genematrix), genes=rownames(Genematrix), group = as.character(t(labels)))
dge_original <- calcNormFactors(dge_original)
tmm_cpm <- cpm(dge_original, normalized.lib.sizes=TRUE, log=TRUE, prior.count = 0.25)
head(cpm)
X18498_N X18499_N X18502_N X18504_N X18510_N X18523_N
A1BG 5.341650 4.5993068 4.825291 3.911623 3.751466 4.9774440
A1BG-AS1 3.615999 2.9313152 3.698829 2.688464 3.157697 3.0125468
A2M 1.600132 -0.2806202 -1.087332 1.814585 -0.714223 -0.6153132
A4GALT 5.041173 0.8670626 2.472727 2.419003 3.306094 4.2341398
AAAS 3.894256 3.0051013 4.155261 4.495314 3.822198 3.3532893
AACS 5.899931 6.7668568 5.450058 6.592507 6.354400 6.3255013
X18358_N X3622_N X3659_N X4973_N pt30_N pt91_N
A1BG 5.865947 3.8087270 5.334818 2.9688165 5.0434796 4.3424445
A1BG-AS1 3.792472 1.5453264 3.433898 0.4596994 1.4233049 0.6597173
A2M 5.774967 6.6147191 6.838555 5.2833627 5.2514988 4.7792050
A4GALT 3.177039 -0.6493577 3.488798 -0.8146537 -0.6983404 -0.7119011
AAAS 3.583164 4.1066540 4.361269 3.8706954 4.2109429 3.7959070
AACS 5.965997 5.3455790 5.289151 5.3129416 5.2251829 5.1260744
Look at a PCA plot of the log2cpm
#PCA function (original code from Julien Roux)
#Load in the plot_scores function
plot_scores <- function(pca, scores, n, m, cols, points=F, pchs =20, legend=F){
xmin <- min(scores[,n]) - (max(scores[,n]) - min(scores[,n]))*0.05
if (legend == T){ ## let some room (35%) for a legend
xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.50
}
else {
xmax <- max(scores[,n]) + (max(scores[,n]) - min(scores[,n]))*0.05
}
ymin <- min(scores[,m]) - (max(scores[,m]) - min(scores[,m]))*0.05
ymax <- max(scores[,m]) + (max(scores[,m]) - min(scores[,m]))*0.05
plot(scores[,n], scores[,m], xlab=paste("PC", n, ": ", round(summary(pca)$importance[2,n],3)*100, "% variance explained", sep=""), ylab=paste("PC", m, ": ", round(summary(pca)$importance[2,m],3)*100, "% variance explained", sep=""), xlim=c(xmin, xmax), ylim=c(ymin, ymax), type="n")
if (points == F){
text(scores[,n],scores[,m], rownames(scores), col=cols, cex=1)
}
else {
points(scores[,n],scores[,m], col=cols, pch=pchs, cex=1.3)
}
}
pca_genes <- prcomp(t(tmm_cpm), scale = F)
scores <- pca_genes$x
for (n in 1:2){
col.v <- pal[as.integer(metadata$Species)]
plot_scores(pca_genes, scores, n, n+1, col.v)
}
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
Plot the log2cpm
plotDensities(tmm_cpm, col=pal[as.numeric(metadata$Species)], legend="topright")
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
I will need to filter out the lowly expressed genes. This will also be the gene filter I use for the PAS.
Start with log2cpm>2 for 8 of the 12 indiv. Filter the counts
keep.exprs=rowSums(tmm_cpm>2) >8
counts_filtered= Genematrix[keep.exprs,]
plotDensities(counts_filtered, col=pal[as.numeric(metadata$Species)], legend="topright")
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
Make a new dge list and filter:
labels <- paste(metadata$Species, metadata$Line, sep=" ")
dge_in_cutoff <- DGEList(counts=as.matrix(counts_filtered), genes=rownames(counts_filtered), group = as.character(t(labels)))
dge_in_cutoff <- calcNormFactors(dge_in_cutoff)
cpm_in_cutoff <- cpm(dge_in_cutoff, normalized.lib.sizes=TRUE, log=TRUE, prior.count = 0.25)
head(cpm_in_cutoff)
X18498_N X18499_N X18502_N X18504_N X18510_N X18523_N X18358_N
A1BG 5.329663 4.631635 4.907843 3.908975 3.666584 5.080271 5.914427
AAAS 3.870285 3.013698 4.231888 4.498912 3.738314 3.437223 3.612862
AACS 5.890160 6.808219 5.536123 6.605652 6.286849 6.433791 6.014800
AAGAB 6.897081 6.540471 6.287438 6.708810 6.727998 5.851978 6.571066
AAK1 7.173705 7.038504 7.187389 6.461596 6.756658 7.299552 7.137050
AAMDC 3.918769 4.452891 4.589439 4.375697 3.310955 3.994837 4.211144
X3622_N X3659_N X4973_N pt30_N pt91_N
A1BG 3.940920 5.403320 3.058774 5.219852 4.475256
AAAS 4.242667 4.423008 3.977759 4.380532 3.922213
AACS 5.491146 5.357427 5.432327 5.402580 5.264779
AAGAB 6.138503 6.203235 5.949545 5.997346 5.922666
AAK1 6.458894 6.673281 7.758506 6.217116 6.284230
AAMDC 4.838552 4.741566 5.398303 4.879696 5.346957
GenesCutoff=rownames(cpm_in_cutoff)
NormalizedGenesCuttoff=as.data.frame(cbind(Gene_stable_ID=GenesCutoff, cpm_in_cutoff))
Plot the historgram:
hist(cpm_in_cutoff, xlab = "Log2(CPM)", main = "Log2(CPM) values for genes meeting the filtering criteria", breaks = 100 )
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
This looks relatively normal. I will next look at the voom transformed values with quantile normalization.
Species <- factor(metadata$Species)
design <- model.matrix(~ 0 + Species)
head(design)
SpeciesChimp SpeciesHuman
1 0 1
2 0 1
3 0 1
4 0 1
5 0 1
6 0 1
colnames(design) <- gsub("Species", "", dput(colnames(design)))
c("SpeciesChimp", "SpeciesHuman")
cpm.voom<- voom(counts_filtered, design, normalize.method="quantile", plot=T)
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
boxplot(cpm.voom$E, col = pal[as.numeric(metadata$Species)],las=2)
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
plotDensities(cpm.voom, col = pal[as.numeric(metadata$Species)], legend = "topleft")
Version | Author | Date |
---|---|---|
f161386 | brimittleman | 2020-01-18 |
This looks like a good cuttoff. I will make a list of the genes that pass the cutoff.
length(GenesCutoff)
[1] 9731
GenesCutoffDF=as.data.frame(GenesCutoff) %>% rename("genes"=GenesCutoff)
#mkdir ../data/OverlapBenchmark
write.table(GenesCutoffDF,"../data/OverlapBenchmark/genesPassingCuttoff.txt", col.names = T, row.names = F,quote = F)
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] RColorBrewer_1.1-2 scales_1.0.0 edgeR_3.24.0
[4] limma_3.38.2 forcats_0.3.0 stringr_1.3.1
[7] dplyr_0.8.0.1 purrr_0.3.2 readr_1.3.1
[10] tidyr_0.8.3 tibble_2.1.1 ggplot2_3.1.1
[13] tidyverse_1.2.1 R.utils_2.7.0 R.oo_1.22.0
[16] R.methodsS3_1.7.1 gplots_3.0.1 workflowr_1.6.0
loaded via a namespace (and not attached):
[1] locfit_1.5-9.1 Rcpp_1.0.2 lubridate_1.7.4
[4] lattice_0.20-38 gtools_3.8.1 assertthat_0.2.0
[7] rprojroot_1.3-2 digest_0.6.18 R6_2.3.0
[10] cellranger_1.1.0 plyr_1.8.4 backports_1.1.2
[13] evaluate_0.12 httr_1.3.1 pillar_1.3.1
[16] rlang_0.4.0 lazyeval_0.2.1 readxl_1.1.0
[19] rstudioapi_0.10 gdata_2.18.0 whisker_0.3-2
[22] rmarkdown_1.10 labeling_0.3 munsell_0.5.0
[25] broom_0.5.1 compiler_3.5.1 httpuv_1.4.5
[28] modelr_0.1.2 pkgconfig_2.0.2 htmltools_0.3.6
[31] tidyselect_0.2.5 crayon_1.3.4 withr_2.1.2
[34] later_0.7.5 bitops_1.0-6 grid_3.5.1
[37] nlme_3.1-137 jsonlite_1.6 gtable_0.2.0
[40] git2r_0.26.1 magrittr_1.5 KernSmooth_2.23-15
[43] cli_1.1.0 stringi_1.2.4 fs_1.3.1
[46] promises_1.0.1 xml2_1.2.0 generics_0.0.2
[49] tools_3.5.1 glue_1.3.0 hms_0.4.2
[52] yaml_2.2.0 colorspace_1.3-2 caTools_1.17.1.1
[55] rvest_0.3.2 knitr_1.20 haven_1.1.2