Multi-contrast enrichment analysis of DUX data
Mark Ziemann
2021-07-30
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")
genesets <- genesets[which(lapply(genesets,length)>=100)]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,minsetsize = 100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(fshdmres$enrichment_result,20)## set setSize
## 1044 38664_ESRRA_LoVo_None_GSM1208615 165
## 7760 81449_FAM208A_K562_Leukemia_GSM2509472 132
## 7899 82375_SETBP1_HEK_None_GSM2300424 129
## 8904 88351_ZFX_C4-2B_Epithelium_GSM2741769 695
## 8133 83846_FAM208A_K562_Leukemia_GSM2509471 130
## 3868 53078_RBP2_U937_None_GSM700539 770
## 3870 53077_RBP2_U937_None_GSM700538 775
## 3871 53075_RBP2_U937_None_GSM700536 780
## 8326 84931_ZFX_C4-2B_Epithelium_GSM2741768 695
## 8355 84928_ZFX_MCF-7_Epithelium_GSM2741763 689
## 8941 88784_INO80_HepG2_Epithelium_GSM2877257 703
## 8132 83841_FAM208A_K562_Leukemia_GSM2509474 125
## 1075 38722_DLX4_LoVo_None_GSM1208725 430
## 7231 74427_ZSCAN5A_BeWo_None_GSM2255829 182
## 6298 68635_KMT2D_None_Patientderivedxenograftcells_GSM1846892 746
## 7088 73846_KMT2D_MCF-7_None_GSM2264622 720
## 3429 50150_PR_C4-2_Epithelium_GSM1340252 679
## 7767 81162_ZFX_None_None_GSM2741776 700
## 6293 68632_KMT2D_None_Patientderivedxenograftcells_GSM1846896 748
## 1986 45421_RBBP5_K562_Erythroblast_GSM1003449 721
## pANOVA s.dist p.adjustANOVA
## 1044 3.283508e-25 -0.4682067 5.047205e-24
## 7760 5.714923e-15 -0.3939809 3.196885e-14
## 7899 1.008826e-13 -0.3796348 5.001447e-13
## 8904 2.258882e-63 -0.3758650 5.095474e-60
## 8133 1.938470e-13 -0.3737839 9.403663e-13
## 3868 7.436710e-65 -0.3623740 2.236714e-61
## 3870 3.130987e-65 -0.3623374 1.412545e-61
## 3871 1.811774e-65 -0.3619135 1.412545e-61
## 8326 1.871569e-58 -0.3606828 2.110896e-55
## 8355 1.114965e-57 -0.3597028 9.145754e-55
## 8941 1.137905e-58 -0.3594129 1.466760e-55
## 8132 4.200151e-12 -0.3592466 1.827288e-11
## 1075 5.452858e-37 -0.3584598 2.860531e-35
## 7231 1.142615e-16 -0.3567090 7.401161e-16
## 6298 5.269956e-61 -0.3565157 9.510163e-58
## 7088 6.166908e-58 -0.3530985 6.182668e-55
## 3429 8.544028e-55 -0.3528137 4.057514e-52
## 7767 7.685565e-56 -0.3511480 5.590993e-53
## 6293 7.265266e-58 -0.3465850 6.555450e-55
## 1986 8.055294e-56 -0.3462129 5.590993e-53fsdhm_up <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
fshdm_dn <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
hmm <- mitch_import( h,DEtype="edger",geneTable=gth )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15664## Note: no. genes in output = 12281## Note: estimated proportion of input genes in output = 0.784hres <- mitch_calc(x=hmm,genesets=genesets,priority="effect",resrows=100,cores=CORES,minsetsize = 100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(hres$enrichment_result,20)## set setSize pANOVA
## 7545 76967_MBOAT4_HeLa_Epithelium_GSM1587487 666 4.543936e-57
## 3907 53336_POLR2A_HeLa_Epithelium_GSM1482893 672 9.423464e-57
## 3908 53332_POLR2A_HeLa_Epithelium_GSM1482889 686 1.120327e-57
## 2102 45663_POLR2A_GM12891_Lymphoblastoid_GSM935287 665 7.077595e-54
## 6480 69813_NELFE_MCF-7_HumanBreastCancerCell_GSM2151647 694 7.679807e-56
## 3089 48692_H3F3B_None_Spermatid_GSM1375207 614 5.672661e-49
## 815 36351_POLR2A_A549_Epithelium_GSM874634 588 1.535514e-46
## 6491 69816_NELFE_MCF-7_HumanBreastCancerCell_GSM2151644 689 2.518190e-53
## 3926 53328_POLR2A_HeLa_Epithelium_GSM1482885 699 3.703150e-53
## 8931 88924_BRD4_J-LatA72_None_GSM2676802 595 2.161674e-45
## 6482 69814_NELFE_MCF-7_HumanBreastCancerCell_GSM2151646 700 3.640088e-52
## 3918 53334_POLR2A_HeLa_Epithelium_GSM1482891 684 7.255003e-51
## 6487 69815_NELFE_MCF-7_HumanBreastCancerCell_GSM2151645 693 2.537354e-51
## 8106 83889_SIRT1_K562_Leukemia_GSM2474868 118 2.324928e-10
## 3852 53077_RBP2_U937_None_GSM700538 765 4.095260e-55
## 7753 81575_POLR2A_None_ProgenitorCells_GSM2550238 715 2.329858e-51
## 6412 69533_POLR2A_Nalm6_LeukemiaCell_GSM2144899 609 3.053270e-44
## 206 2381_POLR2A_MCF-7_Epithelium_GSM451989 254 1.283871e-19
## 8652 87440_POLR2A_None_ProgenitorCells_GSM2550196 677 5.422893e-48
## 3850 53078_RBP2_U937_None_GSM700539 760 2.827429e-53
## s.dist p.adjustANOVA
## 7545 0.3644027 2.042272e-53
## 3907 0.3618351 2.823584e-53
## 3908 0.3613125 1.007062e-53
## 2102 0.3540764 1.060342e-50
## 6480 0.3534541 1.725845e-52
## 3089 0.3501058 3.186972e-46
## 815 0.3481129 6.901366e-44
## 6491 0.3463850 3.176970e-50
## 3926 0.3434925 3.698624e-50
## 8931 0.3417308 7.772515e-43
## 6482 0.3399605 3.272076e-49
## 3918 0.3392555 5.016555e-48
## 6487 0.3387255 1.900690e-48
## 8106 -0.3382584 8.369553e-10
## 3852 0.3354324 7.362459e-52
## 7753 0.3339143 1.900690e-48
## 6412 0.3335345 8.576827e-42
## 206 0.3312003 1.251705e-18
## 8652 0.3308613 2.708133e-45
## 3850 0.3306632 3.176970e-50hres_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,minsetsize = 100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(jres$enrichment_result,20)## set setSize pANOVA s.dist
## 5393 63740_E2F4_MCF-7_Epithelium_ENCSR505NMN_2 360 6.597810e-20 0.2814857
## 5392 63739_E2F4_MCF-7_Epithelium_ENCSR505NMN_1 451 7.259145e-22 0.2655142
## 7734 81450_FAM208A_K562_Leukemia_GSM2509473 149 4.664879e-08 -0.2598058
## 8108 83846_FAM208A_K562_Leukemia_GSM2509471 132 9.918546e-07 -0.2470759
## 4031 54028_ESR1_MCF-7_Epithelium_GSM1669084 129 1.428028e-06 -0.2462192
## 1042 38664_ESRRA_LoVo_None_GSM1208615 168 4.179873e-08 -0.2457273
## 2060 45617_E2F4_GM12878_BLymphocyte_GSM935330 711 2.196709e-28 0.2455973
## 7736 81449_FAM208A_K562_Leukemia_GSM2509472 133 1.604459e-06 -0.2413560
## 7735 81429_FAM208A_K562_Leukemia_GSM2509478 246 8.551569e-11 -0.2410657
## 1044 38672_SPIB_LoVo_None_GSM1208797 111 1.485496e-05 -0.2382952
## 8230 8447_SMAD1_U937_None_GSM722425 478 3.531984e-17 0.2265124
## 1108 38800_RORA_LoVo_None_GSM1208791 231 3.734856e-09 -0.2258611
## 4054 54052_ESR1_T47D_Epithelium_GSM1669108 117 2.641529e-05 -0.2252772
## 8930 88936_SPI1_None_Macrophage_GSM2592810 209 2.927065e-08 0.2231861
## 8107 83841_FAM208A_K562_Leukemia_GSM2509474 129 1.360290e-05 -0.2221893
## 56 1307_NR3C1_HeLa_Epithelium_GSM604648 110 6.849231e-05 -0.2200695
## 8103 83843_FAM208A_K562_Leukemia_GSM2509476 171 1.221553e-06 -0.2155870
## 4829 59007_LYL1_THP-1_Monocyte_GSM1901550 295 2.359737e-10 0.2153175
## 4052 54048_ESR1_T47D_Epithelium_GSM1669104 150 6.075775e-06 -0.2144413
## 2826 46364_YY1_SK-N-SH_RA_Neuron_GSM803498 750 2.025328e-22 -0.2112430
## p.adjustANOVA
## 5393 3.955607e-17
## 5392 2.176050e-18
## 7734 7.146721e-07
## 8108 1.092823e-05
## 4031 1.512633e-05
## 1042 6.469810e-07
## 2060 1.975501e-24
## 7736 1.681690e-05
## 7735 3.113533e-09
## 1044 1.148409e-04
## 8230 9.342098e-15
## 1108 7.792937e-08
## 4054 1.888336e-04
## 8930 4.708961e-07
## 8107 1.065600e-04
## 56 4.402798e-04
## 8103 1.306791e-05
## 4829 7.193598e-09
## 4052 5.346326e-05
## 2826 9.106886e-19jres_up <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
jres_dn <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
msm <- mitch_import( ms,DEtype="edger",geneTable=gtm )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 12752## Note: no. genes in output = 11830## Note: estimated proportion of input genes in output = 0.928mres <- mitch_calc(x=msm,genesets=genesets,priority="effect",resrows=100,cores=CORES,minsetsize = 100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres$enrichment_result,20)## set setSize pANOVA s.dist
## 7960 83201_POLR2A_TIME_Fibroblast_GSM2218627 149 4.904010e-18 0.4113897
## 8605 87448_POLR2A_TIME_Fibroblast_GSM2218637 659 1.255504e-48 0.3376758
## 8614 87454_POLR2A_TIME_Fibroblast_GSM2218639 678 1.858200e-45 0.3218238
## 8618 87450_POLR2A_TIME_Fibroblast_GSM2218631 597 1.126983e-39 0.3184946
## 4078 54255_NR3C2_None_None_GSM1708661 258 8.433259e-18 0.3118588
## 7121 74195_POLR2A_None_None_GSM2265658 594 2.339698e-35 0.3006757
## 8622 87452_POLR2A_TIME_Fibroblast_GSM2218633 601 2.197308e-35 0.2991331
## 7978 83207_POLR2A_TIME_Fibroblast_GSM2218621 593 2.533378e-34 0.2962841
## 8727 87942_GLI2_THP-1_LeukemiaCell_GSM2779054 588 5.944523e-34 0.2957919
## 3861 53125_POLR2A_MRC5_Fibroblast_GSM1330729 655 1.194521e-36 0.2925370
## 7125 74207_BRD4_SF8628_None_GSM2265638 579 2.455706e-32 0.2904489
## 2279 45854_POLR2A_None_EndothelialCell_GSM935393 594 6.489219e-33 0.2896266
## 1596 42905_POLR2A_AC16_Cardiomyocyte_GSM1240002 618 4.451965e-34 0.2894672
## 8711 87941_GLI2_THP-1_LeukemiaCell_GSM2779055 573 1.458201e-31 0.2882023
## 8341 85515_POLR2A_None_Fibroblast_GSM2151919 212 5.731362e-13 0.2880749
## 3389 50112_MAFB_None_Keratinocyte_GSM1278507 588 4.358086e-32 0.2871657
## 4079 54256_NR3C2_None_None_GSM1708662 181 3.259960e-11 0.2866729
## 3927 53637_STAT5B_None_TLymphocyte_GSM1577754 452 4.327673e-25 0.2858900
## 7772 81969_POLR2A_None_Fibroblast_GSM2151920 455 1.006783e-24 0.2827577
## 6378 69457_TP53_None_None_GSM2046276 387 3.094277e-21 0.2817730
## p.adjustANOVA
## 7960 1.859160e-16
## 8605 1.123300e-44
## 8614 8.312659e-42
## 8618 3.361040e-36
## 4078 3.092310e-16
## 7121 3.488879e-32
## 8622 3.488879e-32
## 7978 3.238018e-31
## 8727 5.909516e-31
## 3861 2.671845e-33
## 7125 1.830934e-29
## 2279 5.278095e-30
## 1596 4.978966e-31
## 8711 8.697683e-29
## 8341 1.025702e-11
## 3389 2.993109e-29
## 4079 4.666698e-10
## 3927 6.245112e-23
## 7772 1.364800e-22
## 6378 2.197183e-19mres_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,minsetsize = 50)## 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$set[1:nrow(x)]
colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(as.matrix(x),col=colfunc(30),margins=c(5,25),trace="none",
scale="none",cexCol=0.6,cexRow=0.25)
unlink("mitch_plots_simple_eff.html")
capture.output(
mitch_report(res1,"mitch_plots_simple_eff.html")
,file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/Rtmp5MYi22/mitch_plots_simple_eff.rds ".##
##
## processing file: mitch.Rmd##
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output file: /mnt/bfx6/bfx/kevinwatt/dux_mitch/mitch.knit.md
##
##
## Output created: /tmp/Rtmp5MYi22/mitch_report.html
# significance
res3 <- mitch_calc(x=mg,genesets=genesets,priority="significance",resrows=100,cores=CORES,minsetsize = 50)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.x <- res3$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