GSE74548: Gene Methylation Enrichment Analysis - accounting for correlation
hcy_meth meta-analysis group
2022-08-04
Introduction
Previously, Mandhri and I analysed the B-PROOF 450K data, trying to understand whether vitamin supplementation caused changes in gene methylation. We used Limma and some basic analyses, which showed no specific probes with FDR<0.05, nor any DMRs.
In this analysis we will use the principle of Gene Set Enrichment Analysis, applying it to many probes belonging to genes. If the probes are trending in concert, then we can make some judgement about the enrichment of those probes. The statistical test used is the CAMERA test, which is a competitive test that attempts to account for correlation between genes in a set, or in this case probes belonging to a gene.
library("parallel")
library("dplyr")
library("kableExtra")
library("eulerr")
library("mitch")
library("limma")
library("IlluminaHumanMethylation450kmanifest")
library("IlluminaHumanMethylation450kanno.ilmn12.hg19")
library("tictoc")Get array annotation data
CORES=detectCores()
ann450k <- getAnnotation(IlluminaHumanMethylation450kanno.ilmn12.hg19)
myann <- data.frame(ann450k[,c("UCSC_RefGene_Name","Regulatory_Feature_Group")])
promoters <- grep("Prom",myann$Regulatory_Feature_Group)
# make a list of probes that belong to a gene
gn <- unique(unlist(strsplit( myann$UCSC_RefGene_Name ,";")))
gnl <- strsplit( myann$UCSC_RefGene_Name ,";")
gnl <- mclapply(gnl,unique,mc.cores=CORES)
myann$UCSC_RefGene_Name <- gnl
gnl <- gnl[which(lapply(gnl,length)>0)]
if ( ! file.exists("sets.Rds") ) {
l <- mclapply(1:nrow(myann), function(i) {
a <- myann[i,]
len <- length(a[[1]][[1]])
probe <- rep(rownames(a),len)
genes <- a[[1]][[1]]
data.frame(genes,probe)
},mc.cores=CORES)
df <- do.call(rbind,l)
sets <- mclapply(X=gn, FUN=function(g) {
df[which(df$genes == g),2]
} , mc.cores=CORES/2)
names(sets) <- gn
saveRDS(object=sets,file="sets.Rds")
} else {
sets <- readRDS("sets.Rds")
}DM3 - association of Hcy with gene methylation
It is thought that high plasma homocysteine can inhibit DNA methylation. Let’s see whether that is the case, and which genes are most affected. This analysis is conducted at the whole gene level as well as on the level of promoters.
Read in and inspect the data
dm <- read.table("dma3a.tsv.gz")
dm <- dm[,4:9]
dm <- merge(myann,dm,by=0)
rownames(dm) <- dm[,1]
dm[,1] = NULL
# testing
#dm <- head(dm,40000)
head(dm,50) %>%
kbl(caption = "Top significant genes with limma") %>%
kable_paper("hover", full_width = F)| UCSC_RefGene_Name | Regulatory_Feature_Group | logFC | AveExpr | t | P.Value | adj.P.Val | B | |
|---|---|---|---|---|---|---|---|---|
| cg00000029 | RBL2 | Promoter_Associated | -0.0446959 | 0.4522209 | -0.8638867 | 0.3888420 | 0.9098372 | -5.265779 |
| cg00000108 | C3orf35 | -0.0122602 | 3.6607954 | -0.1654468 | 0.8687848 | 0.9886500 | -5.576980 | |
| cg00000109 | FNDC3B | -0.0378447 | 2.0296336 | -0.5100334 | 0.6106758 | 0.9574373 | -5.476065 | |
| cg00000165 | -0.0277057 | -1.0289408 | -0.5477061 | 0.5845972 | 0.9528277 | -5.458804 | ||
| cg00000236 | VDAC3 | -0.0308775 | 1.8879879 | -0.6273277 | 0.5312685 | 0.9421655 | -5.418295 | |
| cg00000289 | ACTN1 | -0.0347776 | 0.2694825 | -0.5508856 | 0.5824203 | 0.9524415 | -5.457291 | |
| cg00000292 | ATP2A1 | 0.0078848 | 2.8508680 | 0.1712753 | 0.8642068 | 0.9883139 | -5.576129 | |
| cg00000321 | SFRP1 | -0.0006141 | -0.8769170 | -0.0091726 | 0.9926919 | 0.9994500 | -5.588821 | |
| cg00000363 | -0.0207565 | -0.2718208 | -0.3794289 | 0.7048330 | 0.9708901 | -5.526411 | ||
| cg00000622 | NIPA2 | Promoter_Associated | 0.0333418 | -4.8495004 | 0.7574773 | 0.4497908 | 0.9250804 | -5.340328 |
| cg00000658 | MAN1B1 | 0.0226951 | 2.0750918 | 0.7385729 | 0.4611636 | 0.9276129 | -5.352556 | |
| cg00000714 | TSEN34 | -0.0767442 | -1.5409438 | -2.5167547 | 0.0127504 | 0.4684043 | -2.895740 | |
| cg00000721 | LRRC16A | Unclassified_Cell_type_specific | -0.0031114 | 2.7846882 | -0.0385133 | 0.9693227 | 0.9977628 | -5.588214 |
| cg00000734 | CNBP | Unclassified | -0.0224123 | -3.0864719 | -0.8730215 | 0.3838572 | 0.9082510 | -5.258927 |
| cg00000769 | DDX55 | Promoter_Associated | 0.0044715 | -3.1463101 | 0.1951579 | 0.8454975 | 0.9862198 | -5.572331 |
| cg00000884 | TLR2 | -0.0355966 | 2.4344668 | -0.6542490 | 0.5138171 | 0.9388729 | -5.403363 | |
| cg00000905 | FAM81A | Unclassified | 0.0269914 | -2.1171455 | 0.5264592 | 0.5992411 | 0.9555164 | -5.468689 |
| cg00000924 | KCNQ1 , KCNQ1OT1 | 0.0076655 | 0.4463612 | 0.2133270 | 0.8313222 | 0.9846206 | -5.569112 | |
| cg00000948 | -0.0512202 | 2.8596325 | -1.1837137 | 0.2381445 | 0.8535327 | -4.983571 | ||
| cg00000957 | NPHP4 | Unclassified_Cell_type_specific | 0.0113656 | 2.7283928 | 0.3477238 | 0.7284688 | 0.9734455 | -5.536407 |
| cg00001099 | PSKH2 | -0.0540892 | 2.2699920 | -0.7493275 | 0.4546739 | 0.9262433 | -5.345637 | |
| cg00001245 | MRPS25 | Promoter_Associated | 0.0127411 | -4.2195473 | 0.3178478 | 0.7509823 | 0.9762343 | -5.545030 |
| cg00001249 | 0.0878332 | 2.2493861 | 1.3410880 | 0.1816436 | 0.8210138 | -4.812929 | ||
| cg00001261 | 0.0441601 | -0.2075480 | 0.9709593 | 0.3329193 | 0.8918583 | -5.180991 | ||
| cg00001269 | 0.0390867 | 2.9907317 | 1.0911248 | 0.2767298 | 0.8718875 | -5.074204 | ||
| cg00001349 | MAEL | 0.0094591 | 1.9174464 | 0.1451613 | 0.8847519 | 0.9899344 | -5.579714 | |
| cg00001364 | PROX1 | -0.0859751 | 2.3099396 | -1.4045238 | 0.1619493 | 0.8064409 | -4.738269 | |
| cg00001446 | ELOVL1 | 0.0361831 | 2.9141672 | 0.8905659 | 0.3743945 | 0.9051405 | -5.245568 | |
| cg00001510 | LILRA6 | 0.0045678 | -0.0996494 | 0.1465904 | 0.8836254 | 0.9898532 | -5.579533 | |
| cg00001534 | FAF1 | -0.1186347 | 3.3033444 | -1.5710033 | 0.1180025 | 0.7613262 | -4.526384 | |
| cg00001582 | LOC283050, ZMIZ1 | 0.0248102 | -2.9750721 | 0.9292188 | 0.3540658 | 0.8980126 | -5.215208 | |
| cg00001583 | NR5A2 | -0.0441798 | -2.8591736 | -0.9330761 | 0.3520765 | 0.8973466 | -5.212108 | |
| cg00001594 | ROCK2 | 0.0042517 | -4.7593997 | 0.0853653 | 0.9320693 | 0.9944972 | -5.585695 | |
| cg00001687 | CDK10 | 0.0015161 | 5.0163253 | 0.0361013 | 0.9712431 | 0.9978621 | -5.588292 | |
| cg00001747 | Unclassified_Cell_type_specific | -0.1126870 | -2.7076297 | -1.5885362 | 0.1139845 | 0.7562820 | -4.502731 | |
| cg00001791 | TMEM182 | 0.0182578 | 2.9978670 | 0.2936189 | 0.7693998 | 0.9781258 | -5.551455 | |
| cg00001793 | ETV6 | -0.1098128 | 1.0746323 | -2.0295079 | 0.0439333 | 0.6192038 | -3.825126 | |
| cg00001809 | 0.0525778 | 1.7725728 | 1.1597328 | 0.2477500 | 0.8587519 | -5.007741 | ||
| cg00001854 | DNAJA2 | 0.0114339 | 2.8722017 | 0.2049001 | 0.8378902 | 0.9853220 | -5.570641 | |
| cg00001930 | 0.0632018 | 2.1698165 | 0.8056091 | 0.4215702 | 0.9186297 | -5.307809 | ||
| cg00002028 | PINK1 | Promoter_Associated | 0.0295245 | -3.5036247 | 0.8499858 | 0.3965035 | 0.9114511 | -5.276068 |
| cg00002033 | LRFN1 | 0.0542461 | 1.7907903 | 0.4372262 | 0.6624906 | 0.9647231 | -5.505950 | |
| cg00002080 | RWDD2B | Unclassified_Cell_type_specific | 0.0795838 | 2.1256473 | 1.4263346 | 0.1555674 | 0.8004050 | -4.711822 |
| cg00002116 | MRPL12 | Promoter_Associated | -0.0045132 | -5.0826553 | -0.0905479 | 0.9279562 | 0.9940693 | -5.585300 |
| cg00002145 | COL6A3 | 0.0863898 | 2.7662213 | 1.9901870 | 0.0481398 | 0.6319901 | -3.891945 | |
| cg00002190 | -0.0809549 | 2.2105512 | -1.6860704 | 0.0935784 | 0.7287342 | -4.366527 | ||
| cg00002224 | C8orf31 | Unclassified_Cell_type_specific | 0.0994866 | 0.1264213 | 1.3473757 | 0.1796150 | 0.8195515 | -4.805679 |
| cg00002236 | RTTN | Promoter_Associated | -0.0189864 | -3.5918761 | -0.6113878 | 0.5417426 | 0.9447402 | -5.426842 |
| cg00002406 | CD2BP2 | Promoter_Associated | 0.0073673 | -4.1952587 | 0.2710988 | 0.7866371 | 0.9798899 | -5.556971 |
| cg00002426 | SLMAP | 0.0357775 | 3.4533187 | 0.6786596 | 0.4982571 | 0.9355991 | -5.389284 |
# trim down the input dataset
dm <- dm[,c("UCSC_RefGene_Name","t")]
# histogram of t values
hist(dm$t,breaks=seq(from=-6,to=6,by=1))
Whole gene analysis
# set cores to used for parallel execution
CORES= detectCores()
calc_sc <- function(dm) {
gn <- unique(unlist(strsplit(unlist( dm$UCSC_RefGene_Name) ,", ")))
gnl <- strsplit( unlist(dm$UCSC_RefGene_Name) ,";")
gnl <- mclapply(gnl,unique,mc.cores=CORES)
l <- mclapply(1:nrow(dm), function(i) {
a <- dm[i,]
len <- length(a[[1]][[1]])
tvals <- as.numeric(rep(a[2],len))
genes <- a[[1]][[1]]
data.frame(genes,tvals)
},mc.cores=CORES)
df <- do.call(rbind,l)
gme_res <- mclapply( 1:length(gn), function(i) {
g <- gn[i]
tstats <- df[which(df$genes==g),"tvals"]
myn <- length(tstats)
mymean <- mean(tstats)
mymedian <- median(tstats)
wtselfcont <- wilcox.test(tstats)
res <- c("gene"=g,"nprobes"=myn,"mean"=mymean,"median"=mymedian,
"p-value(sc)"=wtselfcont$p.value)
} , mc.cores=CORES )
gme_res_df <- do.call(rbind, gme_res)
rownames(gme_res_df) <- gme_res_df[,1]
gme_res_df <- gme_res_df[,-1]
tmp <- apply(gme_res_df,2,as.numeric)
rownames(tmp) <- rownames(gme_res_df)
gme_res_df <- as.data.frame(tmp)
gme_res_df$sig <- -log10(gme_res_df[,4])
gme_res_df <- gme_res_df[order(-gme_res_df$sig),]
gme_res_df$`fdr(sc)` <- p.adjust(gme_res_df$`p-value(sc)`)
out <- list("df"=df,"gme_res_df"=gme_res_df)
return(out)
}
tic()
gme_res_wholegene <- calc_sc(dm)
time2 <- toc() #38 44 41 40 44 41 40## 51.175 sec elapsedtime2## $tic
## elapsed
## 3554.186
##
## $toc
## elapsed
## 3605.361
##
## $msg
## logical(0)df <- gme_res_wholegene[[1]]
res <- gme_res_wholegene[[2]]
write.table(res,file="gmea_wholegene.tsv")Table of top results.
head(res,50) %>%
kbl(caption = "Top significant genes with GMEA") %>%
kable_paper("hover", full_width = F)| nprobes | mean | median | p-value(sc) | sig | fdr(sc) | |
|---|---|---|---|---|---|---|
| TNXB | 531 | 0.3429080 | 0.4078607 | 0e+00 | 14.041475 | 0.0000000 |
| PCDHA1 | 162 | -0.7077910 | -0.6245086 | 0e+00 | 13.496797 | 0.0000000 |
| NNAT | 49 | -0.9419415 | -0.9392397 | 0e+00 | 13.303312 | 0.0000000 |
| PCDHA2 | 149 | -0.7188634 | -0.6267169 | 0e+00 | 12.485679 | 0.0000000 |
| PCDHA3 | 141 | -0.7189282 | -0.6267169 | 0e+00 | 11.583810 | 0.0000001 |
| KCNQ1DN | 39 | -1.6144397 | -1.7080108 | 0e+00 | 11.439140 | 0.0000001 |
| TAP1 | 100 | 0.8373978 | 0.8170222 | 0e+00 | 10.577873 | 0.0000005 |
| NCOR2 | 212 | -0.6647509 | -0.5447866 | 0e+00 | 10.345470 | 0.0000009 |
| PCDHGA1 | 317 | -0.4141393 | -0.4153141 | 0e+00 | 10.238108 | 0.0000012 |
| PCDHA4 | 131 | -0.6779619 | -0.6026401 | 0e+00 | 10.042257 | 0.0000018 |
| PCDHGA2 | 309 | -0.4165786 | -0.4153141 | 0e+00 | 9.987553 | 0.0000021 |
| PCDHGA3 | 295 | -0.4180524 | -0.3901466 | 0e+00 | 9.576696 | 0.0000053 |
| MESTIT1 | 59 | -0.7596278 | -0.7939385 | 0e+00 | 9.544056 | 0.0000057 |
| PCDHA5 | 122 | -0.6998042 | -0.6245086 | 0e+00 | 9.542358 | 0.0000058 |
| NKX6-2 | 36 | -1.3952706 | -1.4319593 | 0e+00 | 9.536050 | 0.0000058 |
| SOX2OT | 85 | -0.7468762 | -0.8104998 | 0e+00 | 9.479982 | 0.0000066 |
| PCDHA7 | 103 | -0.7321981 | -0.6267169 | 0e+00 | 9.375369 | 0.0000085 |
| C11orf21 | 36 | -1.7668062 | -1.9463963 | 0e+00 | 9.257296 | 0.0000111 |
| PITX2 | 64 | -1.0007742 | -0.9208371 | 0e+00 | 9.163509 | 0.0000138 |
| PCDHA6 | 114 | -0.6916765 | -0.6245086 | 0e+00 | 8.749552 | 0.0000357 |
| TSPAN32 | 42 | -1.6254336 | -1.9412022 | 0e+00 | 8.645175 | 0.0000454 |
| MEST | 85 | -0.6582939 | -0.7858506 | 0e+00 | 8.607705 | 0.0000495 |
| ASCL2 | 49 | -0.8873371 | -0.9068647 | 0e+00 | 8.538922 | 0.0000580 |
| WT1 | 60 | -1.0380025 | -1.0581139 | 0e+00 | 8.537528 | 0.0000582 |
| PCDHGB1 | 277 | -0.4040374 | -0.3512600 | 0e+00 | 8.169027 | 0.0001360 |
| PCDHA8 | 92 | -0.7250898 | -0.6311017 | 0e+00 | 8.158124 | 0.0001394 |
| KIAA1949 | 101 | 0.7427567 | 0.6964985 | 0e+00 | 8.107412 | 0.0001567 |
| PCDHA10 | 81 | -0.7546498 | -0.6842488 | 0e+00 | 7.961957 | 0.0002190 |
| PCDHA9 | 84 | -0.7351768 | -0.6598677 | 0e+00 | 7.948914 | 0.0002257 |
| PSMB8 | 83 | 0.8313260 | 0.6488706 | 0e+00 | 7.945020 | 0.0002277 |
| PRDM13 | 41 | -0.9251076 | -0.9595314 | 0e+00 | 7.870850 | 0.0002701 |
| GNA12 | 84 | -0.9539754 | -0.7963257 | 0e+00 | 7.622375 | 0.0004787 |
| PCDHGA4 | 263 | -0.4030756 | -0.3512600 | 0e+00 | 7.620039 | 0.0004812 |
| MAD1L1 | 684 | -0.2827612 | -0.2513758 | 0e+00 | 7.532317 | 0.0005889 |
| SFRP2 | 44 | -1.1033457 | -1.2056608 | 0e+00 | 7.518236 | 0.0006083 |
| SOX1 | 27 | -1.2956875 | -1.5392701 | 0e+00 | 7.349659 | 0.0008967 |
| ZIC1 | 40 | -1.0223609 | -1.1114647 | 1e-07 | 7.295516 | 0.0010157 |
| SPON2 | 30 | -1.0371460 | -1.0318217 | 1e-07 | 7.096401 | 0.0016064 |
| TLX1 | 36 | -0.7858743 | -0.8711780 | 1e-07 | 7.094355 | 0.0016139 |
| PPP1R2P1 | 27 | -1.2635175 | -1.2835267 | 1e-07 | 6.826780 | 0.0029884 |
| TBX15 | 57 | -0.8784010 | -0.9985338 | 2e-07 | 6.710128 | 0.0039091 |
| HLA-E | 64 | 0.9883267 | 0.9471868 | 2e-07 | 6.621543 | 0.0047933 |
| SOX2 | 31 | -0.9356689 | -0.9861379 | 4e-07 | 6.380592 | 0.0083477 |
| HLA-J | 60 | -0.9474161 | -0.9835159 | 4e-07 | 6.363271 | 0.0086869 |
| TFAP2A | 77 | -0.6823000 | -0.6389762 | 4e-07 | 6.359493 | 0.0087624 |
| PCDHGB2 | 249 | -0.3779342 | -0.2994303 | 5e-07 | 6.334383 | 0.0092835 |
| TBX2 | 42 | -0.9192182 | -0.9768360 | 5e-07 | 6.283615 | 0.0104341 |
| NR2E1 | 55 | -0.7936621 | -0.8098274 | 6e-07 | 6.236670 | 0.0116246 |
| DMRTA2 | 32 | -0.8190626 | -0.8833632 | 6e-07 | 6.231215 | 0.0117710 |
| LOC100128811 | 24 | -0.9214760 | -1.0762458 | 6e-07 | 6.224720 | 0.0119478 |
Volcano plots
# volcano selfcont
sig <- subset(res,`fdr(sc)` < 0.05)
plot(res$median , -log10(res$`p-value(sc)`) ,
xlab="effect size (mean t-stat)", ylab="-log10(p-value)",
pch=19, cex=0.5, col="gray",main="self contained test")
grid()
points(sig$median , -log10(sig$`p-value(sc)`) ,
pch=19, cex=0.5, col="red")
Boxplots
Boxplots smallest pvalue.
par(mfrow=c(1,2))
n=50
# self contained
gs <- head(rownames(res),50)
tstats <- lapply(gs, function(g) {
df[which(df$genes==g),"tvals"]
})
names(tstats) <- gs
tstats <- tstats[order(unlist(lapply(tstats,median)))]
boxplot(tstats,horizontal=TRUE,las=1,
main="smallest p-val(selfcont)",cex.axis=0.6,
xlab="t-statistic")
grid()
n=50
# effect size median
sig <- subset(res,`fdr(sc)` < 0.05)
gs <- head(rownames(sig[order(-abs(sig$median)),]),n)
if ( length(gs) >2 ) {
tstats <- lapply(gs, function(g) {
df[which(df$genes==g),"tvals"]
})
names(tstats) <- gs
tstats <- tstats[order(unlist(lapply(tstats,median)))]
boxplot(tstats,horizontal=TRUE,las=1,
main="biggest effect size(median)",cex.axis=0.6,
xlab="t-statistic")
grid()
} else {
plot(1)
mtext("too few significant genes found")
}
Mitch
dmscore <- data.frame( res$median * res$sig)
rownames(dmscore) <- rownames(res)
colnames(dmscore) <- "metric"
if ( ! file.exists("ReactomePathways.gmt") ) {
download.file("https://reactome.org/download/current/ReactomePathways.gmt.zip",
destfile="ReactomePathways.gmt.zip")
unzip("ReactomePathways.gmt.zip")
}
file.info("ReactomePathways.gmt")## size isdir mode mtime ctime
## ReactomePathways.gmt 897680 FALSE 664 2022-05-11 13:54:42 2022-05-11 13:54:42
## atime uid gid uname grname
## ReactomePathways.gmt 2022-05-11 13:55:35 1001 1003 mdz mdzgenesets <- gmt_import("ReactomePathways.gmt")
length(genesets)## [1] 2546mres <- mitch_calc(x=dmscore, genesets=genesets, priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres$enrichment_result,20) %>%
kbl(caption = "Top enriched gene sets with GMEA-Mitch") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 797 | Negative regulation of activity of TFAP2 (AP-2) family transcription factors | 10 | 0.0001209 | -0.7018024 | 0.0009285 |
| 1099 | Regulation of commissural axon pathfinding by SLIT and ROBO | 10 | 0.0003908 | -0.6474208 | 0.0025714 |
| 87 | Apoptotic cleavage of cell adhesion proteins | 11 | 0.0002096 | -0.6453083 | 0.0014958 |
| 1418 | Transcriptional regulation of testis differentiation | 12 | 0.0002348 | -0.6130531 | 0.0016520 |
| 1386 | Thyroxine biosynthesis | 10 | 0.0034244 | -0.5343657 | 0.0159178 |
| 879 | POU5F1 (OCT4), SOX2, NANOG repress genes related to differentiation | 10 | 0.0055806 | -0.5060048 | 0.0228640 |
| 367 | ERBB2 Activates PTK6 Signaling | 13 | 0.0018315 | -0.4990366 | 0.0092526 |
| 796 | Negative regulation of TCF-dependent signaling by WNT ligand antagonists | 15 | 0.0009549 | -0.4925046 | 0.0053957 |
| 64 | Adenylate cyclase activating pathway | 10 | 0.0081256 | -0.4832503 | 0.0312683 |
| 29 | Acetylcholine Neurotransmitter Release Cycle | 16 | 0.0009281 | -0.4780294 | 0.0052637 |
| 479 | GABA synthesis, release, reuptake and degradation | 19 | 0.0003437 | -0.4742558 | 0.0022913 |
| 923 | Platelet sensitization by LDL | 17 | 0.0008191 | 0.4686399 | 0.0047701 |
| 275 | Defective B3GALTL causes PpS | 34 | 0.0000029 | -0.4632690 | 0.0000521 |
| 1360 | TP53 regulates transcription of additional cell cycle genes whose exact role in the p53 pathway remain uncertain | 19 | 0.0004924 | 0.4616661 | 0.0031316 |
| 1167 | SRP-dependent cotranslational protein targeting to membrane | 105 | 0.0000000 | 0.4519886 | 0.0000000 |
| 1162 | SLBP Dependent Processing of Replication-Dependent Histone Pre-mRNAs | 11 | 0.0094503 | 0.4518113 | 0.0354189 |
| 1340 | TFAP2 (AP-2) family regulates transcription of growth factors and their receptors | 12 | 0.0074999 | -0.4456644 | 0.0295334 |
| 837 | O-glycosylation of TSR domain-containing proteins | 35 | 0.0000051 | -0.4453420 | 0.0000803 |
| 168 | Carnitine metabolism | 11 | 0.0106185 | -0.4447950 | 0.0388601 |
| 385 | Erythrocytes take up carbon dioxide and release oxygen | 12 | 0.0079199 | -0.4426103 | 0.0306439 |
mitch_report(mres,outfile="dma3a_mitch.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmphV0LWD/dma3a_mitch.rds ".##
##
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## ordinary text without R code## output file: /mnt/data/mdz/projects/gmea/mitch.knit.md## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /mnt/data/mdz/projects/gmea/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmphV0LWD/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmphV0LWD/rmarkdown-str3441fa6b40d25f.html##
## Output created: /tmp/RtmphV0LWD/mitch_report.html## [1] TRUEUsing CAMERA
stat <- dm$t
names(stat) <- rownames(dm)
# slow
sets2 <- head(sets,1000)
tic()
cres <- cameraPR(statistic=stat, index=sets2, use.ranks = FALSE, inter.gene.cor=0.01, sort = TRUE)
toc()## 33.653 sec elapsed# fast
CORES=detectCores()
#sets <- head(sets,100000)
sets_split <- split(sets,1:CORES)## Warning in split.default(sets, 1:CORES): data length is not a multiple of split
## variabletic()
cres <- mclapply(X=sets_split, function(mysets) {
cameraPR(statistic=stat, index=mysets, use.ranks = FALSE, inter.gene.cor=0.01, sort = TRUE)
}, mc.cores=CORES)
cres <- do.call(rbind,cres)
rownames(cres) <- sapply(strsplit(rownames(cres),"\\."),"[[",2)
cres <- cres[order(cres$PValue),]
g <- rownames(cres)[1]
m <- mclapply(rownames(cres) , function(g) {
probes <- sets[[g]]
scores <- stat[which(names(stat) %in% probes)]
mymedian <- median(scores)
mymean <- mean(scores)
c(mymean,mymedian)
},mc.cores=CORES)
mdf <- do.call(rbind,m)
colnames(mdf) <- c("mean","median")
cres <- cbind(mdf,cres)
toc()## 104.206 sec elapsedhead(subset(cres,Direction=="Up"),25)## mean median NGenes Direction PValue FDR
## PTPRCAP 2.3244582 2.4199633 16 Up 2.971405e-16 3.732084e-13
## LTA 1.9127107 2.1742753 24 Up 9.103873e-16 1.144357e-12
## POU2AF1 2.3400616 2.8811809 12 Up 4.108978e-13 5.177312e-10
## HLA-E 0.9883267 0.9471868 64 Up 6.711529e-10 4.214840e-07
## UBASH3A 2.1508739 2.4913830 10 Up 7.137722e-10 8.893602e-07
## TAP1 0.8373978 0.8170222 100 Up 1.770983e-09 1.488403e-06
## IL32 2.1626345 2.1155364 9 Up 3.539798e-09 2.215914e-06
## CD3D 2.2705304 2.5050647 8 Up 4.655631e-09 5.833505e-06
## IFITM1 1.6211476 1.8124585 16 Up 6.419055e-09 4.031166e-06
## PSMB8 0.8313260 0.6488706 83 Up 1.231922e-08 3.868234e-06
## CXCR5 1.3747828 1.5721151 22 Up 1.284890e-08 5.362276e-06
## HLA-F 0.9382476 0.9727570 56 Up 1.611764e-08 1.009770e-05
## DGKA 1.2742041 1.1275025 25 Up 2.378672e-08 9.966635e-06
## KIAA1949 0.7427567 0.6964985 101 Up 5.797326e-08 2.429080e-05
## BCL11B 0.8447215 1.1065618 61 Up 1.279820e-07 3.905623e-05
## GPR81 1.5466288 1.3820297 14 Up 1.593058e-07 6.653674e-05
## DIABLO 1.3096804 1.4524589 18 Up 5.685340e-07 1.190131e-04
## SNORD34 1.6297553 1.4291833 11 Up 7.563096e-07 1.578166e-04
## SNORD31 1.7803007 1.9604996 9 Up 9.213007e-07 4.984571e-04
## LIME1 1.3134401 1.7264438 17 Up 9.851513e-07 6.201527e-04
## LCK 1.0847201 1.5988245 26 Up 1.011981e-06 1.590075e-04
## PRSS22 1.2096414 1.2322848 20 Up 1.189635e-06 4.984571e-04
## SNORD22 2.1334924 2.1363205 6 Up 1.390755e-06 5.827262e-04
## ISG20 1.5104566 1.1300646 12 Up 1.678216e-06 5.290576e-04
## SEPT1 1.6389022 1.7726724 10 Up 1.910060e-06 5.091599e-04head(subset(cres,Direction=="Down"),25)## mean median NGenes Direction PValue FDR
## C11orf21 -1.7668062 -1.9463963 36 Down 8.428364e-15 1.055231e-11
## TSPAN32 -1.6254336 -1.9412022 42 Down 6.104176e-14 7.691262e-11
## MPO -2.4948914 -2.6484633 13 Down 1.841064e-13 2.312376e-10
## KCNQ1DN -1.6144397 -1.7080108 39 Down 3.924695e-13 4.933342e-10
## PRTN3 -1.9416063 -2.5713679 19 Down 2.669346e-11 3.366045e-08
## MIR145 -2.5680735 -2.7594034 9 Down 1.303212e-10 8.216749e-08
## ELANE -1.9193572 -2.0514902 17 Down 3.376629e-10 4.227540e-07
## AZU1 -2.3303366 -2.3559866 10 Down 1.107618e-09 4.637228e-07
## NKX6-2 -1.3952706 -1.4319593 36 Down 1.657257e-09 1.032471e-06
## CD177 -2.6754771 -3.7016583 7 Down 2.368184e-09 1.488403e-06
## CEBPE -2.1853301 -2.8379658 11 Down 2.725895e-09 3.423724e-06
## OXT -2.0521117 -2.2532375 12 Down 7.114327e-09 4.453569e-06
## SLC16A3 -1.2912785 -0.9007149 38 Down 1.571977e-08 6.528943e-06
## LOC646627 -2.5340767 -2.4228463 7 Down 1.679106e-08 2.107277e-05
## LOC100131496 -2.4936562 -2.3802536 7 Down 2.883213e-08 1.810658e-05
## MS4A3 -2.2133875 -2.3290819 9 Down 3.750388e-08 9.420974e-06
## GPR97 -2.4545992 -2.2893246 7 Down 4.821967e-08 2.429080e-05
## SLFN13 -1.6680322 -1.5960605 17 Down 6.246367e-08 2.615146e-05
## S100A9 -1.7520050 -2.3862889 15 Down 6.563207e-08 1.373898e-05
## FAM124B -1.8078312 -1.7546957 13 Down 1.553549e-07 3.905623e-05
## SPI1 -1.5737683 -1.8428044 18 Down 1.880414e-07 5.904499e-05
## REC8 -1.6097189 -1.8430645 17 Down 1.888226e-07 3.388016e-05
## GNA12 -0.9539754 -0.7963257 84 Down 1.891490e-07 1.110827e-04
## WT1 -1.0380025 -1.0581139 60 Down 2.108660e-07 6.600105e-05
## TNFRSF6B -2.3207431 -3.3172884 7 Down 2.644825e-07 1.110827e-04head(cres,50) %>%
kbl(caption = "Top significant genes with CAMERA") %>%
kable_paper("hover", full_width = F)| mean | median | NGenes | Direction | PValue | FDR | |
|---|---|---|---|---|---|---|
| PTPRCAP | 2.3244582 | 2.4199633 | 16 | Up | 0e+00 | 0.0000000 |
| LTA | 1.9127107 | 2.1742753 | 24 | Up | 0e+00 | 0.0000000 |
| C11orf21 | -1.7668062 | -1.9463963 | 36 | Down | 0e+00 | 0.0000000 |
| TSPAN32 | -1.6254336 | -1.9412022 | 42 | Down | 0e+00 | 0.0000000 |
| MPO | -2.4948914 | -2.6484633 | 13 | Down | 0e+00 | 0.0000000 |
| KCNQ1DN | -1.6144397 | -1.7080108 | 39 | Down | 0e+00 | 0.0000000 |
| POU2AF1 | 2.3400616 | 2.8811809 | 12 | Up | 0e+00 | 0.0000000 |
| PRTN3 | -1.9416063 | -2.5713679 | 19 | Down | 0e+00 | 0.0000000 |
| MIR145 | -2.5680735 | -2.7594034 | 9 | Down | 0e+00 | 0.0000001 |
| ELANE | -1.9193572 | -2.0514902 | 17 | Down | 0e+00 | 0.0000004 |
| HLA-E | 0.9883267 | 0.9471868 | 64 | Up | 0e+00 | 0.0000004 |
| UBASH3A | 2.1508739 | 2.4913830 | 10 | Up | 0e+00 | 0.0000009 |
| AZU1 | -2.3303366 | -2.3559866 | 10 | Down | 0e+00 | 0.0000005 |
| NKX6-2 | -1.3952706 | -1.4319593 | 36 | Down | 0e+00 | 0.0000010 |
| TAP1 | 0.8373978 | 0.8170222 | 100 | Up | 0e+00 | 0.0000015 |
| CD177 | -2.6754771 | -3.7016583 | 7 | Down | 0e+00 | 0.0000015 |
| CEBPE | -2.1853301 | -2.8379658 | 11 | Down | 0e+00 | 0.0000034 |
| IL32 | 2.1626345 | 2.1155364 | 9 | Up | 0e+00 | 0.0000022 |
| CD3D | 2.2705304 | 2.5050647 | 8 | Up | 0e+00 | 0.0000058 |
| IFITM1 | 1.6211476 | 1.8124585 | 16 | Up | 0e+00 | 0.0000040 |
| OXT | -2.0521117 | -2.2532375 | 12 | Down | 0e+00 | 0.0000045 |
| PSMB8 | 0.8313260 | 0.6488706 | 83 | Up | 0e+00 | 0.0000039 |
| CXCR5 | 1.3747828 | 1.5721151 | 22 | Up | 0e+00 | 0.0000054 |
| SLC16A3 | -1.2912785 | -0.9007149 | 38 | Down | 0e+00 | 0.0000065 |
| HLA-F | 0.9382476 | 0.9727570 | 56 | Up | 0e+00 | 0.0000101 |
| LOC646627 | -2.5340767 | -2.4228463 | 7 | Down | 0e+00 | 0.0000211 |
| DGKA | 1.2742041 | 1.1275025 | 25 | Up | 0e+00 | 0.0000100 |
| LOC100131496 | -2.4936562 | -2.3802536 | 7 | Down | 0e+00 | 0.0000181 |
| MS4A3 | -2.2133875 | -2.3290819 | 9 | Down | 0e+00 | 0.0000094 |
| GPR97 | -2.4545992 | -2.2893246 | 7 | Down | 0e+00 | 0.0000243 |
| KIAA1949 | 0.7427567 | 0.6964985 | 101 | Up | 1e-07 | 0.0000243 |
| SLFN13 | -1.6680322 | -1.5960605 | 17 | Down | 1e-07 | 0.0000262 |
| S100A9 | -1.7520050 | -2.3862889 | 15 | Down | 1e-07 | 0.0000137 |
| BCL11B | 0.8447215 | 1.1065618 | 61 | Up | 1e-07 | 0.0000391 |
| FAM124B | -1.8078312 | -1.7546957 | 13 | Down | 2e-07 | 0.0000391 |
| GPR81 | 1.5466288 | 1.3820297 | 14 | Up | 2e-07 | 0.0000665 |
| SPI1 | -1.5737683 | -1.8428044 | 18 | Down | 2e-07 | 0.0000590 |
| REC8 | -1.6097189 | -1.8430645 | 17 | Down | 2e-07 | 0.0000339 |
| GNA12 | -0.9539754 | -0.7963257 | 84 | Down | 2e-07 | 0.0001111 |
| WT1 | -1.0380025 | -1.0581139 | 60 | Down | 2e-07 | 0.0000660 |
| TNFRSF6B | -2.3207431 | -3.3172884 | 7 | Down | 3e-07 | 0.0001111 |
| PITX2 | -1.0007742 | -0.9208371 | 64 | Down | 4e-07 | 0.0000779 |
| PSIMCT-1 | -1.8716010 | -1.8122459 | 11 | Down | 4e-07 | 0.0004985 |
| SFRP2 | -1.1033457 | -1.2056608 | 44 | Down | 5e-07 | 0.0003373 |
| TACR3 | -1.7300623 | -1.9615634 | 13 | Down | 6e-07 | 0.0001190 |
| DIABLO | 1.3096804 | 1.4524589 | 18 | Up | 6e-07 | 0.0001190 |
| HORMAD2 | -1.7271886 | -2.0776542 | 13 | Down | 6e-07 | 0.0001450 |
| SOX1 | -1.2956875 | -1.5392701 | 27 | Down | 6e-07 | 0.0001099 |
| MGC12982 | -1.5745583 | -1.6173178 | 16 | Down | 7e-07 | 0.0006202 |
| SNORD34 | 1.6297553 | 1.4291833 | 11 | Up | 8e-07 | 0.0001578 |
CAMERA + pathway analysis
There’s no ES and the p-value is biased, so not sure about this
Promoter analysis only
dm <- read.table("dma3a.tsv.gz")
dm <- dm[,4:9]
dm <- merge(myann,dm,by=0)
rownames(dm) <- dm[,1]
dm[,1] = NULL
dm <- dm[grep("Promoter_Associated",dm$Regulatory_Feature_Group),]
head(dm,50) %>%
kbl(caption = "Top significant genes with limma") %>%
kable_paper("hover", full_width = F)| UCSC_RefGene_Name | Regulatory_Feature_Group | logFC | AveExpr | t | P.Value | adj.P.Val | B | |
|---|---|---|---|---|---|---|---|---|
| cg00000029 | RBL2 | Promoter_Associated | -0.0446959 | 0.4522209 | -0.8638867 | 0.3888420 | 0.9098372 | -5.265779 |
| cg00000622 | NIPA2 | Promoter_Associated | 0.0333418 | -4.8495004 | 0.7574773 | 0.4497908 | 0.9250804 | -5.340328 |
| cg00000769 | DDX55 | Promoter_Associated | 0.0044715 | -3.1463101 | 0.1951579 | 0.8454975 | 0.9862198 | -5.572331 |
| cg00001245 | MRPS25 | Promoter_Associated | 0.0127411 | -4.2195473 | 0.3178478 | 0.7509823 | 0.9762343 | -5.545030 |
| cg00002028 | PINK1 | Promoter_Associated | 0.0295245 | -3.5036247 | 0.8499858 | 0.3965035 | 0.9114511 | -5.276068 |
| cg00002116 | MRPL12 | Promoter_Associated | -0.0045132 | -5.0826553 | -0.0905479 | 0.9279562 | 0.9940693 | -5.585300 |
| cg00002236 | RTTN | Promoter_Associated | -0.0189864 | -3.5918761 | -0.6113878 | 0.5417426 | 0.9447402 | -5.426842 |
| cg00002406 | CD2BP2 | Promoter_Associated | 0.0073673 | -4.1952587 | 0.2710988 | 0.7866371 | 0.9798899 | -5.556971 |
| cg00002660 | SMARCC2 | Promoter_Associated | -0.0276799 | -3.4149124 | -0.9195186 | 0.3591001 | 0.8995507 | -5.222947 |
| cg00002930 | NFKBIL1 , ATP6V1G2 | Promoter_Associated | 0.0431087 | -2.5246196 | 0.9921947 | 0.3224833 | 0.8886832 | -5.163013 |
| cg00003173 | CHCHD4, TMEM43 | Promoter_Associated | 0.0440248 | -3.1942015 | 1.2228554 | 0.2230416 | 0.8453216 | -4.943077 |
| cg00003202 | RFX5 | Promoter_Associated | 0.0246642 | -4.4899392 | 0.4925218 | 0.6229727 | 0.9593472 | -5.483671 |
| cg00003784 | CCDC45, DDX5 | Promoter_Associated | 0.0271078 | -3.7722693 | 0.8931927 | 0.3729904 | 0.9046774 | -5.243545 |
| cg00004072 | ZFP36 | Promoter_Associated | 0.0532912 | -3.1321117 | 1.4801258 | 0.1406519 | 0.7861540 | -4.644902 |
| cg00004082 | SLC2A9 | Promoter_Associated | -0.0567563 | -2.1842139 | -0.9558817 | 0.3404610 | 0.8938321 | -5.193522 |
| cg00004207 | SFRS7 | Promoter_Associated_Cell_type_specific | -0.0362644 | -3.0355991 | -0.8113672 | 0.4182657 | 0.9177356 | -5.303786 |
| cg00005010 | MT1F | Promoter_Associated_Cell_type_specific | 0.0077148 | -4.0442169 | 0.2134724 | 0.8312090 | 0.9845704 | -5.569085 |
| cg00005543 | TCTE3 , C6orf70 | Promoter_Associated | -0.0049202 | -4.2181795 | -0.1388422 | 0.8897358 | 0.9905448 | -5.580493 |
| cg00006032 | GPHN | Promoter_Associated | -0.0762156 | -3.1976445 | -1.3023458 | 0.1945231 | 0.8284346 | -4.856869 |
| cg00006122 | C12orf44 | Promoter_Associated | 0.0572183 | -3.0480148 | 2.0292594 | 0.0439589 | 0.6192964 | -3.825553 |
| cg00006884 | CCDC126 | Promoter_Associated | 0.0081832 | -3.4410951 | 0.2750624 | 0.7835953 | 0.9796542 | -5.556032 |
| cg00007226 | PACS2 | Promoter_Associated | 0.0187771 | -2.7369800 | 0.6756715 | 0.5001482 | 0.9360383 | -5.391035 |
| cg00007269 | ZNF77 | Promoter_Associated | 0.0243405 | -3.2605321 | 0.9476296 | 0.3446351 | 0.8950468 | -5.200298 |
| cg00007898 | DSTYK | Promoter_Associated | -0.0487729 | -4.7627008 | -0.9935158 | 0.3218412 | 0.8884407 | -5.161882 |
| cg00008004 | Promoter_Associated | 0.0530924 | -3.1300562 | 1.2249733 | 0.2222445 | 0.8448815 | -4.940849 | |
| cg00008033 | ZNF613 | Promoter_Associated | -0.0926061 | -2.3343986 | -1.8015212 | 0.0733558 | 0.6948466 | -4.195215 |
| cg00008188 | C14orf181 | Promoter_Associated | 0.0310443 | -3.3313383 | 1.0703318 | 0.2859557 | 0.8754919 | -5.093560 |
| cg00008387 | TMEM188 | Promoter_Associated | 0.0187394 | -2.6875103 | 0.7807688 | 0.4360017 | 0.9222170 | -5.324840 |
| cg00008665 | C3orf39 | Promoter_Associated | 0.0274437 | 2.6784492 | 0.6050298 | 0.5459492 | 0.9453510 | -5.430190 |
| cg00008671 | FAM190B | Promoter_Associated | 0.0367837 | -0.8166720 | 0.9195016 | 0.3591090 | 0.8995507 | -5.222960 |
| cg00008713 | IMPA2 | Promoter_Associated | 0.0547502 | -2.6401426 | 1.6624517 | 0.0982265 | 0.7348906 | -4.400228 |
| cg00008823 | NRD1 | Promoter_Associated | 0.0392469 | -4.1361594 | 1.3306030 | 0.1850646 | 0.8228002 | -4.824945 |
| cg00008839 | OAZ1 | Promoter_Associated | -0.0109090 | -5.3145920 | -0.2359452 | 0.8137533 | 0.9829590 | -5.564703 |
| cg00009167 | KIAA1324, C1orf194 | Promoter_Associated | 0.0203360 | -2.6887142 | 0.7767049 | 0.4383897 | 0.9227737 | -5.327576 |
| cg00009214 | DERA | Promoter_Associated_Cell_type_specific | 0.0165604 | -2.8461860 | 0.7015914 | 0.4838734 | 0.9326330 | -5.375591 |
| cg00009407 | TTC8 | Promoter_Associated | 0.0546805 | -3.7141576 | 1.8150757 | 0.0712380 | 0.6896927 | -4.174389 |
| cg00009412 | DDX18 | Promoter_Associated | 0.0556711 | -3.8699150 | 1.5351918 | 0.1265570 | 0.7705896 | -4.573905 |
| cg00009970 | MEX3C | Promoter_Associated | 0.0057340 | -3.9394691 | 0.1630548 | 0.8706649 | 0.9887727 | -5.577321 |
| cg00010046 | MGC23284, MVD | Promoter_Associated | 0.0578509 | -3.2839645 | 1.7147500 | 0.0881753 | 0.7201932 | -4.324988 |
| cg00010168 | MMS19, UBTD1 | Promoter_Associated | 0.0215936 | -2.4492618 | 0.7670533 | 0.4440916 | 0.9238443 | -5.334016 |
| cg00010266 | MFSD3 | Promoter_Associated | -0.0110287 | -3.5919353 | -0.3064669 | 0.7596164 | 0.9770348 | -5.548111 |
| cg00010659 | TMEM14C | Promoter_Associated | -0.0108691 | -3.4079584 | -0.3903382 | 0.6967649 | 0.9700739 | -5.522770 |
| cg00010853 | KIAA1949 | Promoter_Associated | 0.0616603 | -0.2660541 | 1.0482193 | 0.2959945 | 0.8797502 | -5.113741 |
| cg00010932 | METTL5 | Promoter_Associated | -0.0433476 | 1.7565091 | -0.7089306 | 0.4793184 | 0.9317887 | -5.371113 |
| cg00010947 | HELQ , MRPS18C | Promoter_Associated | 0.0352093 | -3.4402376 | 0.6786797 | 0.4982445 | 0.9355984 | -5.389272 |
| cg00011122 | C16orf75 | Promoter_Associated | -0.0340731 | -2.7978920 | -0.5123938 | 0.6090266 | 0.9571802 | -5.475019 |
| cg00011284 | RBL2 | Promoter_Associated | -0.0490232 | -3.3631727 | -1.2791249 | 0.2025600 | 0.8332300 | -4.882602 |
| cg00011578 | PPP5C | Promoter_Associated | 0.0318908 | -2.3697313 | 1.3798035 | 0.1694215 | 0.8125546 | -4.767763 |
| cg00011994 | FLOT1, IER3 | Promoter_Associated | -0.0011729 | -3.7442208 | -0.0386883 | 0.9691833 | 0.9977478 | -5.588208 |
| cg00012036 | H3F3B | Promoter_Associated | 0.0349406 | -2.9522732 | 1.2434522 | 0.2153776 | 0.8415437 | -4.921250 |
# trim down the input dataset
dm <- dm[,c("UCSC_RefGene_Name","t")]
# histogram of t values
hist(dm$t,breaks=seq(from=-6,to=6,by=1))
# set cores to used for parallel execution
tic()
gme_res_promoter <- calc_sc(dm)
time2 <- toc() #15.7 20.2 20.3 17.5 17.9 17.7 20.6 ## 15.222 sec elapsedtime2## $tic
## elapsed
## 3780.97
##
## $toc
## elapsed
## 3796.192
##
## $msg
## logical(0)df <- gme_res_promoter[[1]]
res <- gme_res_promoter[[2]]
write.table(res ,file="gmea_promo.tsv")Table of top results.
head(res,50) %>%
kbl(caption = "Top significant genes with GMEA") %>%
kable_paper("hover", full_width = F)| nprobes | mean | median | p-value(sc) | sig | fdr(sc) | |
|---|---|---|---|---|---|---|
| PSMB9 | 49 | 0.5761512 | 0.5821938 | 0.0000026 | 5.576796 | 0.0273321 |
| LTA | 17 | 2.3851459 | 2.5326143 | 0.0000153 | 4.816480 | 0.1573792 |
| ZNF331 | 22 | -0.8964193 | -0.9539272 | 0.0000205 | 4.688161 | 0.2114577 |
| HLA-F | 37 | 0.8824152 | 0.8890888 | 0.0000264 | 4.579069 | 0.2718154 |
| TAP1 | 52 | 0.5228433 | 0.5670995 | 0.0000274 | 4.561618 | 0.2829321 |
| PHTF2 | 29 | 0.6846189 | 0.5747849 | 0.0000318 | 4.497636 | 0.3278101 |
| TMEM60 | 29 | 0.6846189 | 0.5747849 | 0.0000318 | 4.497636 | 0.3278101 |
| KDM6B | 19 | 0.9253189 | 0.5649357 | 0.0000381 | 4.418540 | 0.3932190 |
| C20orf94 | 15 | 0.6004834 | 0.6045686 | 0.0000610 | 4.214420 | 0.6290894 |
| KIAA1949 | 67 | 0.5198463 | 0.5612521 | 0.0000614 | 4.211571 | 0.6331675 |
| SF3A2 | 17 | 1.0063875 | 0.6782074 | 0.0000763 | 4.117510 | 0.7862091 |
| TUBB | 26 | 0.8749918 | 0.9736874 | 0.0000816 | 4.088475 | 0.8404865 |
| PSMB8 | 46 | 0.5721948 | 0.4699794 | 0.0001023 | 3.989915 | 1.0000000 |
| KIF5B | 14 | 0.7902940 | 0.5348532 | 0.0001221 | 3.913390 | 1.0000000 |
| SRP68 | 14 | 0.9111961 | 0.8667275 | 0.0001221 | 3.913390 | 1.0000000 |
| PLEKHJ1 | 16 | 1.0268987 | 0.8825641 | 0.0001526 | 3.816480 | 1.0000000 |
| HSPE1 | 23 | 0.6167638 | 0.5507955 | 0.0001814 | 3.741275 | 1.0000000 |
| HSPD1 | 23 | 0.6167638 | 0.5507955 | 0.0001814 | 3.741275 | 1.0000000 |
| TMEM134 | 19 | 0.5859688 | 0.5494877 | 0.0002098 | 3.678177 | 1.0000000 |
| HSPA1B | 23 | 0.6806965 | 0.5842477 | 0.0002148 | 3.667935 | 1.0000000 |
| IQCH | 13 | 0.8418442 | 0.6822960 | 0.0002441 | 3.612360 | 1.0000000 |
| AAGAB | 13 | 0.8418442 | 0.6822960 | 0.0002441 | 3.612360 | 1.0000000 |
| RWDD1 | 13 | 0.8209741 | 0.6082937 | 0.0002441 | 3.612360 | 1.0000000 |
| SSH3 | 13 | -1.5301230 | -1.4511893 | 0.0002441 | 3.612360 | 1.0000000 |
| STAG3L4 | 13 | 0.7675809 | 0.8131921 | 0.0002441 | 3.612360 | 1.0000000 |
| PMS2L4 | 13 | 0.7675809 | 0.8131921 | 0.0002441 | 3.612360 | 1.0000000 |
| PTPRCAP | 13 | 2.3916698 | 2.4635616 | 0.0002441 | 3.612360 | 1.0000000 |
| BCL11B | 14 | 1.3268440 | 1.3321196 | 0.0002441 | 3.612360 | 1.0000000 |
| GALK2 | 25 | 0.7274296 | 0.7982154 | 0.0002498 | 3.602402 | 1.0000000 |
| PEX10 | 22 | 0.7323587 | 0.8632916 | 0.0002556 | 3.592465 | 1.0000000 |
| TRIM27 | 55 | 0.4703276 | 0.4519905 | 0.0003200 | 3.494916 | 1.0000000 |
| C5orf36 | 20 | 0.7279515 | 0.8111289 | 0.0003223 | 3.491683 | 1.0000000 |
| ANKRD32 | 20 | 0.7279515 | 0.8111289 | 0.0003223 | 3.491683 | 1.0000000 |
| IRAK1BP1 | 14 | 0.7353866 | 0.7967438 | 0.0003662 | 3.436269 | 1.0000000 |
| C5orf35 | 14 | 1.1025095 | 0.9671847 | 0.0003662 | 3.436269 | 1.0000000 |
| PRKRA | 15 | 0.7994224 | 0.7985942 | 0.0004272 | 3.369322 | 1.0000000 |
| DFNB59 | 15 | 0.7994224 | 0.7985942 | 0.0004272 | 3.369322 | 1.0000000 |
| ZBTB9 | 35 | 0.5068120 | 0.4382240 | 0.0004435 | 3.353107 | 1.0000000 |
| HIST1H2AK | 12 | 0.6486225 | 0.6487854 | 0.0004883 | 3.311330 | 1.0000000 |
| ZC3H12D | 12 | 1.0249054 | 0.8920786 | 0.0004883 | 3.311330 | 1.0000000 |
| TBC1D10C | 12 | 1.2114500 | 0.9331465 | 0.0004883 | 3.311330 | 1.0000000 |
| TMEM128 | 12 | 1.0851858 | 0.8574542 | 0.0004883 | 3.311330 | 1.0000000 |
| LYSMD1 | 12 | 0.8175382 | 0.6964782 | 0.0004883 | 3.311330 | 1.0000000 |
| SCNM1 | 12 | 0.8175382 | 0.6964782 | 0.0004883 | 3.311330 | 1.0000000 |
| PPM1M | 13 | 1.3734122 | 1.2332757 | 0.0004883 | 3.311330 | 1.0000000 |
| OBFC2A | 12 | 0.4234877 | 0.3051854 | 0.0004883 | 3.311330 | 1.0000000 |
| NME1-NME2 | 25 | 0.6867271 | 0.7243284 | 0.0004895 | 3.310271 | 1.0000000 |
| CDC7 | 16 | 0.8879637 | 0.8115182 | 0.0005798 | 3.236696 | 1.0000000 |
| ARSG | 16 | 0.7496755 | 0.8700503 | 0.0005798 | 3.236696 | 1.0000000 |
| UVRAG | 15 | 0.8486591 | 0.9494920 | 0.0006104 | 3.214420 | 1.0000000 |
Volcano plots
# volcano selfcont
sig <- subset(res,`fdr(sc)` < 0.05)
plot(res$median , -log10(res$`p-value(sc)`) ,
xlab="effect size (mean t-stat)", ylab="-log10(p-value)",
pch=19, cex=0.5, col="gray",main="self contained test")
grid()
points(sig$median , -log10(sig$`p-value(sc)`) ,
pch=19, cex=0.5, col="red")
Boxplots
Boxplots smallest pvalue.
par(mfrow=c(1,2))
n=50
# self contained
gs <- head(rownames(res),50)
tstats <- lapply(gs, function(g) {
df[which(df$genes==g),"tvals"]
})
names(tstats) <- gs
tstats <- tstats[order(unlist(lapply(tstats,median)))]
boxplot(tstats,horizontal=TRUE,las=1,
main="smallest p-val(selfcont)",cex.axis=0.6,
xlab="t-statistic")
grid()
n=50
# effect size median
sig <- subset(res,`fdr(sc)` < 0.05)
gs <- head(rownames(sig[order(-abs(sig$median)),]),n)
if ( length(gs) >2 ) {
tstats <- lapply(gs, function(g) {
df[which(df$genes==g),"tvals"]
})
names(tstats) <- gs
tstats <- tstats[order(unlist(lapply(tstats,median)))]
boxplot(tstats,horizontal=TRUE,las=1,
main="biggest effect size(median)",cex.axis=0.6,
xlab="t-statistic")
grid()
} else {
plot(1)
mtext("too few significant genes found")
}
Mitch
dmscore <- data.frame( res$median * res$sig)
rownames(dmscore) <- rownames(res)
colnames(dmscore) <- "metric"
genesets <- gmt_import("ReactomePathways.gmt")
mres <- mitch_calc(x=dmscore, genesets=genesets,priority="effect")## Note: Enrichments with large effect sizes may not be
## statistically significant.head(mres$enrichment_result,20) %>%
kbl(caption = "Top enriched gene sets with GMEA-Mitch (promoter only)") %>%
kable_paper("hover", full_width = F)| set | setSize | pANOVA | s.dist | p.adjustANOVA | |
|---|---|---|---|---|---|
| 829 | Regulation of FZD by ubiquitination | 12 | 0.0004435 | -0.5854282 | 0.0293696 |
| 812 | RUNX2 regulates osteoblast differentiation | 13 | 0.0002702 | -0.5832176 | 0.0230084 |
| 379 | Glutamate Neurotransmitter Release Cycle | 11 | 0.0022989 | -0.5306853 | 0.0830377 |
| 65 | Assembly of collagen fibrils and other multimeric structures | 10 | 0.0046010 | -0.5173411 | 0.1166405 |
| 526 | Maturation of nucleoprotein | 11 | 0.0047419 | 0.4916096 | 0.1166405 |
| 1160 | Voltage gated Potassium channels | 10 | 0.0076020 | -0.4873265 | 0.1372969 |
| 625 | Neurotransmitter release cycle | 18 | 0.0003600 | -0.4856269 | 0.0266115 |
| 864 | Regulation of pyruvate dehydrogenase (PDH) complex | 11 | 0.0069827 | -0.4696146 | 0.1328140 |
| 250 | Dopamine Neurotransmitter Release Cycle | 10 | 0.0142609 | -0.4474236 | 0.1868015 |
| 508 | Long-term potentiation | 11 | 0.0104895 | -0.4455287 | 0.1615337 |
| 672 | PKA activation | 10 | 0.0174194 | -0.4341194 | 0.2171792 |
| 1001 | Signaling by Retinoic Acid | 17 | 0.0020036 | -0.4327568 | 0.0770417 |
| 673 | PKA-mediated phosphorylation of CREB | 11 | 0.0133879 | -0.4305654 | 0.1793073 |
| 614 | Negative regulation of NMDA receptor-mediated neuronal transmission | 12 | 0.0105702 | -0.4261380 | 0.1615337 |
| 811 | RUNX2 regulates bone development | 17 | 0.0025294 | -0.4229719 | 0.0861428 |
| 384 | Glycogen storage diseases | 10 | 0.0219436 | -0.4183503 | 0.2356464 |
| 817 | Ras activation upon Ca2+ influx through NMDA receptor | 10 | 0.0272862 | -0.4030277 | 0.2450697 |
| 1053 | TNF receptor superfamily (TNFSF) members mediating non-canonical NF-kB pathway | 10 | 0.0296084 | 0.3971664 | 0.2468059 |
| 1034 | Synthesis of Leukotrienes (LT) and Eoxins (EX) | 10 | 0.0334008 | -0.3883940 | 0.2602207 |
| 835 | Regulation of KIT signaling | 10 | 0.0350228 | 0.3849005 | 0.2642229 |
mitch_report(mres,outfile="dma3a_mitch_promo.html",overwrite=TRUE)## Note: overwriting existing report## Dataset saved as " /tmp/RtmphV0LWD/dma3a_mitch_promo.rds ".##
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## ordinary text without R code## output file: /mnt/data/mdz/projects/gmea/mitch.knit.md## /home/mdz/anaconda3/bin/pandoc +RTS -K512m -RTS /mnt/data/mdz/projects/gmea/mitch.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tmp/RtmphV0LWD/mitch_report.html --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /usr/local/lib/R/site-library/rmarkdown/rmarkdown/lua/latex-div.lua --self-contained --variable bs3=TRUE --standalone --section-divs --template /usr/local/lib/R/site-library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=bootstrap --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /tmp/RtmphV0LWD/rmarkdown-str3441fa75898b7.html##
## Output created: /tmp/RtmphV0LWD/mitch_report.html## [1] TRUESession Information
For reproducibility.
sessionInfo()## R version 4.2.0 (2022-04-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 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_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] pkgload_1.3.0
## [2] GGally_2.1.2
## [3] ggplot2_3.3.6
## [4] reshape2_1.4.4
## [5] beeswarm_0.4.0
## [6] gplots_3.1.3
## [7] gtools_3.9.3
## [8] tibble_3.1.7
## [9] echarts4r_0.4.4
## [10] tictoc_1.0.1
## [11] IlluminaHumanMethylation450kanno.ilmn12.hg19_0.6.1
## [12] IlluminaHumanMethylation450kmanifest_0.4.0
## [13] minfi_1.42.0
## [14] bumphunter_1.38.0
## [15] locfit_1.5-9.6
## [16] iterators_1.0.14
## [17] foreach_1.5.2
## [18] Biostrings_2.64.0
## [19] XVector_0.36.0
## [20] SummarizedExperiment_1.26.1
## [21] Biobase_2.56.0
## [22] MatrixGenerics_1.8.1
## [23] matrixStats_0.62.0
## [24] GenomicRanges_1.48.0
## [25] GenomeInfoDb_1.32.2
## [26] IRanges_2.30.0
## [27] S4Vectors_0.34.0
## [28] BiocGenerics_0.42.0
## [29] limma_3.52.2
## [30] mitch_1.8.0
## [31] eulerr_6.1.1
## [32] kableExtra_1.3.4
## [33] dplyr_1.0.9
##
## loaded via a namespace (and not attached):
## [1] BiocFileCache_2.4.0 systemfonts_1.0.4
## [3] plyr_1.8.7 splines_4.2.0
## [5] BiocParallel_1.30.3 digest_0.6.29
## [7] htmltools_0.5.3 fansi_1.0.3
## [9] magrittr_2.0.3 memoise_2.0.1
## [11] tzdb_0.3.0 readr_2.1.2
## [13] annotate_1.74.0 svglite_2.1.0
## [15] askpass_1.1 siggenes_1.70.0
## [17] prettyunits_1.1.1 colorspace_2.0-3
## [19] blob_1.2.3 rvest_1.0.2
## [21] rappdirs_0.3.3 xfun_0.31
## [23] crayon_1.5.1 RCurl_1.98-1.7
## [25] jsonlite_1.8.0 genefilter_1.78.0
## [27] GEOquery_2.64.2 survival_3.3-1
## [29] glue_1.6.2 gtable_0.3.0
## [31] zlibbioc_1.42.0 webshot_0.5.3
## [33] DelayedArray_0.22.0 Rhdf5lib_1.18.2
## [35] HDF5Array_1.24.1 scales_1.2.0
## [37] DBI_1.1.3 rngtools_1.5.2
## [39] Rcpp_1.0.9 viridisLite_0.4.0
## [41] xtable_1.8-4 progress_1.2.2
## [43] bit_4.0.4 mclust_5.4.10
## [45] preprocessCore_1.58.0 htmlwidgets_1.5.4
## [47] httr_1.4.3 RColorBrewer_1.1-3
## [49] ellipsis_0.3.2 pkgconfig_2.0.3
## [51] reshape_0.8.9 XML_3.99-0.10
## [53] sass_0.4.2 dbplyr_2.2.1
## [55] utf8_1.2.2 tidyselect_1.1.2
## [57] rlang_1.0.4 later_1.3.0
## [59] AnnotationDbi_1.58.0 munsell_0.5.0
## [61] tools_4.2.0 cachem_1.0.6
## [63] cli_3.3.0 generics_0.1.3
## [65] RSQLite_2.2.15 evaluate_0.15
## [67] stringr_1.4.0 fastmap_1.1.0
## [69] yaml_2.3.5 knitr_1.39
## [71] bit64_4.0.5 beanplot_1.3.1
## [73] scrime_1.3.5 caTools_1.18.2
## [75] purrr_0.3.4 KEGGREST_1.36.3
## [77] nlme_3.1-158 doRNG_1.8.2
## [79] sparseMatrixStats_1.8.0 mime_0.12
## [81] nor1mix_1.3-0 xml2_1.3.3
## [83] biomaRt_2.52.0 compiler_4.2.0
## [85] rstudioapi_0.13 filelock_1.0.2
## [87] curl_4.3.2 png_0.1-7
## [89] bslib_0.4.0 stringi_1.7.8
## [91] highr_0.9 GenomicFeatures_1.48.3
## [93] lattice_0.20-45 Matrix_1.4-1
## [95] multtest_2.52.0 vctrs_0.4.1
## [97] pillar_1.8.0 lifecycle_1.0.1
## [99] rhdf5filters_1.8.0 jquerylib_0.1.4
## [101] data.table_1.14.2 bitops_1.0-7
## [103] httpuv_1.6.5 rtracklayer_1.56.1
## [105] R6_2.5.1 BiocIO_1.6.0
## [107] promises_1.2.0.1 KernSmooth_2.23-20
## [109] gridExtra_2.3 codetools_0.2-18
## [111] MASS_7.3-58 assertthat_0.2.1
## [113] rhdf5_2.40.0 openssl_2.0.2
## [115] rjson_0.2.21 withr_2.5.0
## [117] GenomicAlignments_1.32.0 Rsamtools_2.12.0
## [119] GenomeInfoDbData_1.2.8 hms_1.1.1
## [121] quadprog_1.5-8 grid_4.2.0
## [123] tidyr_1.2.0 base64_2.0
## [125] rmarkdown_2.14 DelayedMatrixStats_1.18.0
## [127] illuminaio_0.38.0 shiny_1.7.2
## [129] restfulr_0.0.15