Multi-contrast enrichment analysis of DUX data
Mark Ziemann
2021-07-26
Source: https://github.com/markziemann/dux
Introduction
The goal here is to perform enrichment analysis with the human and mouse DUX data.
suppressPackageStartupMessages({
library("mitch")
library("DESeq2")
library("edgeR")
library("eulerr")
library("UpSetR")
library("gplots")
library("getDEE2")
})
CORES=8Prepare human FSHD data
ss <- read.table("fshd.txt",header=TRUE)
md <- getDEE2Metadata("hsapiens")
mymd <- md[which(md$Sample_name %in% ss$Accession),]
ss <- merge(mymd,ss,by.x="Sample_name",by.y="Accession")
SRRvec <- ss$SRR_accession
x <- getDEE2(species="hsapiens",SRRvec=SRRvec,legacy=TRUE,metadata=md)## For more information about DEE2 QC metrics, visit
## https://github.com/markziemann/dee2/blob/master/qc/qc_metrics.mdxx <- x$GeneCounts
design <- model.matrix(~ss$Group)
rownames(design) <- ss$SRR_accession
xx <- xx[,which(colnames(xx) %in% rownames(design) )]
xx <- xx[which(rowMeans(xx)>10),]
z <- DGEList(counts=xx)
z <- calcNormFactors(z)
z <- estimateDisp(z, design,robust=TRUE,prior.df=1)
fit <- glmFit(z, design)
lrt <- glmLRT(fit)
fshd <- as.data.frame(topTags(lrt,n=Inf))
write.table(fshd,file="fshd.tsv",sep='\t',quote=FALSE)
fshd_up <- rownames(subset(fshd,FDR<0.05 & logFC>0))
fshd_dn <- rownames(subset(fshd,FDR<0.05 & logFC<0))
fshd_subset <- rownames(subset(fshd,FDR<0.05 & logFC>2))
# Read in Watt data
# human
h <- read.table("dux2_edgeR_edger.tsv",header=TRUE,row.names=1)
rownames(h) <- sapply(strsplit(rownames(h),"_"),"[[",1)
h_up <- rownames(subset(h,FDR<0.05 & logFC>0))
h_dn <- rownames(subset(h,FDR<0.05 & logFC<0))
h_subset <- rownames(subset(h,FDR<0.05 & logFC>2))
v1 <- list("fshd up"=fshd_up, "fshd dn"=fshd_dn,
"h up"=h_up,"h dn"=h_dn)
plot(euler(v1),quantities = TRUE)
Prepare Jones data
MCM: strain: ACTA1-MCM/+; treatment: no tamoxifen (CONTROL)
FLExD: strain: FLExDUX4/+treatment: no tamoxifen (DO NOT USE)
dTGM: strain: FLExDUX4/+;MCM/+ treatment: one 5 mg/kg IP injection of tamoxifen (CASE - MILD)
dTGS: FLExDUX4/+;MCM/+ treatment: two 10 mg/kg IP injections of tamoxifen on consecutive days (CASE - SEVERE)
# mouse GSE122562
#URL="https://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122562/suppl/GSE122562_gene_counts$
j <- read.table("GSE122562_gene_counts_table.tsv.gz",header=TRUE,row.names=1)
j[,1]<-NULL
ss <- as.data.frame(colnames(j))
ss$mcm <- grepl("MCM",ss[,1])*1
ss$flexd <- grepl("FLExD",ss[,1])*1
ss$dtgm <- grepl("dTGM",ss[,1])*1
ss$dtgs <- grepl("dTGS",ss[,1])*1
rownames(ss) <- ss[,1]
ss[,1]<-NULL
# here set the contrast
sss <- subset(ss,mcm==1|dtgm==1)
design <- model.matrix(~sss$dtgm)
rownames(design) <- rownames(sss)
jj <- j[,which(colnames(j) %in% rownames(design) )]
jj <- jj[which(rowMeans(jj)>10),]
z <- DGEList(counts=jj)
z <- calcNormFactors(z)
z <- estimateDisp(z, design,robust=TRUE,prior.df=1)
fit <- glmFit(z, design)
lrt<-glmLRT(fit)
dge<-as.data.frame(topTags(lrt,n=Inf))
dge$dispersion<-lrt$dispersion
dge<-merge(dge,lrt$fitted.values,by='row.names')
rownames(dge)=dge$Row.names
dge$Row.names=NULL
dge<-dge[order(dge$PValue),]
head(dge,10)## logFC logCPM LR PValue FDR
## ENSMUSG00000040322 4.145430 5.524643 1519.4858 0.000000e+00 0.000000e+00
## ENSMUSG00000020437 5.432810 5.174086 1224.5266 2.851232e-268 2.142131e-264
## ENSMUSG00000024542 3.011320 4.212642 1220.4921 2.146997e-267 1.075359e-263
## ENSMUSG00000068011 6.345711 4.265444 1127.4326 3.602781e-247 1.353385e-243
## ENSMUSG00000032122 5.995664 4.925190 1100.8525 2.155748e-241 6.478453e-238
## ENSMUSG00000041346 3.280202 4.239581 1091.7976 2.002776e-239 5.015619e-236
## ENSMUSG00000036862 3.382167 5.069340 1003.9379 2.502092e-220 5.370918e-217
## ENSMUSG00000026734 11.103505 2.353322 975.5494 3.706949e-214 6.962577e-211
## ENSMUSG00000045659 4.679723 3.771261 949.1380 2.042257e-208 3.409661e-205
## ENSMUSG00000044447 4.194787 4.501589 922.5110 1.253915e-202 1.884132e-199
## dispersion MCM.1127 MCM.1246 MCM.1349 dTGM.1236
## ENSMUSG00000040322 9.765625e-05 106.953690 115.04141 116.90420 3086.0216
## ENSMUSG00000020437 6.420286e-03 35.321164 37.99211 38.60729 2492.0470
## ENSMUSG00000024542 1.366409e-02 88.027714 94.68427 96.21743 1157.3595
## ENSMUSG00000068011 4.623586e-02 9.935764 10.68710 10.86014 1328.8862
## ENSMUSG00000032122 1.382939e-02 20.184843 21.71120 22.06275 2107.9158
## ENSMUSG00000041346 5.424354e-03 75.786040 81.51689 82.83684 1200.7650
## ENSMUSG00000036862 8.246348e-03 127.368057 136.99948 139.21783 2164.8470
## ENSMUSG00000026734 1.053648e-02 0.000000 0.00000 0.00000 341.6463
## ENSMUSG00000045659 1.500921e-02 21.781858 23.42898 23.80835 913.2603
## ENSMUSG00000044447 1.449207e-02 50.481515 54.29887 55.17810 1508.7009
## dTGM.1308 dTGM.1331
## ENSMUSG00000040322 2825.7735 2985.2536
## ENSMUSG00000020437 2281.8895 2410.6741
## ENSMUSG00000024542 1059.7579 1119.5682
## ENSMUSG00000068011 1216.8195 1285.4940
## ENSMUSG00000032122 1930.1525 2039.0859
## ENSMUSG00000041346 1099.5029 1161.5564
## ENSMUSG00000036862 1982.2827 2094.1581
## ENSMUSG00000026734 312.8349 330.4906
## ENSMUSG00000045659 836.2439 883.4396
## ENSMUSG00000044447 1381.4702 1459.4372jm <- dge
write.table(jm,file="jm.tsv",sep='\t',quote=FALSE)
sss <- subset(ss,mcm==1|dtgs==1)
design <- model.matrix(~sss$dtgs)
rownames(design) <- rownames(sss)
jj <- j[,which(colnames(j) %in% rownames(design) )]
jj <- jj[which(rowMeans(jj)>10),]
z <- DGEList(counts=jj)
z <- calcNormFactors(z)
z <- estimateDisp(z, design,robust=TRUE,prior.df=1)
fit <- glmFit(z, design)
lrt<-glmLRT(fit)
dge<-as.data.frame(topTags(lrt,n=Inf))
dge$dispersion<-lrt$dispersion
dge<-merge(dge,lrt$fitted.values,by='row.names')
rownames(dge)=dge$Row.names
dge$Row.names=NULL
dge<-dge[order(dge$PValue),]
head(dge,10)## logFC logCPM LR PValue FDR
## ENSMUSG00000091747 4.823213 5.166640 1497.693 0.000000e+00 0.000000e+00
## ENSMUSG00000015950 4.166462 4.573994 1416.039 6.871679e-310 5.411791e-306
## ENSMUSG00000020914 6.852232 5.797438 1376.245 3.050561e-301 1.601646e-297
## ENSMUSG00000034768 -3.849882 4.480923 1351.926 5.876642e-296 2.314075e-292
## ENSMUSG00000001131 6.137768 4.449557 1318.287 1.199992e-288 3.392601e-285
## ENSMUSG00000067455 5.348949 4.239762 1318.139 1.292337e-288 3.392601e-285
## ENSMUSG00000046345 -6.893317 4.273700 1280.119 2.363838e-280 5.318973e-277
## ENSMUSG00000038777 -3.024598 4.951953 1248.254 1.988524e-273 3.915156e-270
## ENSMUSG00000062727 7.368636 4.605333 1242.285 3.942227e-272 6.899335e-269
## ENSMUSG00000064288 5.360316 4.050966 1204.621 6.039794e-264 9.513280e-261
## dispersion MCM.1127 MCM.1246 MCM.1349 dTGS.1047
## ENSMUSG00000091747 0.007308578 41.103312 44.205182 45.086409 2668.09650
## ENSMUSG00000015950 0.007362846 41.806168 44.961078 45.857374 1721.21140
## ENSMUSG00000020914 0.009077381 16.055621 17.267262 17.611483 4265.89294
## ENSMUSG00000034768 0.065224750 731.422240 786.619167 802.300364 115.94310
## ENSMUSG00000001131 0.001835045 10.080105 10.840802 11.056913 1636.66914
## ENSMUSG00000067455 0.023280503 14.886713 16.010142 16.329303 1395.64270
## ENSMUSG00000046345 0.018311514 676.562091 727.618986 742.124019 12.85318
## ENSMUSG00000038777 0.012181762 958.006760 1030.302933 1050.841949 269.30376
## ENSMUSG00000062727 0.011519154 4.794572 5.156395 5.259188 1842.33886
## ENSMUSG00000064288 0.028101258 12.890427 13.863206 14.139568 1218.99264
## dTGS.1066 dTGS.1079
## ENSMUSG00000091747 2561.92516 2524.21890
## ENSMUSG00000015950 1652.71938 1628.39476
## ENSMUSG00000020914 4096.14062 4035.85387
## ENSMUSG00000034768 111.32938 109.69084
## ENSMUSG00000001131 1571.54130 1548.41145
## ENSMUSG00000067455 1340.10601 1320.38241
## ENSMUSG00000046345 12.34171 12.16007
## ENSMUSG00000038777 258.58738 254.78150
## ENSMUSG00000062727 1769.02683 1742.99040
## ENSMUSG00000064288 1170.48537 1153.25823js <- dgeNow to see how similar these contrasts are. The overlap is really high, therefore I suggest only using the moderate contrast in downstream mitch analysis.
jm_up <- rownames(subset(jm,FDR<0.05 & logFC>0))
jm_dn <- rownames(subset(jm,FDR<0.05 & logFC<0))
js_up <- rownames(subset(js,FDR<0.05 & logFC>0))
js_dn <- rownames(subset(js,FDR<0.05 & logFC<0))
v1 <- list("mod up"=jm_up, "mod dn"=jm_dn,
"sev up"=js_up,"sev dn"=js_dn)
plot(euler(v1),quantities = TRUE)
Read in Watt data
# mouse simple
# we're not using the factorial
ms <- read.table("EVvsDUX_wDOX_paired.tsv",header=TRUE,row.names=1)
rownames(ms) <- sapply(strsplit(rownames(ms),"_"),"[[",1)
# ortholog mapping
orth <- read.table("mouse2human.txt.sort")
# gene name mapping human
gth <- orth[,c(1,3)]
# gene name mapping human
gtm <- orth[,c(2,3)]Calculate overlap
jm_up <- rownames(subset(jm,FDR<0.05 & logFC>0))
jm_dn <- rownames(subset(jm,FDR<0.05 & logFC<0))
jm_subset <- rownames(subset(jm,FDR<0.05 & logFC>2))
h_up <- rownames(subset(h,FDR<0.05 & logFC>0))
h_dn <- rownames(subset(h,FDR<0.05 & logFC<0))
h_subset <- rownames(subset(h,FDR<0.05 & logFC>2))
ms_up <- rownames(subset(ms,FDR<0.05 & logFC>0))
ms_dn <- rownames(subset(ms,FDR<0.05 & logFC<0))
ms_subset <- rownames(subset(ms,FDR<0.05 & logFC>2))
l <- list("jm_up"=jm_up,"jm_dn"=jm_dn,
"h_up"=h_up,"h_dn"=h_dn,
"fshd up"=fshd_up,"fshd dn"=fshd_dn,
"ms_up"=ms_up,"ms_dn"=ms_dn)
barplot(unlist(lapply(l,length)),main='no. DEGs')
# here are the values
unlist(lapply(l,length))## jm_up jm_dn h_up h_dn fshd up fshd dn ms_up ms_dn
## 4744 4253 3578 3619 2513 483 1902 1495# convert lists to human gene names
jm_up <- unique(gtm[which(gtm$V2 %in% jm_up ),2])
jm_dn <- unique(gtm[which(gtm$V2 %in% jm_dn ),2])
jm_subset <- unique(gtm[which(gtm$V2 %in% jm_subset ),2])
writeLines(jm_subset,con="jonesmouse_subset.txt")
h_up <- unique(gth[which(gth$V1 %in% h_up ),2])
h_dn <- unique(gth[which(gth$V1 %in% h_dn ),2])
h_subset <- unique(gth[which(gth$V1 %in% h_subset ),2])
writeLines(h_subset,con="humanmodel_subset.txt")
ms_up <- unique(gtm[which(gtm$V2 %in% ms_up ),2])
ms_dn <- unique(gtm[which(gtm$V2 %in% ms_dn ),2])
ms_subset <- unique(gtm[which(gtm$V2 %in% ms_subset ),2])
writeLines(ms_subset,con="wattmouse_subset.txt")
fshd_up <- unique(gth[which(gth$V1 %in% fshd_up ),2])
fshd_dn <- unique(gth[which(gth$V1 %in% fshd_dn ),2])
fshd_subset <- unique(gth[which(gth$V1 %in% fshd_subset ),2])
writeLines(fshd_subset,con="fshd_subset.txt")
# venn of subsets
v1 <- list("Jones mouse"=jm_subset,
"human model"=h_subset,
"fshd" = fshd_subset,
"Watt mouse"=ms_subset )
plot(euler(v1),quantities = TRUE,main="subset genes: FDR<0.05 & LogFC>2")
upset(fromList(v1), order.by = "freq", nsets=6)
v1 <- list("jm up"=jm_up,
"jm dn"=jm_dn,
"h up"=h_up,
"h dn"=h_dn,
"fshd up" = fshd_up,
"fshd dn" = fshd_dn,
"ms up"=ms_up,
"ms dn"=ms_dn )
plot(euler(v1),quantities = TRUE,main="FDR genes")
upset(fromList(v1), order.by = "freq", nsets=6)
This is a bit confusing, so we will do separate Venns, comparing each model data with the human fshd data. We can see that the concordance with human is higher with the Watt data as compared to the Jones data. Here, I define concordance ratio as the number of FDR genes that are commonly dysregulated divided by the number of genes with discordant expression.
# human model
v1 <- list("fshd up"=fshd_up,
"fshd dn"=fshd_dn,
"h up"=h_up,
"h dn"=h_dn)
plot(euler(v1),quantities = TRUE)
h_concordance <- (length(intersect(fshd_up,h_up))+length(intersect(fshd_dn,h_dn))) /
(length(intersect(fshd_up,h_dn))+length(intersect(fshd_dn,h_up)))
h_concordance## [1] 0.5576923# jones mouse model
v1 <- list("fshd up"=fshd_up,
"fshd dn"=fshd_dn,
"jm up"=jm_up,
"jm dn"=jm_dn)
plot(euler(v1),quantities = TRUE)
j_concordance <- (length(intersect(fshd_up,jm_up))+length(intersect(fshd_dn,jm_dn))) /
(length(intersect(fshd_up,jm_dn))+length(intersect(fshd_dn,jm_up)))
j_concordance## [1] 6.138756# Watt mouse model
v1 <- list( "fshd up"=fshd_up,
"fshd dn"=fshd_dn,
"ms up"=ms_up,
"ms dn"=ms_dn )
plot(euler(v1),quantities = TRUE)
ms_concordance <- (length(intersect(fshd_up,ms_up))+length(intersect(fshd_dn,ms_dn))) /
(length(intersect(fshd_up,ms_dn))+length(intersect(fshd_dn,ms_up)))
ms_concordance## [1] 5.977011barplot(c(h_concordance,j_concordance,ms_concordance),
names.arg=c("human model","Jones mouse","Watt mouse"),
main="concordance ratio")
Get gene sets
#download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip", destfile="ReactomePathways.gmt.zip")
#unzip("ReactomePathways.gmt.zip")
genesets <- gmt_import("HumanTfPeaks.gmt")Prepare data for running mitch
The mitch_import function creates
hh <- list("fshd"=fshd,"human model"=h)
hhh <- mitch_import(x=hh,DEtype="edger",geneTable=gth)## Note: Mean no. genes in input = 16922.5## Note: no. genes in output = 11955## Note: estimated proportion of input genes in output = 0.706mm <- list("ms"=ms,"jm"=jm)
mmm <- mitch_import(x=mm,DEtype="edger",geneTable=gtm)## Note: Mean no. genes in input = 13889## Note: no. genes in output = 11677## Note: estimated proportion of input genes in output = 0.841mg <- merge(hhh,mmm,by=0)
rownames(mg) <- mg[,1]
mg[,1]<-NULL
colnames(mg) <- c("FSHD","hu_model","Watt_m","Jones_m")
dim(mg)## [1] 10538 4head(mg)## FSHD hu_model Watt_m Jones_m
## A4GALT 2.81314560 -14.988939 0.8182771 5.5001019
## AAAS -0.15717628 -2.435804 0.3877972 0.7776159
## AACS 0.05482256 -1.581711 4.1420212 11.1737053
## AAED1 4.15163719 -1.286093 -0.8848853 -39.4294484
## AAGAB -2.32526173 -1.722220 -1.3839648 0.2777522
## AAK1 1.06457576 14.833940 0.4269837 -1.5398326mg <- apply(mg,2, function(x) {
x[which(x==Inf)] <- 300 ; x[which(x==-Inf)] <- -300 ; return(x)
} )
# pearson
round(cor(mg,method='p'),3)## FSHD hu_model Watt_m Jones_m
## FSHD 1.000 0.410 0.240 0.048
## hu_model 0.410 1.000 0.155 0.002
## Watt_m 0.240 0.155 1.000 0.126
## Jones_m 0.048 0.002 0.126 1.000# spearman
round(cor(mg,method='s'),3)## FSHD hu_model Watt_m Jones_m
## FSHD 1.000 -0.089 0.313 0.433
## hu_model -0.089 1.000 0.167 0.033
## Watt_m 0.313 0.167 1.000 0.488
## Jones_m 0.433 0.033 0.488 1.000colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(cor(mg,method='s'),
cellnote=round(cor(mg,method='s'),3),
trace="none",scale="none",margins=c(15,15),
col=colfunc(25), notecex=2 , main="Spearman")
Here we run mitch unidimensional and then create Venn diagrams of the FDR gene sets.
fshdm <- mitch_import( fshd,DEtype="edger",geneTable=gth )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 18181## Note: no. genes in output = 13632## Note: estimated proportion of input genes in output = 0.75fshdmres <- mitch_calc(x=fshdm,genesets=genesets,priority="effect",resrows=100,cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(fshdmres$enrichment_result,20)## set setSize pANOVA s.dist
## 1385 38839_E2F1_LoVo_None_GSM460122 11 2.518139e-06 -0.8193965
## 530 33604_FAIRE_NHEK_Keratinocyte_GSM460122 10 8.064274e-06 -0.8149905
## 1213 38649_EOMES_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1224 38670_T_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1257 38703_FOXP3_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1291 38738_TCF12_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1320 38769_TFEB_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1350 38805_FOXM1_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1356 38810_NFATC4_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1389 38842_YBX3_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1398 38851_MEF2C_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1406 38862_HSF2_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1407 38904_POLR2A_HeLa_Epithelium_GSM460122 10 8.064274e-06 -0.8149905
## 1556 40354_AR_LNCaP_Epithelium_GSM460122 10 8.064274e-06 -0.8149905
## 1650 41726_TFAM_HeLa_Epithelium_GSM460122 10 8.064274e-06 -0.8149905
## 1822 42643_ARNTL_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1824 42646_BATF3_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1825 42654_FOS_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1848 42680_IRF4_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## 1918 42776_NR1D1_LoVo_None_GSM460122 10 8.064274e-06 -0.8149905
## p.adjustANOVA
## 1385 7.359409e-06
## 530 2.185727e-05
## 1213 2.185727e-05
## 1224 2.185727e-05
## 1257 2.185727e-05
## 1291 2.185727e-05
## 1320 2.185727e-05
## 1350 2.185727e-05
## 1356 2.185727e-05
## 1389 2.185727e-05
## 1398 2.185727e-05
## 1406 2.185727e-05
## 1407 2.185727e-05
## 1556 2.185727e-05
## 1650 2.185727e-05
## 1822 2.185727e-05
## 1824 2.185727e-05
## 1825 2.185727e-05
## 1848 2.185727e-05
## 1918 2.185727e-05fsdhm_up <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
fshdm_dn <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
hmm <- mitch_import( h,DEtype="edger",geneTable=gth )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15664## Note: no. genes in output = 12281## Note: estimated proportion of input genes in output = 0.784hres <- mitch_calc(x=hmm,genesets=genesets,priority="effect",resrows=100,cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(hres$enrichment_result,20)## set setSize pANOVA
## 2473 45613_ZNF274_GM12878_BLymphocyte_GSM460122 11 1.015975e-02
## 7963 732_ZNF274_GM12878_BLymphocyte_GSM460122 11 1.015975e-02
## 2292 44698_LANA_BEC_EndothelialCell_GSM460122 15 3.419221e-03
## 8467 76208_POLR2A_HEK293_Epithelium_GSM460122 12 1.054603e-02
## 3787 49641_ESR1_MCF-7_Epithelium_GSM460122 10 2.008948e-02
## 2096 43823_BCL11A_CAL-1_DendriticCell_GSM460122 53 2.877644e-07
## 8135 74004_ESR1_None_None_GSM460122 13 1.095276e-02
## 7426 69890_FOXA1_None_None_GSM460122 13 1.134431e-02
## 2970 460_ESR1_ECC-1_Epithelium_GSM460122 80 5.988339e-10
## 6857 65213_ZNF454_HEK293_Epithelium_GSM460122 11 2.208551e-02
## 7832 72452_NCOA1_U2OS_None_GSM460122 53 8.501570e-07
## 6527 64544_YBX1_HepG2_Epithelium_GSM460122 78 2.782217e-09
## 3277 47096_EPAS1_None_Macrophage_GSM460122 29 2.913647e-04
## 8466 76209_POLR2A_HEK293_Epithelium_GSM460122 14 1.497346e-02
## 4835 54624_NCAPG_MCF-7_Epithelium_GSM460122 13 2.002706e-02
## 8654 76967_MBOAT4_HeLa_Epithelium_GSM460122 666 4.543936e-57
## 3279 47100_CBFA2T3_CAL-1_DendriticCell_GSM460122 89 3.040319e-09
## 4548 53336_POLR2A_HeLa_Epithelium_GSM460122 672 9.423464e-57
## 4546 53332_POLR2A_HeLa_Epithelium_GSM460122 686 1.120327e-57
## 5719 62573_DROSHA_K562_Erythroblast_GSM460122 14 2.153349e-02
## s.dist p.adjustANOVA
## 2473 0.4476254 1.725165e-02
## 7963 0.4476254 1.725165e-02
## 2292 -0.4366107 6.233781e-03
## 8467 0.4264270 1.786432e-02
## 3787 -0.4245946 3.268437e-02
## 2096 -0.4076416 8.622975e-07
## 8135 -0.4076020 1.851953e-02
## 7426 -0.4056332 1.914111e-02
## 2970 -0.4006465 2.378854e-09
## 6857 -0.3986219 3.564968e-02
## 7832 -0.3911499 2.429822e-06
## 6527 -0.3895679 1.035930e-08
## 3277 -0.3888063 6.152941e-04
## 8466 0.3756303 2.480539e-02
## 4835 -0.3726267 3.260554e-02
## 8654 0.3644027 2.361029e-53
## 3279 -0.3639723 1.127587e-08
## 4548 0.3618351 3.264288e-53
## 4546 0.3613125 1.164244e-53
## 5719 -0.3548661 3.485387e-02hres_up <- subset(hres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
hres_dn <- subset(hres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
jmm <- mitch_import( jm,DEtype="edger",geneTable=gtm )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15026## Note: no. genes in output = 12858## Note: estimated proportion of input genes in output = 0.856jres <- mitch_calc(x=jmm,genesets=genesets,priority="effect",resrows=100,cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(jres$enrichment_result,20)## set setSize pANOVA s.dist
## 1879 42736_NCOA3_LoVo_None_GSM460122 11 3.224060e-04 -0.6262587
## 1320 38776_TFDP2_LoVo_None_GSM460122 11 3.311798e-04 -0.6250416
## 1360 38816_IRX3_LoVo_None_GSM460122 13 1.172649e-04 -0.6169716
## 9232 83263_ATF6_None_MSC_GSM460122 10 7.495346e-04 -0.6155822
## 9826 86517_EPAS1_HUVEC_None_GSM460122 14 6.995517e-05 -0.6137830
## 2139 44056_ARRB1_A549_Epithelium_GSM460122 13 1.300641e-04 -0.6128994
## 1366 38828_BTF3_LoVo_None_GSM460122 10 9.295998e-04 -0.6046700
## 1843 42673_HOXD11_LoVo_None_GSM460122 10 9.295998e-04 -0.6046700
## 1856 42697_NEUROD2_LoVo_None_GSM460122 10 9.295998e-04 -0.6046700
## 10088 87830_CTCF_293T_None_GSM460122 10 9.364217e-04 -0.6042964
## 1221 38665_CBFB_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1229 38674_NKX2-1_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1282 38733_HIF1A_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1817 42644_ATF7_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1825 42652_FERD3L_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1838 42672_HOXB5_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1839 42669_HOXA11_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 1842 42676_HOXD9_LoVo_None_GSM460122 11 5.231674e-04 -0.6039825
## 9789 86202_BCL11A_HUDEP-2_None_GSM460122 12 2.953044e-04 -0.6034174
## 1384 38839_E2F1_LoVo_None_GSM460122 11 5.569155e-04 -0.6010530
## p.adjustANOVA
## 1879 0.0018740975
## 1320 0.0019175993
## 1360 0.0007974777
## 9232 0.0038117828
## 9826 0.0005129553
## 2139 0.0008664001
## 1366 0.0045496168
## 1843 0.0045496168
## 1856 0.0045496168
## 10088 0.0045722513
## 1221 0.0028088527
## 1229 0.0028088527
## 1282 0.0028088527
## 1817 0.0028088527
## 1825 0.0028088527
## 1838 0.0028088527
## 1839 0.0028088527
## 1842 0.0028088527
## 9789 0.0017438377
## 1384 0.0029675595jres_up <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
jres_dn <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
msm <- mitch_import( ms,DEtype="edger",geneTable=gtm )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 12752## Note: no. genes in output = 11830## Note: estimated proportion of input genes in output = 0.928mres <- mitch_calc(x=msm,genesets=genesets,priority="effect",resrows=100,cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres$enrichment_result,20)## set setSize pANOVA
## 8448 76477_CCNT1_C4-2_None_GSM460122 13 0.0001414571
## 8443 76472_CCNT1_LNCaP_Epithelium_GSM460122 10 0.0018028772
## 1871 42852_HOXC13_LoVo_None_GSM460122 16 0.0002497234
## 4304 52539_AR_R1-AD1_Epithelium_GSM460122 11 0.0029181057
## 1793 42693_MIXL1_LoVo_None_GSM460122 10 0.0055116447
## 1795 42695_MTF1_LoVo_None_GSM460122 10 0.0066575617
## 8372 76209_POLR2A_HEK293_Epithelium_GSM460122 14 0.0016486482
## 6638 65083_ZNF582_HEK293_Epithelium_GSM460122 11 0.0052859250
## 5344 57954_NR3C1_MM1.RL_None_GSM460122 10 0.0087595657
## 1790 42689_LHX5_LoVo_None_GSM460122 14 0.0020917740
## 3570 49180_EGFA-T1_HEK293T_Epithelium_GSM460122 11 0.0066306432
## 665 34945_RELA_LCL_Lymphoblastoid_GSM460122 13 0.0037429267
## 669 34955_RELA_LCL_Lymphoblastoid_GSM460122 11 0.0098797877
## 3692 49633_PRKDC_MCF-7_Epithelium_GSM460122 14 0.0036327578
## 6382 64420_PRDM12_HEK293_Epithelium_GSM460122 10 0.0145205705
## 667 34951_RELA_LCL_Lymphoblastoid_GSM460122 13 0.0054121301
## 2009 43685_KDM4C_KYSE-150_EpitheloidCell_GSM460122 10 0.0168014113
## 4930 55644_CDK9_HCT-116_None_GSM460122 12 0.0094687609
## 1796 42704_NR4A2_LoVo_None_GSM460122 10 0.0188870853
## 6764 65213_ZNF454_HEK293_Epithelium_GSM460122 10 0.0194293675
## s.dist p.adjustANOVA
## 8448 -0.6095976 0.0009595339
## 8443 -0.5699831 0.0096520231
## 1871 -0.5289064 0.0016220960
## 4304 -0.5182949 0.0147820922
## 1793 -0.5069205 0.0256651588
## 1795 -0.4956007 0.0303000245
## 8372 0.4858545 0.0089382335
## 6638 -0.4857125 0.0247827537
## 5344 -0.4787648 0.0381576498
## 1790 -0.4750097 0.0110317968
## 3570 -0.4727903 0.0302177138
## 665 -0.4644417 0.0183358743
## 669 -0.4493150 0.0423720272
## 3692 -0.4490098 0.0178901119
## 6382 -0.4464129 0.0582810385
## 667 -0.4456031 0.0252590682
## 2009 -0.4367174 0.0655140136
## 4930 -0.4326451 0.0408482107
## 1796 -0.4288156 0.0716592076
## 6764 -0.4268866 0.0732562816mres_up <- subset(mres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
mres_dn <- subset(mres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
v1 <- list("fshd up"=fsdhm_up,
"fshd dn"=fshdm_dn,
"h up"=hres_up,
"h dn"=hres_dn)
plot(euler(v1),quantities = TRUE)
v1 <- list("fshd up"=fsdhm_up,
"fshd dn"=fshdm_dn,
"j up"=jres_up,
"j dn"=jres_dn)
plot(euler(v1),quantities = TRUE)
v1 <- list("fshd up"=fsdhm_up,
"fshd dn"=fshdm_dn,
"m up"=mres_up,
"m dn"=mres_dn)
plot(euler(v1),quantities = TRUE)
Here we run multivariate mitch with different prioritisation schemes.
# effect size
res1 <- mitch_calc(x=mg,genesets=genesets,priority="effect",resrows=100,cores=CORES)## Note: Enrichments with large effect sizes may not be
## statistically significant.x <- res1$enrichment_result[1:100,c(4:7)]
rownames(x) <- res1$enrichment_result$set[1:nrow(x)]
colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(as.matrix(x),col=colfunc(30),margins=c(5,25),trace="none",
scale="none",cexCol=0.6,cexRow=0.25)
unlink("mitch_plots_simple_eff.html")
capture.output(
mitch_report(res1,"mitch_plots_simple_eff.html")
,file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/RtmpWpaNc2/mitch_plots_simple_eff.rds ".##
##
## processing file: mitch.Rmd##
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output file: /mnt/bfx6/bfx/kevinwatt/dux_mitch/mitch.knit.md
##
##
## Output created: /tmp/RtmpWpaNc2/mitch_report.html
# significance
res3 <- mitch_calc(x=mg,genesets=genesets,priority="significance",resrows=100,cores=CORES)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.x <- res3$enrichment_result[1:100,c(4:7)]
rownames(x) <- res3$enrichment_result$set[1:nrow(x)]
colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(as.matrix(x),col=colfunc(30),margins=c(5,25),trace="none",
scale="none",cexCol=0.6, cexRow=0.25)
# discordance
res2 <- mitch_calc(x=mg,genesets=genesets,priority="SD",resrows=100,cores=CORES)## Note: Prioritisation by SD after selecting sets with
## p.adjustMANOVA<=0.05.x <- res2$enrichment_result[1:100,c(4:7)]
rownames(x) <- res2$enrichment_result$set[1:nrow(x)]
colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(as.matrix(x),col=colfunc(30),margins=c(5,25),trace="none",
scale="none",cexCol=0.6, cexRow=0.25)
Session information
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## 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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.2.1 GGally_2.1.2
## [3] ggplot2_3.3.5 reshape2_1.4.4
## [5] beeswarm_0.4.0 gtools_3.9.2
## [7] tibble_3.1.3 dplyr_1.0.7
## [9] echarts4r_0.4.1 getDEE2_1.2.0
## [11] gplots_3.1.1 UpSetR_1.4.0
## [13] eulerr_6.1.0 edgeR_3.34.0
## [15] limma_3.48.1 DESeq2_1.32.0
## [17] SummarizedExperiment_1.22.0 Biobase_2.52.0
## [19] MatrixGenerics_1.4.0 matrixStats_0.59.0
## [21] GenomicRanges_1.44.0 GenomeInfoDb_1.28.1
## [23] IRanges_2.26.0 S4Vectors_0.30.0
## [25] BiocGenerics_0.38.0 mitch_1.4.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 ellipsis_0.3.2 rprojroot_2.0.2
## [4] XVector_0.32.0 farver_2.1.0 bit64_4.0.5
## [7] AnnotationDbi_1.54.1 fansi_0.5.0 splines_4.1.0
## [10] cachem_1.0.5 geneplotter_1.70.0 knitr_1.33
## [13] polyclip_1.10-0 jsonlite_1.7.2 annotate_1.70.0
## [16] png_0.1-7 shiny_1.6.0 compiler_4.1.0
## [19] httr_1.4.2 assertthat_0.2.1 Matrix_1.3-4
## [22] fastmap_1.1.0 later_1.2.0 prettyunits_1.1.1
## [25] htmltools_0.5.1.1 tools_4.1.0 gtable_0.3.0
## [28] glue_1.4.2 GenomeInfoDbData_1.2.6 Rcpp_1.0.7
## [31] jquerylib_0.1.4 vctrs_0.3.8 Biostrings_2.60.1
## [34] xfun_0.24 polylabelr_0.2.0 stringr_1.4.0
## [37] mime_0.11 lifecycle_1.0.0 XML_3.99-0.6
## [40] zlibbioc_1.38.0 MASS_7.3-54 scales_1.1.1
## [43] hms_1.1.0 promises_1.2.0.1 RColorBrewer_1.1-2
## [46] yaml_2.2.1 memoise_2.0.0 gridExtra_2.3
## [49] sass_0.4.0 reshape_0.8.8 stringi_1.7.3
## [52] RSQLite_2.2.7 highr_0.9 genefilter_1.74.0
## [55] desc_1.3.0 caTools_1.18.2 BiocParallel_1.26.1
## [58] rlang_0.4.11 pkgconfig_2.0.3 bitops_1.0-7
## [61] evaluate_0.14 lattice_0.20-44 purrr_0.3.4
## [64] htmlwidgets_1.5.3 labeling_0.4.2 bit_4.0.4
## [67] tidyselect_1.1.1 plyr_1.8.6 magrittr_2.0.1
## [70] R6_2.5.0 generics_0.1.0 DelayedArray_0.18.0
## [73] DBI_1.1.1 pillar_1.6.1 withr_2.4.2
## [76] survival_3.2-11 KEGGREST_1.32.0 RCurl_1.98-1.3
## [79] crayon_1.4.1 htm2txt_2.1.1 KernSmooth_2.23-20
## [82] utf8_1.2.2 rmarkdown_2.9 progress_1.2.2
## [85] locfit_1.5-9.4 grid_4.1.0 blob_1.2.2
## [88] digest_0.6.27 xtable_1.8-4 httpuv_1.6.1
## [91] munsell_0.5.0 bslib_0.2.5.1