EdgeR, Scatter Plot and Homer Analysis of Female versus Male (Peaks)
Evangelyn Sim
2/15/2021
Last updated: 2021-02-19
Checks: 6 1
Knit directory: Human_Development_ATACseq_bulk/
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Untracked files:
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Untracked: *xls.up.bed
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Untracked: FetalvsYoung_noXY.jn.rnk
Untracked: Homo_sapiens.GRCh38.96.fulllength.saf
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Introduction
In the GEO submission, 4 processed files (peaks) were uploaded.
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all_unfiltered.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all.fix_filt.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.hum.fix_filt.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.MvsF.fix_filt.csv
They have been uploaded in the /output folder and will be used below to generate different figures.
Used libraries and functions
- bedtools/2.27.1
- Homer
library(edgeR)Loading required package: limmalibrary(limma)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
library(moonBook)
library(webr)
library(waffle)
library(extrafont)Registering fonts with Rlibrary(grid)
library(gridExtra)
Attaching package: 'gridExtra'The following object is masked from 'package:dplyr':
combinelibrary(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************library(ggpubr)
Attaching package: 'ggpubr'The following object is masked from 'package:cowplot':
get_legendlibrary(RColorBrewer)Read files
PRIOR = 20
FDR = 0.05
rm1 <- read.csv("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.hum.fix_filt.csv", row.names = 1)
info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/ATACseq_samplesheet.txt", header = TRUE, sep = "\t", stringsAsFactors = F)
info = info[c(8:20),]
m = match(info$ID,names(rm1))
rm2 = rm1[,m]
rm1 = rm2
sampleinfo = info
levels(factor(sampleinfo$Group))[1] "Adult"levels(factor(sampleinfo$BinSex))[1] "Adult_F" "Adult_M"table(colnames(rm2)==sampleinfo$ID)
TRUE
13 matrix = rm2
pheno = infoSex-specific Differentially Regulated Genes in Adult
Differential gene expresison analysis
attach(pheno)
design = model.matrix(as.formula("~ 0 + BinSex + Batch"))
detach(pheno)
design BinSexAdult_F BinSexAdult_M Batch
1 0 1 1
2 0 1 1
3 0 1 1
4 0 1 2
5 0 1 2
6 0 1 2
7 0 1 2
8 1 0 2
9 1 0 2
10 1 0 2
11 1 0 2
12 1 0 2
13 1 0 2
attr(,"assign")
[1] 1 1 2
attr(,"contrasts")
attr(,"contrasts")$BinSex
[1] "contr.treatment"D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)
Contrast = makeContrasts(FAdultvsMAdult = BinSexAdult_M - BinSexAdult_F,
levels=design)
res = list()
contrast.name = colnames(Contrast)
for(i in 1:length(contrast.name)){
lrt = glmLRT(fit, contrast = Contrast[,i])
results = lrt$table
disp = lrt$dispersion
fitted.vals = lrt$fitted.values
coefficients = lrt$coefficients
results$adj.p.value = p.adjust(p = results$PValue, method = "fdr" )
table(row.names(results) == row.names(fitted.vals))
Name = row.names(results)
res0 = cbind(Name, results, disp, fitted.vals, coefficients)
res[[i]] = res0[order(res0$adj.p.value),]
res[[i]]$Chr= gsub(".*_|:.*$", "", rownames(res[[i]]))
res[[i]] = mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0 & res[[i]]$Chr=="X", "tan2",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0 & res[[i]]$Chr=="Y", "tan2",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0 & res[[i]]$Chr=="Y", "tan2",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0 & res[[i]]$Chr=="X", "tan2",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0 ,"dodgerblue1",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0 , "deeppink1", "grey80")))))))
mxFDR = res[[i]][res[[i]]$adj.p.value <= FDR,]
mxFDR_Up = mxFDR[mxFDR$logFC>0,]
mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
res[[i]]= mutate(res[[i]], FDR= nrow(mxFDR))
res[[i]]= mutate(res[[i]], FDRup= nrow(mxFDR_Up))
res[[i]]= mutate(res[[i]], FDRdn= nrow(mxFDR_Dn))
}
for(i in 1:length(contrast.name)){
print(contrast.name[i])
print(table(res[[i]]$adj.p.value < 0.05))
}[1] "FAdultvsMAdult"
FALSE TRUE
97482 288 par(mfrow=c(1,1))
for(i in 1:length(contrast.name)){
plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name[i],
"\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2],
"\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}
Genome Feature of Peaks Enriched in Female or Male
Select peaks with p<=0.01
#!/bin/bash
set -x
#Create sets of foreground regions in bed format
for XLS in *xls ; do
UP=$XLS.up.bed
DN=$XLS.dn.bed
awk ' $5<0.01 && $2>0 {print $1"\t"$1}' $XLS \
| cut -d '_' -f2- | sed 's/:/\t/' | sed 's/-/\t/' \
| bedtools sort > $UP
awk ' $5<0.01 && $2<0 {print $1"\t"$1}' $XLS \
| cut -d '_' -f2- | sed 's/:/\t/' | sed 's/-/\t/' \
| bedtools sort > $DN
done
#Create a set of background regions from those not changing in any comparison
BG=$XLS.bg.bed
tail -qn +2 *xls \
| awk '$5>0.1 {print $1}' \
| sort | uniq -c \
| cut -d '_' -f2- | tr ':-' '\t' \
| bedtools sort > $BGHomer annotate peaks
#!/bin/bash
set -x
REF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/Homo_sapiens.GRCh38.96.gtf
#PATH=$PATH:/group/card2/Evangelyn_Sim/NGS/app/homer/.//bin/
for BED in *.up.bed *.dn.bed ; do
OUT=$BED.homeranno.txt
mkdir go/$BED
annotatePeaks.pl $BED hg38 -gtf $REF -go go/$BED -annStats $BED.stats.txt > $OUT
donePlot Homer peak annotation results
files = list.files(path = "/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/20180726_hATACseq_MF/R/5.pks.mg.mapq30.rmBL.q30/6.pkstats/p001_MvF_20200624", pattern = ".stats.txt", full.names = T)
mx = lapply(files, read.delim, header=T, stringsAsFactors = F)
for(i in 1:length(mx)){
mx[[i]] = mx[[i]][c(1:5),]
mx[[i]]$Number.of.peaks = as.numeric(mx[[i]]$Number.of.peaks)
mx[[i]]$totalpeaks = sum(mx[[i]]$Number.of.peaks)
mx[[i]]$percentage = round(mx[[i]]$Number.of.peaks/sum(mx[[i]]$Number.of.peaks) *100, digits = 2)
print(PieDonut(mx[[i]],aes(Annotation,count=Number.of.peaks),r0=0.5,start=3*pi/2,labelpositionThreshold=0.1, showPieName = T, showDonutName = T,
title = gsub("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/20180726_hATACseq_MF/R/5.pks.mg.mapq30.rmBL.q30/6.pkstats/p001_MvF_20200624/edgeR_ATAC_pks_all_hum_MvF_|.xls|.bed.stats.txt", "", files[[i]])))
}
Transcription Factor Motif Enrichment Analysis of Peaks Enriched in Female or Male by Homer
#!/bin/bash
set -x
CWD=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/20180726_hATACseq_MF/R/5.pks.mg.mapq30.rmBL.q30/7.homer/p001_all
echo $CWD
REF=/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/refgenome/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa
#PATH=$PATH:/home/esim/software/.//bin/
#Create sets of foreground regions in bed format
for XLS in *xls ; do
UP=$XLS.up.bed
DN=$XLS.dn.bed
awk ' $5<0.01 && $2>0 {print $1"\t"$1}' $XLS \
| cut -d '_' -f2- | sed 's/:/\t/' | sed 's/-/\t/' \
| bedtools sort > $UP
awk ' $5<0.01 && $2<0 {print $1"\t"$1}' $XLS \
| cut -d '_' -f2- | sed 's/:/\t/' | sed 's/-/\t/' \
| bedtools sort > $DN
done
#Create a set of background regions from those not changing in any comparison
BG=$XLS.bg.bed
tail -qn +2 *xls \
| awk '$5>0.1 {print $1}' \
| sort | uniq -c \
| awk '$1~/6/ {print $2}' \
| cut -d '_' -f2- | tr ':-' '\t' \
| bedtools sort > $BG
cd $CWD
#Call Homer enriched motifs with default background, then with ATAC peak BG
for FG in *up.bed *dn.bed ; do
cd $CWD
BED=$CWD/$FG
#find enriched motifs
findMotifsGenome.pl $FG $REF $FG.df.out -p 10 -keepFiles
cd $FG.df.out
rm -rf instances
mkdir instances
cd instances
for i in ../homerResults/motif*.motif ; do
BASE=`basename $i`
mkdir $BASE
findMotifsGenome.pl $BED $REF $BASE -find $i | sort -k6gr > $BASE/$BASE &
done
cd $CWD
done
wait
cd $CWD
#Call Homer enriched motifs with ATAC peak BG
for FG in *up.bed *dn.bed ; do
cd $CWD
BED=$CWD/$FG
#find enriched motifs
findMotifsGenome.pl $FG $REF $FG.out -bg $BG -p 10 -keepFiles
#find instances of enriched motifs
cd $FG.out
rm -rf instances
mkdir instances
cd instances
for i in ../homerResults/motif*.motif ; do
BASE=`basename $i`
mkdir $BASE
findMotifsGenome.pl $BED $REF $BASE -find $i | sort -k6gr > $BASE/$BASE &
done ; wait
cd $CWD
done
for MOTIF in `find . | grep instances | grep motif$` ; do
OUT=$MOTIF.bed
awk '{print $1,$2,length($3)}' $MOTIF \
| grep -v PositionID | cut -d '_' -f2 \
| tr ':' '\t' | sed 's/-/\t/' \
| awk '{printf "%s\t%.0f\t%.0f\n", $1,(($2+$3)/2)+$4-10,(($2+$3)/2)+$4+10}' > $OUT
donemx1 = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/20180726_hATACseq_MF/R/5.pks.mg.mapq30.rmBL.q30/7.homer/p001_human_MvF_20200624/edgeR_ATAC_pks_all_hum_MvF_FAdultvsMAdult.xls.up.bed.df.out/knownResults.txt", header=TRUE, sep = "\t")
colnames(mx1) = c("Motif.Name","Consensus","P.value","Log.P.value",
"q.value..Benjamini.","Peak.number","Peak.number.with.Motif",
"Background.number", "Background.number.with.Motif")
mx1$TF = gsub("[(].*", "", mx1$Motif.Name)
mx1$TF = gsub("/.*", "", mx1$TF)
mx1$TF = gsub("PGR","PR", mx1$TF)
mx1$Log.P.value = -(mx1$Log.P.value)
mx1 = mx1[!duplicated(mx1$TF),]
mx1 = mx1[c(1:10),]
mx1 = mx1[order(mx1$Log.P.value),]
mx1$TF = toupper(mx1$TF)
mx1$TF = factor(mx1$TF, levels = mx1$TF)
s1 = ggplot(mx1, aes(TF, Log.P.value, color=Log.P.value))+
geom_bar(stat = "identity", fill="white", color="dodgerblue1") +
labs(title = "TF enriched in male")+
theme_minimal()+
coord_flip()
mx2 = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/20180726_hATACseq_MF/R/5.pks.mg.mapq30.rmBL.q30/7.homer/p001_human_MvF_20200624/edgeR_ATAC_pks_all_hum_MvF_FAdultvsMAdult.xls.dn.bed.df.out/knownResults.txt", header=TRUE, sep = "\t")
colnames(mx2) = c("Motif.Name","Consensus","P.value","Log.P.value",
"q.value..Benjamini.","Peak.number","Peak.number.with.Motif",
"Background.number", "Background.number.with.Motif")
mx2$TF = gsub("[(].*", "", mx2$Motif.Name)
mx2$TF = gsub("/.*", "", mx2$TF)
mx2$Log.P.value = -(mx2$Log.P.value)
mx2 = mx2[!duplicated(mx2$TF),]
mx2 = mx2[c(1:10),]
mx2 = mx2[order(mx2$Log.P.value),]
mx2$TF = toupper(mx2$TF)
mx2$TF = factor(mx2$TF, levels = mx2$TF)
s2 = ggplot(mx2, aes(TF, Log.P.value, color=Log.P.value))+
geom_bar(stat = "identity", fill="white", color="deeppink1") +
labs(title = "TF enriched in female")+
theme_minimal()+
coord_flip()multi = arrangeGrob(s1,s2,
ncol = 2, nrow = 1)
plot = as_ggplot(multi)
plot
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] RColorBrewer_1.1-2 ggpubr_0.4.0 cowplot_1.0.0 gridExtra_2.3
[5] extrafont_0.17 waffle_0.7.0 webr_0.1.5 moonBook_0.2.3
[9] ggplot2_3.3.2 dplyr_1.0.2 edgeR_3.26.8 limma_3.40.6
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-150 fs_1.5.0 insight_0.9.0
[4] rprojroot_1.3-2 tools_3.6.1 backports_1.1.10
[7] R6_2.5.0 DT_0.14 sjlabelled_1.1.6
[10] colorspace_1.4-1 withr_2.3.0 tidyselect_1.1.0
[13] mnormt_1.5-6 extrafontdb_1.0 curl_4.3
[16] compiler_3.6.1 git2r_0.27.1 flextable_0.5.10
[19] xml2_1.3.2 officer_0.3.12 labeling_0.4.2
[22] scales_1.1.1 lmtest_0.9-38 psych_1.9.12.31
[25] readr_1.4.0 systemfonts_0.2.3 stringr_1.4.0
[28] digest_0.6.27 foreign_0.8-71 editData_0.1.2
[31] rmarkdown_2.5 rio_0.5.16 base64enc_0.1-3
[34] pkgconfig_2.0.3 htmltools_0.5.0 fastmap_1.0.1
[37] highr_0.8 rvg_0.2.5 htmlwidgets_1.5.2
[40] rlang_0.4.7 readxl_1.3.1 rstudioapi_0.11
[43] shiny_1.5.0 farver_2.0.3 generics_0.1.0
[46] zoo_1.8-8 jsonlite_1.7.0 zip_2.1.1
[49] car_3.0-10 magrittr_1.5 Rcpp_1.0.5
[52] munsell_0.5.0 abind_1.4-5 gdtools_0.2.2
[55] lifecycle_0.2.0 stringi_1.5.3 whisker_0.4
[58] yaml_2.2.1 carData_3.0-4 MASS_7.3-51.6
[61] parallel_3.6.1 promises_1.1.1 sjmisc_2.8.5
[64] forcats_0.5.0 crayon_1.3.4 miniUI_0.1.1.1
[67] lattice_0.20-41 haven_2.3.1 hms_0.5.3
[70] locfit_1.5-9.4 knitr_1.30 pillar_1.4.6
[73] uuid_0.1-4 ggsignif_0.6.0 glue_1.4.2
[76] evaluate_0.14 data.table_1.13.2 vcd_1.4-8
[79] vctrs_0.3.2 tweenr_1.0.1 httpuv_1.5.4
[82] Rttf2pt1_1.3.8 cellranger_1.1.0 gtable_0.3.0
[85] purrr_0.3.4 polyclip_1.10-0 tidyr_1.1.2
[88] xfun_0.18 ggforce_0.3.2 openxlsx_4.2.3
[91] mime_0.9 xtable_1.8-4 broom_0.7.0
[94] rstatix_0.6.0 later_1.1.0.1 tibble_3.0.3
[97] shinyWidgets_0.5.4 rrtable_0.2.1 ellipsis_0.3.1
[100] ztable_0.2.0 devEMF_3.8