FunctionalEnrichmentAnalysisPeak
FunctionalEnrichmentAnalysisPeak.RmdIntroduction
After DE analysis, functional enrichment analysis is often performed to interpret the results.
DEbPeak provides two kinds of functional enrichment analysis based on clusterProfiler for peak-related data:
- Gene ontology (GO) analysis with DA peak binding genes
- Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis with DA peak binding genes
Gene Set Enrichment Analysis (GSEA) is not available for peak-related data.
Example data
The data used here are from In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer:
- RNA-seq data: the sgCtrl vs sgFli1 RNA sequencing at D8 Cl13 p.i., the raw data are stored in GSE149838
- ATAC-seq data: sgCtrl vs sgFli1 ATAC sequencing at D9 Cl13 p.i., the raw data are stored in GSE149836
Count matrix
The raw data:
# read the raw data
atac.counts = utils::read.table(file = "/home/songyabing/R/learn/tmp/DEbPeak/GSE149836_combUnionReadsWithLabels.txt",
sep = "\t", header = T)
head(atac.counts)## chr start end X290_1_S4 X290_2_S5 X360_1_S6 X360_2_S7 R_1_S2 R_2_S3
## 1 chr1 3131803 3131948 2 15 0 3 4 0
## 2 chr1 3210046 3210121 0 1 0 1 0 0
## 3 chr1 3265577 3265648 3 2 0 8 5 2
## 4 chr1 3345206 3345286 3 2 1 0 2 1
## 5 chr1 3505380 3505504 3 4 0 12 4 2
## 6 chr1 3513642 3513745 0 0 2 3 13 2To perform analysis (including Quality Control), we need to preprocess the data (row: feature, column: sample):
# read the processed data, the feature is consist of peak region, gene symbol and annotated binding region.
atac.counts.file = system.file("extdata", "RA_ATAC_count.txt", package = "DEbPeak")
atac.counts = utils::read.table(file = atac.counts.file, sep = "\t", header = T)
head(atac.counts)## Fli1KO_1 Fli1KO_2 Fli1KO_3
## chr1_GL456211_random:112828-112899|LOC100041034|DI 4 6 14
## chr1_GL456211_random:113055-113190|LOC100041034|DI 7 13 10
## chr1_GL456211_random:147874-147989|LOC100041034|DI 15 16 22
## chr1_GL456211_random:167241-167436|LOC100041034|DI 53 40 43
## chr1_GL456211_random:174677-174747|LOC100041034|I 9 6 17
## chr1_GL456211_random:199244-199444|LOC100041034|DI 23 14 26
## Fli1KO_4 WT_1 WT_2
## chr1_GL456211_random:112828-112899|LOC100041034|DI 12 10 3
## chr1_GL456211_random:113055-113190|LOC100041034|DI 11 21 31
## chr1_GL456211_random:147874-147989|LOC100041034|DI 14 23 27
## chr1_GL456211_random:167241-167436|LOC100041034|DI 54 39 46
## chr1_GL456211_random:174677-174747|LOC100041034|I 6 14 10
## chr1_GL456211_random:199244-199444|LOC100041034|DI 11 27 46If you have raw bam files of input/control and treatment samples and sample metadata, DEbPeak provides PeakMatrix to prepare the above count matrix.
Sample metadata
And, we also need the sample metadata:
# read the processed data
atac.meta.file = system.file("extdata", "RA_ATAC_meta.txt", package = "DEbPeak")
atac.meta = utils::read.table(file = atac.meta.file, sep = "\t", header = T)
head(atac.meta)## condition
## Fli1KO_1 KO
## Fli1KO_2 KO
## Fli1KO_3 KO
## Fli1KO_4 KO
## WT_1 WT
## WT_2 WTCreate DESeqDataSet
With above data, we can create DESeqDataSet object:
suppressWarnings(suppressMessages(library(DESeq2)))
# create dds
dds.atac <- DESeq2::DESeqDataSetFromMatrix(
countData = atac.counts, colData = atac.meta,
design = ~condition
)## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsLoad packages
# library
suppressWarnings(suppressMessages(library(DESeq2))) # Already loaded
suppressWarnings(suppressMessages(library(DEbPeak)))Differential Expression Analysis
We will use DESeq2 as example:
# set control level
dds.atac$condition <- relevel(dds.atac$condition, ref = "WT")
# conduct differential expressed genes analysis
dds.atac <- DESeq(dds.atac)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.## final dispersion estimates## fitting model and testing
# extract results
dds.atac.results <- results(dds.atac,contrast=c("condition",'KO','WT'))
dds.atac.results.ordered <- dds.atac.results[order(dds.atac.results$log2FoldChange,decreasing = TRUE),]
head(dds.atac.results.ordered)## log2 fold change (MLE): condition KO vs WT
## Wald test p-value: condition KO vs WT
## DataFrame with 6 rows and 6 columns
## baseMean log2FoldChange lfcSE
## <numeric> <numeric> <numeric>
## chr8:94825359-94825432|Ciapin1|P 12.10658 6.43921 1.52702
## chr3:105628355-105628501|Ddx20|I 11.18000 6.32317 1.52344
## chr1:118440678-118440831|2900060B14Rik|I 7.83684 5.79091 1.56965
## chr1:165368382-165368517|Dcaf6|I 6.14017 5.43928 1.61963
## chr16:59434969-59435053|Gabrr3|I 5.70677 5.34867 1.63142
## chr11:20780454-20780545|Aftph|DI 5.39047 5.26962 1.63661
## stat pvalue padj
## <numeric> <numeric> <numeric>
## chr8:94825359-94825432|Ciapin1|P 4.21686 2.47728e-05 0.00126570
## chr3:105628355-105628501|Ddx20|I 4.15058 3.31633e-05 0.00159881
## chr1:118440678-118440831|2900060B14Rik|I 3.68931 2.24864e-04 0.00689256
## chr1:165368382-165368517|Dcaf6|I 3.35834 7.84110e-04 0.01786577
## chr16:59434969-59435053|Gabrr3|I 3.27854 1.04345e-03 0.02191561
## chr11:20780454-20780545|Aftph|DI 3.21985 1.28258e-03 0.02530960GO enrichment
Different from those in RNA-seq Functional Enrichment Analysis (RNA-seq), we need to use data.type to indicate that the data are from peak-related data, and peak.anno.key to specify the peak binding to which region are used for analysis, available peak.anno.key values are: Promoter, 5' UTR, 3' UTR, Exon, Intron, Downstream, Distal Intergenic, All.
# save results to working directory
# ConductFE(deres = dds.atac.results.ordered, data.type = "ATAC", peak.anno.key = "All",
# gene.type = "SYMBOL", signif = "padj", l2fc.threshold = 0, enrich.type = "GO")
# return list of results
all.go.results=ConductFE(deres = dds.atac.results.ordered, data.type = "ATAC", peak.anno.key = "All",
gene.type = "SYMBOL", signif = "padj", l2fc.threshold = 0,enrich.type = "GO",str.width = 50, save = F)Up regulated genes
Overview
up.go.res=all.go.results[["UP"]][["GO"]]
up.go.res.table=up.go.res[["table"]]
head(up.go.res.table)## ONTOLOGY ID Description
## GO:0022407 BP GO:0022407 regulation of cell-cell adhesion
## GO:0045785 BP GO:0045785 positive regulation of cell adhesion
## GO:0050863 BP GO:0050863 regulation of T cell activation
## GO:0007159 BP GO:0007159 leukocyte cell-cell adhesion
## GO:1903037 BP GO:1903037 regulation of leukocyte cell-cell adhesion
## GO:0042098 BP GO:0042098 T cell proliferation
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0022407 98/2024 427/23328 1.834990e-19 1.083928e-15 7.992829e-16
## GO:0045785 98/2024 435/23328 7.213431e-19 2.130487e-15 1.571009e-15
## GO:0050863 80/2024 321/23328 2.362160e-18 4.651093e-15 3.429690e-15
## GO:0007159 82/2024 345/23328 1.894198e-17 2.797257e-14 2.062682e-14
## GO:1903037 76/2024 309/23328 3.723402e-17 4.398827e-14 3.243671e-14
## GO:0042098 59/2024 213/23328 3.896259e-16 3.835867e-13 2.828547e-13
## geneID
## GO:0022407 Malt1/Ubash3b/Myo10/Anxa1/Prkcq/Cd81/Rasgrp1/Blm/Havcr2/Twsg1/Il20rb/Pik3r1/Cd6/Glmn/Ccl5/Nr4a3/Casp3/Cd3e/Ifng/Prkca/Cited2/Dusp10/Zmiz1/Nck1/Peli1/Serpine2/Cdkn2a/Ikzf1/Cd4/Adam19/Mad1l1/Cd44/Ptprc/Il12rb1/Cd28/Slamf1/Cd86/Cd47/Thy1/Vtcn1/Adam8/Tgfbr2/Muc4/Nf2/Ets1/Cd244a/Mbp/Runx3/Card11/Ccl28/Nfat5/Rhoh/Il2/Prkcz/Zfp608/Pag1/Runx1/Notch1/Cd80/Abl1/Tnfaip3/Itgb2/Aif1/Akna/Ptafr/Dusp22/Nfkbiz/Ccr2/Il6/Lck/Tnfaip8l2/Itgal/Lgals3/Cyld/Rdx/Piezo1/Ndfip1/Ptpn22/Zc3h12a/Coro1a/Tnfrsf14/Cdh1/Egr3/Jak1/Trpv4/Ctla4/Nck2/Adora2a/Il21/Cblb/Itpkb/Src/Rgcc/Gata3/Magi2/Cyrib/Il6st/Nckap1l
## GO:0045785 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
## GO:0050863 Malt1/Anxa1/Prkcq/Cd81/Braf/Rasgrp1/Blm/Havcr2/Csk/Twsg1/Il20rb/Cd6/Glmn/Ccl5/Casp3/Cd3e/Ifng/Dusp10/Zmiz1/Tnfrsf1b/Nck1/Peli1/Cdkn2a/Ikzf1/Cd4/Mad1l1/Cd44/Ptprc/Il12rb1/Cd28/Slamf1/Cd86/Cd47/Thy1/Vtcn1/Adam8/Tgfbr2/Cd244a/Tox/Sit1/Runx3/Card11/Prdm1/Rhoh/Mapk8ip1/Il2/Prkcz/Zfp608/Pag1/Runx1/Cd80/Abl1/Aif1/Sos1/Dusp22/Nfkbiz/Ccr2/Il6/Lck/Tnfaip8l2/Itgal/Lgals3/Cyld/Ndfip1/Ptpn22/Zc3h12a/Coro1a/Tnfrsf14/Egr3/Rac2/Ctla4/Nck2/Adora2a/Il21/Cblb/Itpkb/Gata3/Cyrib/Il6st/Nckap1l
## GO:0007159 Malt1/Anxa1/Prkcq/Cd81/Jam2/Rasgrp1/Blm/Havcr2/Twsg1/Il20rb/Cd6/Glmn/Ccl5/Nr4a3/Casp3/Cd3e/Ifng/Dusp10/Zmiz1/Nck1/Peli1/Cdkn2a/Ikzf1/Cd4/Mad1l1/Cd44/Ptprc/Il12rb1/Cd28/Slamf1/Cd86/Cd47/Thy1/Cx3cr1/Vtcn1/Adam8/Tgfbr2/Ets1/Cd244a/Selplg/Sema4d/Runx3/Card11/Ccl28/Nfat5/Rhoh/Stk10/Il2/Prkcz/Zfp608/Pag1/Runx1/Cd80/Itgb2/Aif1/Ptafr/Dusp22/Nfkbiz/Ccr2/Il6/Lck/Tnfaip8l2/Itgal/Lgals3/Cyld/Ndfip1/Ptpn22/Zc3h12a/Coro1a/Tnfrsf14/Egr3/Rac2/Ctla4/Nck2/Adora2a/Il21/Cblb/Itpkb/Gata3/Cyrib/Il6st/Nckap1l
## GO:1903037 Malt1/Anxa1/Prkcq/Cd81/Rasgrp1/Blm/Havcr2/Twsg1/Il20rb/Cd6/Glmn/Ccl5/Nr4a3/Casp3/Cd3e/Ifng/Dusp10/Zmiz1/Nck1/Peli1/Cdkn2a/Ikzf1/Cd4/Mad1l1/Cd44/Ptprc/Il12rb1/Cd28/Slamf1/Cd86/Cd47/Thy1/Vtcn1/Adam8/Tgfbr2/Ets1/Cd244a/Runx3/Card11/Ccl28/Nfat5/Rhoh/Il2/Prkcz/Zfp608/Pag1/Runx1/Cd80/Itgb2/Aif1/Ptafr/Dusp22/Nfkbiz/Ccr2/Il6/Lck/Tnfaip8l2/Itgal/Lgals3/Cyld/Ndfip1/Ptpn22/Zc3h12a/Coro1a/Tnfrsf14/Egr3/Ctla4/Nck2/Adora2a/Il21/Cblb/Itpkb/Gata3/Cyrib/Il6st/Nckap1l
## GO:0042098 Malt1/Anxa1/Prkcq/Cd81/Rasgrp1/Blm/Havcr2/Twsg1/Il20rb/Cd6/Glmn/Ccl5/Casp3/Cd3e/Ifng/Tnfrsf1b/Nck1/Peli1/Cdkn2a/Cd4/Mad1l1/Cd44/Ptprc/Il12rb1/Cd28/Slamf1/Cd86/Fyn/Vtcn1/Tgfbr2/Cd244a/Card11/Mapk8ip1/Il2/Cd80/Dock2/Abl1/Itgb2/Aif1/Sos1/Cxcr4/Ccr2/Itgad/Il6/Itgal/Lgals3/Ndfip1/P2rx7/Ptpn22/Coro1a/Tnfrsf14/Rac2/Ctla4/Nck2/Il21/Cblb/Il6st/Fkbp1a/Nckap1l
## Count
## GO:0022407 98
## GO:0045785 98
## GO:0050863 80
## GO:0007159 82
## GO:1903037 76
## GO:0042098 59
Down regulated genes
Overview
down.go.res=all.go.results[["DOWN"]][["GO"]]
down.go.res.table = down.go.res[["table"]]
head(down.go.res.table)## ONTOLOGY ID
## GO:0022407 BP GO:0022407
## GO:0007264 BP GO:0007264
## GO:1903034 BP GO:1903034
## GO:0007265 BP GO:0007265
## GO:0051056 BP GO:0051056
## GO:0010543 BP GO:0010543
## Description GeneRatio
## GO:0022407 regulation of cell-cell adhesion 89/1843
## GO:0007264 small GTPase mediated signal transduction 88/1843
## GO:1903034 regulation of response to wounding 47/1843
## GO:0007265 Ras protein signal transduction 69/1843
## GO:0051056 regulation of small GTPase mediated signal transduction 58/1843
## GO:0010543 regulation of platelet activation 19/1843
## BgRatio pvalue p.adjust qvalue
## GO:0022407 427/23328 1.509144e-17 8.866221e-14 6.155719e-14
## GO:0007264 437/23328 2.135011e-16 6.271594e-13 4.354298e-13
## GO:1903034 186/23328 5.015682e-13 9.822378e-10 6.819568e-10
## GO:0007265 350/23328 1.095362e-12 1.608813e-09 1.116981e-09
## GO:0051056 271/23328 2.087054e-12 2.452289e-09 1.702597e-09
## GO:0010543 38/23328 8.510919e-12 8.333608e-09 5.785932e-09
## geneID
## GO:0022407 Mbp/Runx1/Zc3h12d/Pdcd1lg2/Lef1/Bcl10/Egr3/Sh2b3/Blk/Card11/Socs5/Cited2/Tmem131l/Crtam/Shb/Efnb1/Rasgrp1/Smad7/Il18/Pik3r1/Itga4/Il6st/Cd83/Prkca/Ass1/Adk/Nr4a3/Mdk/Tenm3/Stat5b/Twsg1/Itpkb/Abat/Ndfip1/Prkg1/Tnfrsf21/Epha7/Prkcq/Casp3/Ripor2/Icos/Cd9/Jak1/Tnfaip3/Tespa1/Cd74/Selp/Tnfrsf14/Zfp608/Gcnt2/Efnb2/Akna/H2-Aa/Icosl/Btla/Alox12/Tnr/Adrb2/Dusp10/Magi2/Tbx18/Ccr7/Bmp7/Socs6/Zc3h12a/Ifng/Scrib/Plpp3/Ccr5/Klf4/Syk/Myo10/Gm9733/Tnfsf4/Spta1/Serpine2/Ptger3/Il1b/Swap70/Il15/Gata3/Prkcd/Il1rl2/Arg1/Efna5/Il7/Ifnb1/Cx3cl1/Hspd1
## GO:0007264 Rsu1/Plk2/Map4k4/Arhgef1/Cyth1/Ctnnal1/Garnl3/Rapgef4/Heg1/Prex1/Rap2a/Akap13/Camk2d/Itsn1/Psd3/Ccdc88c/Arf6/Jun/Nucb2/Rasgrp1/Arhgef10/Farp1/Itgb1/Rhoc/Tiam1/Nrp1/Arhgap18/Arl6/Spry2/Arhgap26/Dennd4c/Celsr1/Itpkb/Rap1a/Plce1/Rasgef1a/Vangl2/Rras2/Fgd2/Rasa3/Arhgap25/Rhoq/Ripor2/Rap1gap2/Sos2/Vav3/Rasgrf1/Ralgapa2/Arhgef3/Trio/Dab2ip/Dgkz/Srgap1/Dok2/Rapgef5/Iqsec1/Iqsec2/Ralgps2/Cyth3/Ralgds/Arfgef1/Rapgef1/Arhgef33/Rap2b/Cdon/Prag1/Rnd3/Rgl1/Notch2/Arhgef12/Arhgap35/Arhgdib/Sipa1l1/Arhgap17/Arhgef37/Arhgap24/Spata13/Nckap1/Cdc42ep3/Rhobtb1/Rasgrp3/Iqsec3/Sdcbp/Arhgap32/Elmo1/Dock7/Cd2ap/Auts2
## GO:1903034 Sh2b3/Blk/Ptprj/Itgb1/Pros1/Prkca/Mdk/Phldb2/Ntrk3/Abat/Prkg1/Apoh/Adam17/Prkcq/Cd9/Xylt1/Selp/Tlr4/Fgf2/Txk/Alox12/Tnr/Clec7a/Adrb2/Dusp10/Tec/Adra2a/Arfgef1/Cd109/D130043K22Rik/Ano6/Plpp3/Klf4/Syk/Epha4/Igf1r/Stk24/Pla2g4a/Cd36/Serpine2/Ptger3/Clasp2/Tspan8/Prkcd/Smoc2/Ptger4/Lrp1
## GO:0007265 Rsu1/Plk2/Map4k4/Arhgef1/Cyth1/Ctnnal1/Heg1/Prex1/Rap2a/Akap13/Camk2d/Itsn1/Psd3/Arf6/Jun/Rasgrp1/Arhgef10/Farp1/Itgb1/Tiam1/Nrp1/Arl6/Spry2/Dennd4c/Celsr1/Itpkb/Rap1a/Plce1/Rasgef1a/Vangl2/Rras2/Fgd2/Rasa3/Ripor2/Sos2/Vav3/Rasgrf1/Ralgapa2/Arhgef3/Trio/Dab2ip/Dgkz/Srgap1/Dok2/Iqsec1/Iqsec2/Ralgps2/Cyth3/Arfgef1/Rapgef1/Arhgef33/Rap2b/Prag1/Notch2/Arhgef12/Arhgap35/Arhgdib/Arhgap17/Arhgef37/Arhgap24/Spata13/Nckap1/Cdc42ep3/Rasgrp3/Iqsec3/Sdcbp/Arhgap32/Elmo1/Auts2
## GO:0051056 Map4k4/Arhgef1/Cyth1/Garnl3/Heg1/Prex1/Akap13/Camk2d/Itsn1/Psd3/Arf6/Rasgrp1/Arhgef10/Farp1/Itgb1/Tiam1/Nrp1/Arhgap18/Spry2/Arhgap26/Dennd4c/Itpkb/Plce1/Rasgef1a/Fgd2/Rasa3/Arhgap25/Ripor2/Rap1gap2/Sos2/Vav3/Rasgrf1/Ralgapa2/Arhgef3/Trio/Dab2ip/Dgkz/Iqsec1/Iqsec2/Ralgps2/Cyth3/Arfgef1/Rapgef1/Arhgef33/Cdon/Prag1/Notch2/Arhgef12/Arhgap35/Arhgdib/Sipa1l1/Arhgap17/Arhgef37/Arhgap24/Spata13/Iqsec3/Cd2ap/Auts2
## GO:0010543 Sh2b3/Blk/Ptprj/Prkca/Abat/Prkg1/Prkcq/Cd9/Selp/Tlr4/Txk/Alox12/Adrb2/Tec/Syk/Pla2g4a/Serpine2/Ptger3/Prkcd
## Count
## GO:0022407 89
## GO:0007264 88
## GO:1903034 47
## GO:0007265 69
## GO:0051056 58
## GO:0010543 19
KEGG enrichment
# save results to working directory
# ConductFE(deres = dds.atac.results.ordered, data.type = "ATAC", peak.anno.key = "All",
# gene.type = "SYMBOL", signif = "padj", l2fc.threshold = 0,enrich.type = "KEGG")
all.kegg.results=ConductFE(deres = dds.atac.results.ordered, data.type = "ATAC", peak.anno.key = "All",
gene.type = "SYMBOL", signif = "padj", l2fc.threshold = 0,enrich.type = "KEGG", str.width = 50, save = F)Up regulated genes
Overview
up.kegg.res=all.kegg.results[["UP"]][["KEGG"]]
up.kegg.res.table=up.kegg.res[["table"]]
head(up.kegg.res.table)## ID
## mmu04660 mmu04660
## mmu05235 mmu05235
## mmu04650 mmu04650
## mmu04810 mmu04810
## mmu04658 mmu04658
## mmu04659 mmu04659
## Description
## mmu04660 T cell receptor signaling pathway - Mus musculus (house mouse)
## mmu05235 PD-L1 expression and PD-1 checkpoint pathway in cancer - Mus musculus (house mouse)
## mmu04650 Natural killer cell mediated cytotoxicity - Mus musculus (house mouse)
## mmu04810 Regulation of actin cytoskeleton - Mus musculus (house mouse)
## mmu04658 Th1 and Th2 cell differentiation - Mus musculus (house mouse)
## mmu04659 Th17 cell differentiation - Mus musculus (house mouse)
## GeneRatio BgRatio pvalue p.adjust qvalue
## mmu04660 38/947 103/9270 3.713529e-13 1.229178e-10 7.974315e-11
## mmu05235 31/947 88/9270 2.190028e-10 3.624496e-08 2.351398e-08
## mmu04650 37/947 122/9270 5.540093e-10 6.112570e-08 3.965540e-08
## mmu04810 54/947 231/9270 3.281464e-09 2.715411e-07 1.761628e-07
## mmu04658 29/947 88/9270 4.956182e-09 3.280993e-07 2.128550e-07
## mmu04659 32/947 105/9270 7.133231e-09 3.482919e-07 2.259550e-07
## geneID
## mmu04660 Malt1/Cd247/Nfkbia/Prkcq/Jun/Rasgrp1/Pik3r1/Grap2/Ikbkb/Cd3e/Ifng/Nck1/Cd4/Ptprc/Cd28/Pak4/Fyn/Nfatc1/Nfkb1/Raf1/Card11/Cd8a/Il2/Pik3r3/Sos1/Cd3d/Pik3ca/Lcp2/Pdcd1/Ppp3ca/Lck/Vav1/Pik3cd/Itk/Ctla4/Nck2/Fos/Cblb
## mmu05235 Cd247/Nfkbia/Prkcq/Jun/Rasgrp1/Batf/Pik3r1/Ikbkb/Cd3e/Ifng/Hif1a/Pten/Cd4/Cd28/Ifngr1/Nfatc1/Nfkb1/Raf1/Egf/Ptpn11/Pik3r3/Cd3d/Pik3ca/Pdcd1/Ppp3ca/Lck/Eml4/Pik3cd/Jak1/Fos/Map2k3
## mmu04650 Cd247/Klrd1/Braf/Sh3bp2/Pik3r1/Casp3/Klrb1c/Ifng/Prkca/Rac1/Ifnab/Prkcb/Ifngr1/Ptk2b/Fyn/Nfatc1/Cd244a/Raf1/Gzmb/Ptpn11/Raet1d/Pik3r3/Itgb2/Hcst/Sos1/Pik3ca/Lcp2/Klra8/Tnfsf10/Ppp3ca/Lck/Itgal/Vav1/Fasl/Pik3cd/Rac2/Prf1
## mmu04810 Itga9/Itgav/Arhgef12/Braf/Diaph3/Itga2/Crkl/Pik3r1/Fgf4/Pdgfrb/Rac1/Fgd3/Actn1/Pak4/Arpc3/Fgf20/Raf1/Myh9/Ppp1r12a/Pip4k2a/Spata13/Hc/Pip5k1b/Scin/Tmsb4x/Rras2/Actr3/Egf/Pik3r3/Ppp1r12c/Itgb2/Sos1/Cxcr4/Pik3ca/Actb/Itgad/Ppp1ccb/Itga1/Iqgap1/Itgal/Myl10/Pxn/C9/Rdx/Vav1/Itga3/F2r/Pik3cd/Rac2/Src/Itgb3/Rgcc/Baiap2/Nckap1l
## mmu04658 Cd247/Nfkbia/Prkcq/Jun/Ikbkb/Cd3e/Ifng/H2-Eb2/Maf/Cd4/Il12rb1/Ifngr1/Nfatc1/Nfkb1/Runx3/Il2/Notch1/Cd3d/Il12rb2/Tyk2/Ppp3ca/Lck/Maml3/Rbpj/Stat4/Maml2/Jak1/Fos/Gata3
## mmu04659 Rora/Cd247/Nfkbia/Prkcq/Jun/Ikbkb/Cd3e/Ifng/Hif1a/H2-Eb2/Cd4/Il12rb1/Ifngr1/Tgfbr2/Nfatc1/Nfkb1/Il2/Runx1/Il22/Iltifb/Cd3d/Rxra/Tyk2/Ppp3ca/Il6/Lck/Smad3/Jak1/Fos/Il21/Gata3/Il6st
## Count
## mmu04660 38
## mmu05235 31
## mmu04650 37
## mmu04810 54
## mmu04658 29
## mmu04659 32
Down regulated genes
Overview
down.kegg.res=all.kegg.results[["DOWN"]][["KEGG"]]
down.kegg.res.table=down.kegg.res[["table"]]
head(down.kegg.res.table)## ID
## mmu04971 mmu04971
## mmu04022 mmu04022
## mmu04510 mmu04510
## mmu04724 mmu04724
## mmu04014 mmu04014
## mmu04625 mmu04625
## Description
## mmu04971 Gastric acid secretion - Mus musculus (house mouse)
## mmu04022 cGMP-PKG signaling pathway - Mus musculus (house mouse)
## mmu04510 Focal adhesion - Mus musculus (house mouse)
## mmu04724 Glutamatergic synapse - Mus musculus (house mouse)
## mmu04014 Ras signaling pathway - Mus musculus (house mouse)
## mmu04625 C-type lectin receptor signaling pathway - Mus musculus (house mouse)
## GeneRatio BgRatio pvalue p.adjust qvalue
## mmu04971 22/847 75/9270 4.949399e-07 0.0001077581 7.988485e-05
## mmu04022 37/847 173/9270 6.570615e-07 0.0001077581 7.988485e-05
## mmu04510 39/847 203/9270 5.476379e-06 0.0005987508 4.438749e-04
## mmu04724 26/847 114/9270 8.932193e-06 0.0007324399 5.429833e-04
## mmu04014 42/847 235/9270 1.574604e-05 0.0009435154 6.994610e-04
## mmu04625 25/847 112/9270 1.965326e-05 0.0009435154 6.994610e-04
## geneID
## mmu04971 Itpr1/Itpr2/Plcb4/Camk2d/Prkacb/Atp1b3/Ezr/Kcnj1/Prkca/Atp1a1/Atp4a/Car2/Calm2/Adcy7/Hrh2/Kcnk10/Kcnj2/Kcnj16/Chrm3/Calm5/Mylk3/Plcb1
## mmu04022 Pde2a/Itpr1/Ppp3ca/Itpr2/Atp2b1/Plcb4/Pik3cg/Atp1b3/Mef2d/Irs2/Atp1a1/Mef2a/Prkg1/Calm2/Insr/Ppp3r1/Cnga1/Nfatc1/Cacna1d/Adrb2/Adra2a/Adcy7/4930544G11Rik/Mef2b/Agtr1b/Gtf2i/Pde3a/Irag1/Ppp1cb/Pln/Kcnmb1/Atp2b4/Raf1/Calm5/Mylk3/Plcb1/Vdac2
## mmu04510 Mylpf/Jun/Ccnd2/Pik3r1/Itga4/Itgb1/Prkca/Col6a1/Actn1/Itga7/Rap1a/Sos2/Vav3/Rasgrf1/Itga2/Fyn/Myl12b/Shc3/Tnr/Ccnd3/Bcar1/4930544G11Rik/Rapgef1/Pak3/Itga9/Lama3/Mapk8/Flt1/Arhgap35/Itgb8/Igf1r/Hgf/Bcl2/Ppp1cb/Parvg/Raf1/Pdgfc/Vcl/Mylk3
## mmu04724 Itpr1/Ppp3ca/Itpr2/Grk3/Slc1a2/Plcb4/Prkacb/Gng7/Prkca/Slc38a1/Gls2/Gng2/Ppp3r1/Slc38a2/Cacna1d/Shank1/Adcy7/Slc1a7/Homer1/Slc1a3/Pla2g4a/Grm5/Gria3/Grik4/Plcb1/Grm7
## mmu04014 Fasl/Arf6/Prkacb/Gng7/Rasgrp1/Kit/Pik3r1/Prkca/Tiam1/Fgf18/Ets2/Rap1a/Plce1/Rras2/Rasa3/Gng2/Sos2/Rasgrf1/Rasa1/Calm2/Ralgapa2/Insr/Fgf2/Shc3/Rapgef5/Ralgds/4930544G11Rik/Pak3/Rgl1/Mapk8/Flt1/Igf1r/Hgf/Pla2g4a/Plaat3/Rasgrp3/Fgf9/Raf1/Efna5/Pdgfc/Calm5/Htr7
## mmu04625 Relb/Itpr1/Ppp3ca/Itpr2/Bcl10/Egr3/Jun/Pik3r1/Rras2/Calm2/Ppp3r1/Nfatc1/Clec7a/4930544G11Rik/Mapk8/Arhgef12/Syk/Mapkapk2/Clec4e/Il1b/Bcl3/Prkcd/Raf1/Ccl22/Calm5
## Count
## mmu04971 22
## mmu04022 37
## mmu04510 39
## mmu04724 26
## mmu04014 42
## mmu04625 25
Session info
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /home/softwares/anaconda3/envs/r4.0/lib/libopenblasp-r0.3.12.so
##
## locale:
## [1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=zh_CN.UTF-8 LC_COLLATE=zh_CN.UTF-8
## [5] LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=zh_CN.UTF-8
## [7] LC_PAPER=zh_CN.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] org.Mm.eg.db_3.12.0 AnnotationDbi_1.52.0
## [3] DEbPeak_1.4.0 DESeq2_1.30.1
## [5] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [7] MatrixGenerics_1.2.1 matrixStats_0.58.0
## [9] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7
## [11] IRanges_2.24.1 S4Vectors_0.28.1
## [13] BiocGenerics_0.42.0
##
## loaded via a namespace (and not attached):
## [1] rsvd_1.0.3
## [2] ggvenn_0.1.9
## [3] apeglm_1.12.0
## [4] Rsamtools_2.6.0
## [5] rsvg_2.1
## [6] foreach_1.5.1
## [7] rprojroot_2.0.2
## [8] crayon_1.4.1
## [9] V8_3.4.2
## [10] MASS_7.3-58
## [11] nlme_3.1-152
## [12] backports_1.2.1
## [13] sva_3.38.0
## [14] GOSemSim_2.25.0
## [15] rlang_1.1.0
## [16] XVector_0.30.0
## [17] readxl_1.4.2
## [18] irlba_2.3.5
## [19] limma_3.46.0
## [20] GOstats_2.56.0
## [21] BiocParallel_1.24.1
## [22] rjson_0.2.20
## [23] bit64_4.0.5
## [24] glue_1.6.2
## [25] DiffBind_3.0.15
## [26] mixsqp_0.3-43
## [27] pheatmap_1.0.12
## [28] parallel_4.0.3
## [29] DEFormats_1.18.0
## [30] base64url_1.4
## [31] tcltk_4.0.3
## [32] DOSE_3.23.2
## [33] haven_2.5.2
## [34] tidyselect_1.2.0
## [35] rio_0.5.27
## [36] XML_3.99-0.6
## [37] tidyr_1.3.0
## [38] ggpubr_0.4.0
## [39] GenomicAlignments_1.26.0
## [40] xtable_1.8-4
## [41] ggnetwork_0.5.12
## [42] magrittr_2.0.3
## [43] evaluate_0.14
## [44] ggplot2_3.4.2
## [45] cli_3.6.1
## [46] zlibbioc_1.36.0
## [47] hwriter_1.3.2
## [48] rstudioapi_0.14
## [49] bslib_0.3.1
## [50] GreyListChIP_1.22.0
## [51] fastmatch_1.1-3
## [52] BiocSingular_1.6.0
## [53] xfun_0.30
## [54] askpass_1.1
## [55] clue_0.3-59
## [56] gson_0.0.9
## [57] cluster_2.1.1
## [58] caTools_1.18.2
## [59] tidygraph_1.2.0
## [60] tibble_3.2.1
## [61] ggrepel_0.9.1
## [62] Biostrings_2.58.0
## [63] png_0.1-7
## [64] withr_2.5.0
## [65] bitops_1.0-6
## [66] ggforce_0.3.3
## [67] RBGL_1.66.0
## [68] plyr_1.8.6
## [69] cellranger_1.1.0
## [70] GSEABase_1.52.1
## [71] pcaPP_2.0-1
## [72] dqrng_0.2.1
## [73] coda_0.19-4
## [74] pillar_1.9.0
## [75] gplots_3.1.1
## [76] GlobalOptions_0.1.2
## [77] cachem_1.0.4
## [78] GenomicFeatures_1.42.2
## [79] fs_1.5.0
## [80] GetoptLong_1.0.5
## [81] clusterProfiler_4.7.1
## [82] DelayedMatrixStats_1.12.3
## [83] vctrs_0.6.2
## [84] generics_0.1.0
## [85] plot3D_1.4
## [86] tools_4.0.3
## [87] foreign_0.8-81
## [88] NOISeq_2.34.0
## [89] munsell_0.5.0
## [90] tweenr_1.0.2
## [91] fgsea_1.16.0
## [92] DelayedArray_0.16.3
## [93] fastmap_1.1.0
## [94] compiler_4.0.3
## [95] abind_1.4-5
## [96] rtracklayer_1.50.0
## [97] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [98] GenomeInfoDbData_1.2.4
## [99] gridExtra_2.3
## [100] edgeR_3.32.1
## [101] lattice_0.20-45
## [102] ggnewscale_0.4.7
## [103] AnnotationForge_1.32.0
## [104] utf8_1.2.1
## [105] dplyr_1.1.2
## [106] BiocFileCache_1.14.0
## [107] jsonlite_1.8.4
## [108] scales_1.2.1
## [109] graph_1.68.0
## [110] carData_3.0-4
## [111] sparseMatrixStats_1.2.1
## [112] TFEA.ChIP_1.10.0
## [113] genefilter_1.72.1
## [114] car_3.0-11
## [115] doParallel_1.0.16
## [116] latticeExtra_0.6-29
## [117] R.utils_2.12.0
## [118] brew_1.0-6
## [119] checkmate_2.0.0
## [120] rmarkdown_2.14
## [121] openxlsx_4.2.3
## [122] pkgdown_1.6.1
## [123] cowplot_1.1.1
## [124] textshaping_0.3.6
## [125] forcats_1.0.0
## [126] downloader_0.4
## [127] BSgenome_1.58.0
## [128] igraph_1.4.99.9024
## [129] survival_3.2-10
## [130] numDeriv_2016.8-1.1
## [131] yaml_2.2.1
## [132] plotrix_3.8-2
## [133] systemfonts_1.0.4
## [134] ashr_2.2-47
## [135] SQUAREM_2021.1
## [136] htmltools_0.5.2
## [137] memoise_2.0.0
## [138] VariantAnnotation_1.36.0
## [139] locfit_1.5-9.4
## [140] graphlayouts_0.7.1
## [141] batchtools_0.9.15
## [142] PCAtools_2.2.0
## [143] viridisLite_0.4.0
## [144] rrcov_1.7-0
## [145] digest_0.6.27
## [146] assertthat_0.2.1
## [147] rappdirs_0.3.3
## [148] emdbook_1.3.12
## [149] RSQLite_2.2.5
## [150] amap_0.8-18
## [151] yulab.utils_0.0.4
## [152] debugme_1.1.0
## [153] misc3d_0.9-1
## [154] data.table_1.14.2
## [155] blob_1.2.1
## [156] R.oo_1.24.0
## [157] ragg_0.4.0
## [158] labeling_0.4.2
## [159] splines_4.0.3
## [160] ggupset_0.3.0
## [161] RCurl_1.98-1.3
## [162] broom_1.0.4
## [163] hms_1.1.3
## [164] colorspace_2.0-0
## [165] BiocManager_1.30.16
## [166] shape_1.4.6
## [167] sass_0.4.1
## [168] GEOquery_2.58.0
## [169] Rcpp_1.0.9
## [170] mvtnorm_1.1-2
## [171] circlize_0.4.15
## [172] enrichplot_1.10.2
## [173] fansi_0.4.2
## [174] tzdb_0.3.0
## [175] truncnorm_1.0-8
## [176] ChIPseeker_1.33.0.900
## [177] R6_2.5.0
## [178] grid_4.0.3
## [179] lifecycle_1.0.3
## [180] ShortRead_1.48.0
## [181] zip_2.1.1
## [182] curl_4.3
## [183] ggsignif_0.6.3
## [184] jquerylib_0.1.3
## [185] robustbase_0.95-0
## [186] DO.db_2.9
## [187] Matrix_1.5-4
## [188] qvalue_2.22.0
## [189] desc_1.3.0
## [190] org.Hs.eg.db_3.12.0
## [191] RColorBrewer_1.1-2
## [192] iterators_1.0.13
## [193] stringr_1.5.0
## [194] DOT_0.1
## [195] ggpie_0.2.5
## [196] beachmat_2.6.4
## [197] polyclip_1.10-0
## [198] biomaRt_2.46.3
## [199] purrr_1.0.1
## [200] shadowtext_0.0.9
## [201] gridGraphics_0.5-1
## [202] mgcv_1.8-34
## [203] ComplexHeatmap_2.13.1
## [204] openssl_1.4.3
## [205] patchwork_1.0.0
## [206] bdsmatrix_1.3-4
## [207] codetools_0.2-18
## [208] invgamma_1.1
## [209] GO.db_3.12.1
## [210] gtools_3.8.2
## [211] prettyunits_1.1.1
## [212] dbplyr_2.3.2
## [213] R.methodsS3_1.8.1
## [214] gtable_0.3.0
## [215] DBI_1.1.1
## [216] highr_0.8
## [217] ggfun_0.0.6
## [218] httr_1.4.5
## [219] KernSmooth_2.23-18
## [220] stringi_1.5.3
## [221] progress_1.2.2
## [222] reshape2_1.4.4
## [223] farver_2.1.0
## [224] annotate_1.68.0
## [225] viridis_0.6.1
## [226] Rgraphviz_2.34.0
## [227] xml2_1.3.4
## [228] bbmle_1.0.24
## [229] systemPipeR_1.24.3
## [230] boot_1.3-28
## [231] readr_2.1.4
## [232] geneplotter_1.68.0
## [233] ggplotify_0.1.0
## [234] Category_2.56.0
## [235] DEoptimR_1.0-11
## [236] bit_4.0.4
## [237] scatterpie_0.1.7
## [238] jpeg_0.1-8.1
## [239] ggraph_2.0.5
## [240] pkgconfig_2.0.3
## [241] rstatix_0.7.0
## [242] knitr_1.37