EdgeR, MA plot and Peak Annotation of nuclei ATAC-seq
Evangelyn Sim
2/19/2021
Last updated: 2021-02-19
Checks: 6 1
Knit directory: 2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/
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Introduction
In the GEO submission 1 processed files were uploaded.
- mouseATAC_peaks_cov2.bed.saf.pe.mx.fix_filt
They have been uploaded in the /output folder and will be used below to generate different figures.
Used libraries and functions
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_legend1. EdgeR
Read files
PRIOR = 20
FDR = 0.01
rm1 <- read.delim("/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/Github/2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/output/mouseATAC_peaks_cov2.bed.saf.pe.mx.fix_filt", header = TRUE, row.names = 1)
matrix = rm1[,c(1:6)]
phenoN = data.frame(colnames(matrix))
colnames(phenoN) = "sampleN"
phenoN$sample = gsub(".[0-9]", "", phenoN$sampleN)
pheno = data.frame(phenoN[,c(2)])
colnames(pheno) = "sample"
write.table(pheno, file="/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/Github/2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/output/pheno.matrix_cov2.txt", sep="\t", quote = F, row.names = F)
pheno = read.delim(file="/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/Github/2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/output/pheno.matrix_cov2.txt")
########################################################################################################
attach(pheno)
design = model.matrix(as.formula("~ 0 + sample"))
detach(pheno)
design sampleLacz sampleYap
1 1 0
2 1 0
3 1 0
4 0 1
5 0 1
6 0 1
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$sample
[1] "contr.treatment"D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)
Contrast = makeContrasts(atac_cov2_LacZvsYap = sampleYap - sampleLacz,
levels=design)
res = list()
contrast.name = colnames(Contrast)
for(i in 1:length(contrast.name)){
lrt = glmLRT(fit, contrast = Contrast[,i])
results = lrt$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),]
write.table(res[[i]], file= paste0("/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/Github/2021_UoM_Yap_shRNA_nuclei_RNAseq_ATACseq/output/edgeR_", contrast.name[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$PValue <= 0.01 , "red", "black"))
mxFDR = res[[i]][res[[i]]$PValue <= 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))
}2. MA plot of EdgeR result
par(mfrow=c(1,1))
for(i in 1:length(contrast.name)){
plot(res[[i]]$logFC, -log10(res[[i]]$PValue), pch=20, cex=0.8, col=res[[i]]$cs,
xlab = "LogFC", ylab = "-log10(PValue)",
main = paste0(contrast.name[i],
"\np<=0.01, N=", res[[i]][1,ncol(res[[i]])-2],
"\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
grid(nx = NULL, ny = NULL, col = "blue", lty = "dotted")
}
3. Genome Feature of Peaks Enriched in AAV6:lacZ-shRNA or AAV6:Yap-shRNA
Select peaks with p<=0.01
#!/bin/bash
set -x
#Create sets of foreground regions in bed format
for XLS in *xls ; do
UP=$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 > $BGHomer annotate peaks
#!/bin/bash
set -x
REF=/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/refgenome/Mus_musculus.GRCm38.96.gtf
#PATH=$PATH:/group/card2/Evangelyn_Sim/NGS/app/homer/.//bin/
for BED in *.up.bed *.dn.bed ; do
OUT=$BED.homeranno.txt
annotatePeaks.pl $BED mm10 -gtf $REF -go go -annStats $BED.stats.txt > $OUT
donePlot Homer peak annotation results
files = list.files(path = "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/2.pks/4.pkstats/", pattern = ".stats.txt", full.names = T)
mx = lapply(files, read.delim, header=T)
files[1] "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/2.pks/4.pkstats//edgeR_atac_cov2_LacZvsYap.xls.dn.bed.stats.txt"
[2] "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/2.pks/4.pkstats//edgeR_atac_cov2_LacZvsYap.xls.up.bed.stats.txt"for(i in 1:length(mx)){
mxFDR = mx[[i]][c(1:5),]
#write.table(mxFDR,
# file = paste0(gsub("./|.txt","",files[[i]]),".tidy.txt"),
# col.names = T, row.names = F, sep = "\t")
}
files1 = list.files(path = "/group/card2/Evangelyn_Sim/Collaboration_Kev_UoM/Sequencing_ATAC_RNA/20190530_ATAC_run1/R/2.pks/4.pkstats/", pattern = ".stats.tidy.txt", full.names = T)
mx1 = lapply(files1, read.delim, header=T)
for(j in 1:length(mx1)){
mx[[j]]=PieDonut(mx1[[j]],aes(Annotation,count=Number.of.peaks),r0=0.5,start=3*pi/2,labelpositionThreshold=0.1)
}multi = arrangeGrob(mx[[1]],mx[[2]],
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] ggpubr_0.4.0 cowplot_1.0.0 gridExtra_2.3 extrafont_0.17
[5] waffle_0.7.0 webr_0.1.5 moonBook_0.2.3 ggplot2_3.3.2
[9] dplyr_1.0.2 edgeR_3.26.8 limma_3.40.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-150 fs_1.5.0 RColorBrewer_1.1-2
[4] insight_0.9.0 rprojroot_1.3-2 tools_3.6.1
[7] backports_1.1.10 R6_2.5.0 DT_0.14
[10] sjlabelled_1.1.6 colorspace_1.4-1 withr_2.3.0
[13] tidyselect_1.1.0 mnormt_1.5-6 extrafontdb_1.0
[16] curl_4.3 compiler_3.6.1 git2r_0.27.1
[19] flextable_0.5.10 xml2_1.3.2 officer_0.3.12
[22] labeling_0.4.2 scales_1.1.1 lmtest_0.9-38
[25] psych_1.9.12.31 readr_1.4.0 systemfonts_0.2.3
[28] stringr_1.4.0 digest_0.6.27 foreign_0.8-71
[31] editData_0.1.2 rmarkdown_2.5 rio_0.5.16
[34] base64enc_0.1-3 pkgconfig_2.0.3 htmltools_0.5.0
[37] fastmap_1.0.1 highr_0.8 rvg_0.2.5
[40] htmlwidgets_1.5.2 rlang_0.4.7 readxl_1.3.1
[43] rstudioapi_0.11 shiny_1.5.0 farver_2.0.3
[46] generics_0.1.0 zoo_1.8-8 jsonlite_1.7.0
[49] zip_2.1.1 car_3.0-10 magrittr_1.5
[52] Rcpp_1.0.5 munsell_0.5.0 abind_1.4-5
[55] gdtools_0.2.2 lifecycle_0.2.0 stringi_1.5.3
[58] whisker_0.4 yaml_2.2.1 carData_3.0-4
[61] MASS_7.3-51.6 parallel_3.6.1 promises_1.1.1
[64] sjmisc_2.8.5 forcats_0.5.0 crayon_1.3.4
[67] miniUI_0.1.1.1 lattice_0.20-41 haven_2.3.1
[70] hms_0.5.3 locfit_1.5-9.4 knitr_1.30
[73] pillar_1.4.6 uuid_0.1-4 ggsignif_0.6.0
[76] glue_1.4.2 evaluate_0.14 data.table_1.13.2
[79] vcd_1.4-8 vctrs_0.3.2 tweenr_1.0.1
[82] httpuv_1.5.4 Rttf2pt1_1.3.8 cellranger_1.1.0
[85] gtable_0.3.0 purrr_0.3.4 polyclip_1.10-0
[88] tidyr_1.1.2 xfun_0.18 ggforce_0.3.2
[91] openxlsx_4.2.3 mime_0.9 xtable_1.8-4
[94] broom_0.7.0 rstatix_0.6.0 later_1.1.0.1
[97] tibble_3.0.3 shinyWidgets_0.5.4 rrtable_0.2.1
[100] ellipsis_0.3.1 ztable_0.2.0 devEMF_3.8