Multi-contrast enrichment analysis of DUX data
Mark Ziemann
2020-12-03
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")
})Prepare 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("ReactomePathways.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)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(fshdmres$enrichment_result,20)## set setSize pANOVA
## 190 Classical antibody-mediated complement activation 10 1.129498e-07
## 357 FCGR activation 17 1.415620e-07
## 937 Regulation of Complement cascade 33 5.533471e-13
## 185 Citric acid cycle (TCA cycle) 21 1.134951e-08
## 1041 Scavenging of heme from plasma 10 1.066934e-04
## 1347 tRNA processing in the mitochondrion 16 1.130006e-06
## 1184 TNFs bind their physiological receptors 20 6.736202e-08
## 1341 rRNA processing in the mitochondrion 16 1.937363e-06
## 520 Initial triggering of complement 21 5.834797e-08
## 220 Crosslinking of collagen fibrils 10 2.218697e-04
## 657 Mitochondrial tRNA aminoacylation 18 1.008319e-06
## 572 Keratan sulfate degradation 12 6.724612e-05
## 654 Mitochondrial calcium ion transport 21 1.902720e-07
## 542 Interleukin-10 signaling 31 4.053262e-10
## 297 Dissolution of Fibrin Clot 10 4.026251e-04
## 1344 tRNA Aminoacylation 24 4.389097e-08
## 847 Processing of SMDT1 15 1.560788e-05
## 116 Branched-chain amino acid catabolism 21 3.625843e-07
## 460 Glyoxylate metabolism and glycine degradation 25 6.276013e-08
## 217 Creation of C4 and C2 activators 14 5.878801e-05
## s.dist p.adjustANOVA
## 190 0.9684187 1.752669e-06
## 357 0.7371498 2.171690e-06
## 937 0.7250377 2.490062e-11
## 185 -0.7194846 2.286841e-07
## 1041 0.7075760 8.891116e-04
## 1347 -0.7027394 1.481076e-05
## 1184 0.6970908 1.109009e-06
## 1341 -0.6872062 2.444337e-05
## 520 0.6835543 1.009869e-06
## 220 0.6743356 1.583039e-03
## 657 -0.6656546 1.334540e-05
## 572 0.6644640 5.933481e-04
## 654 -0.6564613 2.822716e-06
## 542 0.6486311 1.094381e-08
## 297 0.6461606 2.600688e-03
## 1344 -0.6454414 7.796422e-07
## 847 -0.6441703 1.646144e-04
## 116 -0.6412286 4.994784e-06
## 460 -0.6250753 1.046002e-06
## 217 0.6201246 5.326430e-04fsdhm_up <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
fshdm_dn <- subset(fshdmres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
hmm <- mitch_import( h,DEtype="edger",geneTable=gth )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 15664## Note: no. genes in output = 12281## Note: estimated proportion of input genes in output = 0.784hres <- mitch_calc(x=hmm,genesets=genesets,priority="effect",resrows=100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(hres$enrichment_result,20)## set
## 1090 Striated Muscle Contraction
## 1277 rRNA modification in the nucleus and cytosol
## 190 Condensation of Prometaphase Chromosomes
## 883 Reduction of cytosolic Ca++ levels
## 426 Glycogen breakdown (glycogenolysis)
## 418 Gluconeogenesis
## 599 Metabolism of non-coding RNA
## 1281 snRNP Assembly
## 763 Phase 0 - rapid depolarisation
## 1214 Transport of the SLBP Dependant Mature mRNA
## 1215 Transport of the SLBP independent Mature mRNA
## 984 SUMOylation of SUMOylation proteins
## 327 Export of Viral Ribonucleoproteins from Nucleus
## 787 Postmitotic nuclear pore complex (NPC) reformation
## 1210 Transport of Ribonucleoproteins into the Host Nucleus
## 989 SUMOylation of ubiquitinylation proteins
## 1262 mRNA Splicing - Major Pathway
## 704 Nuclear import of Rev protein
## 243 Defective TPR may confer susceptibility towards thyroid papillary carcinoma (TPC)
## 890 Regulation of Glucokinase by Glucokinase Regulatory Protein
## setSize pANOVA s.dist p.adjustANOVA
## 1090 31 4.539093e-09 -0.6083950 2.165315e-07
## 1277 53 7.425697e-14 0.5938798 1.062700e-11
## 190 10 1.282174e-03 0.5880531 1.065446e-02
## 883 11 1.095227e-03 -0.5685263 9.796196e-03
## 426 14 4.319330e-04 -0.5433393 4.486529e-03
## 418 23 6.980144e-06 -0.5413891 1.231565e-04
## 599 47 1.475521e-10 0.5402801 8.638504e-09
## 1281 47 1.475521e-10 0.5402801 8.638504e-09
## 763 23 1.099712e-05 -0.5296275 1.792948e-04
## 1214 30 6.365834e-07 0.5253449 1.639839e-05
## 1215 29 1.058392e-06 0.5236583 2.524462e-05
## 984 28 2.905098e-06 0.5107554 6.028374e-05
## 327 27 4.616330e-06 0.5094512 8.617149e-05
## 787 25 1.053178e-05 0.5091253 1.761679e-04
## 1210 26 7.857786e-06 0.5063993 1.349444e-04
## 989 32 7.954567e-07 0.5042861 1.970285e-05
## 1262 164 1.358912e-28 0.5022695 4.375696e-26
## 704 27 6.626712e-06 0.5009883 1.185445e-04
## 243 24 2.228722e-05 0.5001224 3.299533e-04
## 890 24 2.228722e-05 0.5001224 3.299533e-04hres_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)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(jres$enrichment_result,20)## set
## 1206 Trafficking and processing of endosomal TLR
## 187 Classical antibody-mediated complement activation
## 263 Degradation of cysteine and homocysteine
## 376 Formation of Senescence-Associated Heterochromatin Foci (SAHF)
## 533 Interleukin-10 signaling
## 113 Branched-chain amino acid catabolism
## 182 Citric acid cycle (TCA cycle)
## 498 Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell
## 199 Complex I biogenesis
## 214 Creation of C4 and C2 activators
## 349 FCGR activation
## 1034 Signal regulatory protein family interactions
## 827 Prolactin receptor signaling
## 201 Condensation of Prophase Chromosomes
## 657 Mucopolysaccharidoses
## 660 MyD88 deficiency (TLR2/4)
## 491 IRAK4 deficiency (TLR2/4)
## 752 PECAM1 interactions
## 504 Inflammasomes
## 281 Diseases associated with the TLR signaling cascade
## setSize pANOVA s.dist p.adjustANOVA
## 1206 11 9.681194e-06 0.7702612 1.908785e-04
## 187 10 8.951275e-05 0.7153487 1.343709e-03
## 263 13 3.848691e-05 -0.6593287 6.518103e-04
## 376 11 1.776598e-04 0.6527523 2.336580e-03
## 533 25 3.568403e-08 0.6366586 1.240489e-06
## 113 21 8.436518e-07 -0.6207651 2.274416e-05
## 182 22 5.878132e-07 -0.6151520 1.704638e-05
## 498 49 1.129784e-13 0.6129571 1.356768e-11
## 199 54 1.184067e-14 -0.6069964 1.955191e-12
## 214 11 5.148383e-04 0.6047326 5.574602e-03
## 349 16 3.446502e-05 0.5980426 5.912766e-04
## 1034 13 1.988778e-04 0.5959457 2.478467e-03
## 827 11 8.027637e-04 -0.5836594 8.284773e-03
## 201 13 2.983682e-04 0.5793395 3.550850e-03
## 657 11 9.879101e-04 0.5735828 9.739024e-03
## 660 10 1.796502e-03 0.5701588 1.474024e-02
## 491 11 1.063321e-03 0.5699739 1.032829e-02
## 752 10 2.106377e-03 0.5615504 1.676220e-02
## 504 19 2.460304e-05 0.5590163 4.577551e-04
## 281 22 5.803003e-06 0.5583373 1.197776e-04jres_up <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist > 0)$set
jres_dn <- subset(jres$enrichment_result,p.adjustANOVA<0.02 & s.dist < 0)$set
msm <- mitch_import( ms,DEtype="edger",geneTable=gtm )## The input is a single dataframe; one contrast only. Converting
## it to a list for you.## Note: Mean no. genes in input = 12752## Note: no. genes in output = 11830## Note: estimated proportion of input genes in output = 0.928mres <- mitch_calc(x=msm,genesets=genesets,priority="effect",resrows=100)## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres$enrichment_result,20)## set
## 509 Interleukin-10 signaling
## 365 Formation of tubulin folding intermediates by CCT/TriC
## 919 Regulation of pyruvate dehydrogenase (PDH) complex
## 172 Citric acid cycle (TCA cycle)
## 139 Carnitine metabolism
## 994 Signal regulatory protein family interactions
## 612 Mitochondrial tRNA aminoacylation
## 805 Pyruvate metabolism and Citric Acid (TCA) cycle
## 1081 Suppression of phagosomal maturation
## 792 Prolactin receptor signaling
## 607 Mitochondrial Fatty Acid Beta-Oxidation
## 771 Post-chaperonin tubulin folding pathway
## 804 Pyruvate metabolism
## 40 Activation of IRF3/IRF7 mediated by TBK1/IKK epsilon
## 1255 p130Cas linkage to MAPK signaling for integrins
## 107 Branched-chain amino acid catabolism
## 1127 TP53 Regulates Transcription of Genes Involved in G1 Cell Cycle Arrest
## 781 Prefoldin mediated transfer of substrate to CCT/TriC
## 456 Heme biosynthesis
## 20 ATF6 (ATF6-alpha) activates chaperone genes
## setSize pANOVA s.dist p.adjustANOVA
## 509 20 1.382222e-07 0.6802921 3.458265e-06
## 365 18 1.820714e-06 0.6496877 3.416517e-05
## 919 15 2.308141e-05 -0.6312146 3.201291e-04
## 172 22 1.505012e-06 -0.5924643 2.866262e-05
## 139 12 4.373628e-04 -0.5862808 3.875520e-03
## 994 13 3.100744e-04 0.5777596 3.020267e-03
## 612 18 3.482304e-05 -0.5635841 4.628562e-04
## 805 50 8.724571e-12 -0.5580407 7.951823e-10
## 1081 11 1.579315e-03 0.5502311 1.071918e-02
## 792 10 3.383627e-03 -0.5352284 1.880808e-02
## 607 31 2.576502e-07 -0.5347009 5.977485e-06
## 771 16 2.523095e-04 0.5285255 2.515211e-03
## 804 26 4.140198e-06 -0.5217201 6.860900e-05
## 40 12 1.893510e-03 0.5179528 1.208059e-02
## 1255 12 2.130470e-03 0.5121143 1.322141e-02
## 107 21 5.657195e-05 -0.5076435 6.940943e-04
## 1127 13 1.553765e-03 0.5069424 1.060216e-02
## 781 23 3.182964e-05 0.5011581 4.275223e-04
## 456 13 1.798192e-03 -0.5000944 1.158835e-02
## 20 10 6.303225e-03 0.4989002 3.031363e-02mres_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)## 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$set[1:nrow(x)]
colfunc <- colorRampPalette(c("blue","white", "red"))
heatmap.2(as.matrix(x),col=colfunc(30),margins=c(5,25),trace="none",
scale="none",cexCol=0.6,cexRow=0.25)
unlink("mitch_plots_simple_eff.html")
capture.output(
mitch_report(res1,"mitch_plots_simple_eff.html")
,file = "/dev/null", append = FALSE,
type = c("output", "message"), split = FALSE)## Dataset saved as " /tmp/RtmpAJoNko/mitch_plots_simple_eff.rds ".##
##
## processing file: mitch.Rmd##
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output file: /mnt/bfx6/bfx/kevinwatt/dux_mitch/mitch.knit.md
##
##
## Output created: /tmp/RtmpAJoNko/mitch_report.html
# significance
res3 <- mitch_calc(x=mg,genesets=genesets,priority="significance",resrows=100)## Note: When prioritising by significance (ie: small
## p-values), large effect sizes might be missed.x <- res3$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)## 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 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## 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_1.1.0 GGally_2.0.0
## [3] ggplot2_3.3.2 reshape2_1.4.4
## [5] beeswarm_0.2.3 gtools_3.8.2
## [7] tibble_3.0.4 dplyr_1.0.2
## [9] echarts4r_0.3.3 getDEE2_1.0.0
## [11] gplots_3.1.0 UpSetR_1.4.0
## [13] eulerr_6.1.0 edgeR_3.32.0
## [15] limma_3.46.0 DESeq2_1.30.0
## [17] SummarizedExperiment_1.20.0 Biobase_2.50.0
## [19] MatrixGenerics_1.2.0 matrixStats_0.57.0
## [21] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0
## [23] IRanges_2.24.0 S4Vectors_0.28.0
## [25] BiocGenerics_0.36.0 mitch_1.2.2
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 bit64_4.0.5 progress_1.2.2
## [4] RColorBrewer_1.1-2 httr_1.4.2 rprojroot_1.3-2
## [7] backports_1.2.0 tools_4.0.3 R6_2.5.0
## [10] KernSmooth_2.23-18 DBI_1.1.0 colorspace_2.0-0
## [13] withr_2.3.0 htm2txt_2.1.1 prettyunits_1.1.1
## [16] tidyselect_1.1.0 gridExtra_2.3 bit_4.0.4
## [19] compiler_4.0.3 desc_1.2.0 DelayedArray_0.16.0
## [22] labeling_0.4.2 caTools_1.18.0 scales_1.1.1
## [25] genefilter_1.72.0 stringr_1.4.0 digest_0.6.27
## [28] rmarkdown_2.5 XVector_0.30.0 pkgconfig_2.0.3
## [31] htmltools_0.5.0 highr_0.8 fastmap_1.0.1
## [34] htmlwidgets_1.5.2 rlang_0.4.8 RSQLite_2.2.1
## [37] shiny_1.5.0 farver_2.0.3 generics_0.1.0
## [40] jsonlite_1.7.1 BiocParallel_1.24.1 RCurl_1.98-1.2
## [43] magrittr_1.5 GenomeInfoDbData_1.2.4 Matrix_1.2-18
## [46] Rcpp_1.0.5 munsell_0.5.0 lifecycle_0.2.0
## [49] stringi_1.5.3 yaml_2.2.1 MASS_7.3-53
## [52] zlibbioc_1.36.0 plyr_1.8.6 grid_4.0.3
## [55] blob_1.2.1 promises_1.1.1 crayon_1.3.4
## [58] lattice_0.20-41 splines_4.0.3 annotate_1.68.0
## [61] hms_0.5.3 polylabelr_0.2.0 locfit_1.5-9.4
## [64] knitr_1.30 pillar_1.4.6 geneplotter_1.68.0
## [67] XML_3.99-0.5 glue_1.4.2 evaluate_0.14
## [70] vctrs_0.3.4 httpuv_1.5.4 testthat_3.0.0
## [73] gtable_0.3.0 purrr_0.3.4 polyclip_1.10-0
## [76] assertthat_0.2.1 reshape_0.8.8 xfun_0.19
## [79] mime_0.9 xtable_1.8-4 later_1.1.0.1
## [82] survival_3.2-7 AnnotationDbi_1.52.0 memoise_1.1.0
## [85] ellipsis_0.3.1