GSE188236: miRNA target enrichment analysis - the effect of background list
Mark Ziemann
2023-12-14
Source: TBA
Introduction
Most popular tools that do enrichment analysis of microRNA target genes don’t accept a background list. Let’s see what happens when we use a background list for this type of analysis. GSE188236 was used because it has both miR and mRNA datasets from the same samples, which means we can generate a background for the microRNA target gene pathway enrichment analysis. This dataset examines the effect of TGFB1 expression and co-treatment with SB431542 is a selective and potent inhibitor of the TGF-beta/Activin/Nodal pathway. For the sake of this analysis we will only be looking at the comparison of control and TGFB1 cells, with 5 replicates.
library("DESeq2")## Loading required package: S4Vectors## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges## Loading required package: GenomicRanges## Loading required package: GenomeInfoDb## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.##
## Attaching package: 'Biobase'## The following object is masked from 'package:MatrixGenerics':
##
## rowMedians## The following objects are masked from 'package:matrixStats':
##
## anyMissing, rowMedianslibrary("plyr")##
## Attaching package: 'plyr'## The following object is masked from 'package:matrixStats':
##
## count## The following object is masked from 'package:IRanges':
##
## desc## The following object is masked from 'package:S4Vectors':
##
## renamelibrary("dplyr")##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following object is masked from 'package:Biobase':
##
## combine## The following object is masked from 'package:matrixStats':
##
## count## The following objects are masked from 'package:GenomicRanges':
##
## intersect, setdiff, union## The following object is masked from 'package:GenomeInfoDb':
##
## intersect## The following objects are masked from 'package:IRanges':
##
## collapse, desc, intersect, setdiff, slice, union## The following objects are masked from 'package:S4Vectors':
##
## first, intersect, rename, setdiff, setequal, union## The following objects are masked from 'package:BiocGenerics':
##
## combine, intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary("kableExtra")##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
##
## group_rowslibrary("readxl")
library("fgsea")
library("eulerr")Method overview
Load count data from GEO, conduct DESeq2 and make short list of up and downregulated genes. Select target mRNAs using a database. Discard any target mRNAs that aren’t expressed at sufficient level to be differential. Conduct enrichment analysis on these target mRNAs. Repeat the above but without the background list.
Load data
if ( file.exists("GSE188236_miRcounts.rds") ) {
df_mir <- readRDS("GSE188236_miRcounts.rds")
df_mrna <- readRDS("GSE188236_mRNAcounts.rds")
} else {
download.file("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE188236&format=file",
destfile="GSE188236.tar")
untar("GSE188236.tar")
files_mrna <- c("GSM5673193_CTRL_1.txt.gz","GSM5673194_CTRL_2.txt.gz","GSM5673195_CTRL_3.txt.gz",
"GSM5673196_CTRL_4.txt.gz","GSM5673197_CTRL_5.txt.gz","GSM5673198_TGFB1_1.txt.gz",
"GSM5673199_TGFB1_2.txt.gz","GSM5673200_TGFB1_3.txt.gz","GSM5673201_TGFB1_4.txt.gz",
"GSM5673202_TGFB1_5.txt.gz")
dat_mrna <- lapply(files_mrna,function(f) {
read.table(f, header=TRUE)
})
df_mrna <- join_all(dat_mrna)
rownames(df_mrna) <- df_mrna$Ensembl_gene_id ; df_mrna$Ensembl_gene_id=NULL
saveRDS(object=df_mrna,file="GSE188236_mRNAcounts.rds")
files_mir <- c("GSM5673208_CTRL_1.txt.gz", "GSM5673209_CTRL_2.txt.gz", "GSM5673210_CTRL_3.txt.gz",
"GSM5673211_CTRL_4.txt.gz", "GSM5673212_CTRL_5.txt.gz", "GSM5673213_TGFB1_1.txt.gz",
"GSM5673214_TGFB1_2.txt.gz", "GSM5673215_TGFB1_3.txt.gz", "GSM5673216_TGFB1_4.txt.gz",
"GSM5673217_TGFB1_5.txt.gz")
dat_mir <- lapply(files_mir,function(f) {
read.table(f, header=TRUE)
})
df_mir <- join_all(dat_mir)
rownames(df_mir) <- df_mir$Ensembl_ID ; df_mir$Ensembl_ID=NULL
saveRDS(object=df_mir,file="GSE188236_miRcounts.rds")
dir.create("GSE188236")
delfiles <- list.files(".",pattern="txt.gz")
file.copy(delfiles,"GSE188236",overwrite=TRUE)
unlink(delfiles)
}Filtering
dim(df_mir)## [1] 602 10summary(rowMeans(df_mir))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5 41 200 20923 1700 4214429df_mir_f <- df_mir[which(rowMeans(df_mir)>9),]
dim(df_mir_f)## [1] 595 10df_mir_f <- round(df_mir_f)
dim(df_mrna)## [1] 60666 10summary(rowMeans(df_mrna))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 0 1 468 17 10326474df_mrna_f <- df_mrna[which(rowMeans(df_mrna)>9),]
dim(df_mrna_f)## [1] 17646 10summary(rowMeans(df_mrna_f))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9 29 120 1606 486 10326474Load ensembl 105 data
Not sure whether this is the correct one. Just a guess. There are 60666 rows of data and 60531 in the v105 annotation which is quite close. Data was downloaded from Ensembl biomart archive 27th Nov 2023.
gt <- read.table("mart_export.txt",sep="\t",header=TRUE)Differential analysis miRNA
ss <- data.frame(colnames(df_mir_f))
rownames(ss) <- ss[,1]
colnames(ss) <- "samplename"
ss$trt <- factor(grepl("TGF",ss$samplename))
dds <- DESeqDataSetFromMatrix(countData = df_mir_f , colData = ss, design = ~ trt )## converting counts to integer moderes <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz<- results(res)
vsd <- vst(dds, blind=FALSE,nsub=200)
zz <- cbind(as.data.frame(z),assay(vsd))
dge_mir <- as.data.frame(zz[order(zz$pvalue),])
dge_mir[1:20,1:6] %>%
kbl(caption = "Top miRNA expression differences between ctrl and TGFB1 treated cells") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSG00000221430.1 | 38.70888 | 1.3966136 | 0.2675422 | 5.220163 | 0.0000002 | 0.0000999 |
| ENSG00000207635.1 | 1374.01594 | -3.0763902 | 0.6573548 | -4.679954 | 0.0000029 | 0.0005590 |
| ENSG00000215991.1 | 200.10822 | -2.8669389 | 0.6137997 | -4.670805 | 0.0000030 | 0.0005590 |
| ENSG00000199157.1 | 22.05865 | -2.0649738 | 0.5161413 | -4.000792 | 0.0000631 | 0.0088225 |
| ENSG00000207595.1 | 7570.74021 | 0.9544503 | 0.2425789 | 3.934598 | 0.0000833 | 0.0093170 |
| ENSG00000199177.1 | 16601.35509 | 0.9993812 | 0.2621493 | 3.812259 | 0.0001377 | 0.0128293 |
| ENSG00000202569.2 | 10823.70658 | -0.9710050 | 0.2784574 | -3.487087 | 0.0004883 | 0.0389953 |
| ENSG00000207947.1 | 39369.13777 | 0.4604510 | 0.1362776 | 3.378773 | 0.0007281 | 0.0508760 |
| ENSG00000215938.1 | 34.96205 | -1.6676766 | 0.4993528 | -3.339676 | 0.0008388 | 0.0520964 |
| ENSG00000208035.1 | 182667.75439 | 1.1575924 | 0.3544068 | 3.266282 | 0.0010897 | 0.0609141 |
| ENSG00000201943.1 | 42.93282 | -1.1252750 | 0.3643687 | -3.088287 | 0.0020131 | 0.0991240 |
| ENSG00000216009.1 | 865.66647 | -0.7038392 | 0.2291310 | -3.071777 | 0.0021279 | 0.0991240 |
| ENSG00000211997.1 | 5393.96134 | 0.5880439 | 0.1953641 | 3.009990 | 0.0026126 | 0.1123404 |
| ENSG00000207584.1 | 3061.07930 | 0.3739365 | 0.1304552 | 2.866398 | 0.0041517 | 0.1657724 |
| ENSG00000207759.1 | 2503.74820 | 0.5475869 | 0.1989519 | 2.752359 | 0.0059168 | 0.2204983 |
| ENSG00000207548.1 | 24.61279 | 1.1562851 | 0.4404271 | 2.625373 | 0.0086554 | 0.2655144 |
| ENSG00000207944.1 | 3589.42379 | 0.3380020 | 0.1302380 | 2.595264 | 0.0094518 | 0.2655144 |
| ENSG00000207975.1 | 176.52056 | 0.4530077 | 0.1754826 | 2.581497 | 0.0098373 | 0.2655144 |
| ENSG00000201143.1 | 41.29776 | -0.9828034 | 0.3824975 | -2.569437 | 0.0101864 | 0.2655144 |
| ENSG00000208008.1 | 80986.50162 | 0.4585598 | 0.1787005 | 2.566080 | 0.0102855 | 0.2655144 |
mir_up <- rownames(subset(dge_mir,padj<0.05 & log2FoldChange >0 ))
mir_up## [1] "ENSG00000221430.1" "ENSG00000207595.1" "ENSG00000199177.1"mir_up <- gt[which(gt$Gene.stable.ID.version %in% mir_up),]
mir_up$Gene.name <- tolower(mir_up$Gene.name)
mir_up## Gene.stable.ID Gene.stable.ID.version Gene.name
## 20177 ENSG00000199177 ENSG00000199177.1 mir31
## 20307 ENSG00000207595 ENSG00000207595.1 mir181a2
## 47185 ENSG00000221430 ENSG00000221430.1 mir1294mir_dn <- rownames(subset(dge_mir,padj<0.05 & log2FoldChange <0 ))
mir_dn## [1] "ENSG00000207635.1" "ENSG00000215991.1" "ENSG00000199157.1"
## [4] "ENSG00000202569.2"mir_dn <- gt[which(gt$Gene.stable.ID.version %in% mir_dn),]
mir_dn## Gene.stable.ID Gene.stable.ID.version Gene.name
## 33471 ENSG00000207635 ENSG00000207635.1 MIR499A
## 49205 ENSG00000215991 ENSG00000215991.1 MIR208Bmir_dn$Gene.name <- tolower(mir_dn$Gene.name)
mir_dn## Gene.stable.ID Gene.stable.ID.version Gene.name
## 33471 ENSG00000207635 ENSG00000207635.1 mir499a
## 49205 ENSG00000215991 ENSG00000215991.1 mir208bDifferential analysis mRNA
ss <- data.frame(colnames(df_mrna_f))
rownames(ss) <- ss[,1]
colnames(ss) <- "samplename"
ss$trt <- factor(grepl("TGF",ss$samplename))
dds <- DESeqDataSetFromMatrix(countData = df_mrna_f , colData = ss, design = ~ trt )
res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz<- results(res)
vsd <- vst(dds, blind=FALSE,nsub=200)
zz <- cbind(as.data.frame(z),assay(vsd))
dge_mrna <- as.data.frame(zz[order(zz$pvalue),])
dge_mrna[1:20,1:6] %>%
kbl(caption = "Top mRNA gene expression differences between ctrl and TGFB1 treated cells") %>%
kable_paper("hover", full_width = F)| baseMean | log2FoldChange | lfcSE | stat | pvalue | padj | |
|---|---|---|---|---|---|---|
| ENSG00000175899 | 693.18892 | -3.0227028 | 0.3819312 | -7.914259 | 0.00e+00 | 0.0000000 |
| ENSG00000130203 | 23.58394 | -2.5336090 | 0.3990153 | -6.349654 | 0.00e+00 | 0.0000019 |
| ENSG00000140022 | 110.23686 | -1.6602078 | 0.2730986 | -6.079153 | 0.00e+00 | 0.0000071 |
| ENSG00000162849 | 296.52318 | 0.8694710 | 0.1496897 | 5.808489 | 0.00e+00 | 0.0000278 |
| ENSG00000155792 | 48.35853 | -1.3544237 | 0.2396370 | -5.651982 | 0.00e+00 | 0.0000559 |
| ENSG00000172005 | 27.16968 | -2.5036791 | 0.4766035 | -5.253170 | 1.00e-07 | 0.0004393 |
| ENSG00000183230 | 28.83016 | -2.9147741 | 0.5615835 | -5.190277 | 2.00e-07 | 0.0005288 |
| ENSG00000025039 | 192.64840 | -2.1110174 | 0.4248466 | -4.968893 | 7.00e-07 | 0.0013385 |
| ENSG00000203805 | 19.24304 | 1.6474490 | 0.3317431 | 4.966038 | 7.00e-07 | 0.0013385 |
| ENSG00000177354 | 168.52007 | -1.6862593 | 0.3491360 | -4.829807 | 1.40e-06 | 0.0024093 |
| ENSG00000102678 | 51.26376 | -2.1852803 | 0.4569946 | -4.781852 | 1.70e-06 | 0.0027836 |
| ENSG00000166091 | 16.35597 | -2.5529347 | 0.5438487 | -4.694200 | 2.70e-06 | 0.0039320 |
| ENSG00000155011 | 37.71857 | 2.5664640 | 0.5656626 | 4.537093 | 5.70e-06 | 0.0077344 |
| ENSG00000149256 | 539.39085 | 0.7748345 | 0.1732289 | 4.472894 | 7.70e-06 | 0.0090408 |
| ENSG00000120820 | 312.10446 | 0.7915045 | 0.1770330 | 4.470944 | 7.80e-06 | 0.0090408 |
| ENSG00000265727 | 260.91927 | 0.5588482 | 0.1258137 | 4.441871 | 8.90e-06 | 0.0090408 |
| ENSG00000197852 | 773.84275 | -0.5856260 | 0.1319888 | -4.436937 | 9.10e-06 | 0.0090408 |
| ENSG00000143028 | 157.63287 | -1.8189523 | 0.4101872 | -4.434444 | 9.20e-06 | 0.0090408 |
| ENSG00000130766 | 79.01247 | 0.9431118 | 0.2132849 | 4.421840 | 9.80e-06 | 0.0090802 |
| ENSG00000167470 | 278.70379 | -0.5306694 | 0.1212890 | -4.375247 | 1.21e-05 | 0.0106100 |
mrna_up <- rownames(subset(dge_mrna,padj<0.05 & log2FoldChange >0 ))
mrna_up## [1] "ENSG00000162849" "ENSG00000203805" "ENSG00000155011" "ENSG00000149256"
## [5] "ENSG00000120820" "ENSG00000265727" "ENSG00000130766" "ENSG00000254951"
## [9] "ENSG00000028277" "ENSG00000286458" "ENSG00000183049" "ENSG00000125430"
## [13] "ENSG00000128965" "ENSG00000157680" "ENSG00000250384" "ENSG00000243753"
## [17] "ENSG00000142871" "ENSG00000144802" "ENSG00000276012" "ENSG00000137809"
## [21] "ENSG00000278880"mrna_up <- gt[which(gt$Gene.stable.ID %in% mrna_up),]
mrna_up <- unique(mrna_up$Gene.name)
mrna_up <- mrna_up[mrna_up != ""]
mrna_up## [1] "RN7SL648P" "DKK2" "CHAC1" "ITGA11" "TENM4" "PLPP4"
## [7] "UBE2CP3" "SESN2" "DGKI" "HS3ST3B1" "CAMK1D" "KIF26B"
## [13] "HLA-L" "GLT8D2" "CCN1" "POU2F2" "NFKBIZ"mrna_dn <- rownames(subset(dge_mrna,padj<0.05 & log2FoldChange <0 ))
mrna_dn## [1] "ENSG00000175899" "ENSG00000130203" "ENSG00000140022" "ENSG00000155792"
## [5] "ENSG00000172005" "ENSG00000183230" "ENSG00000025039" "ENSG00000177354"
## [9] "ENSG00000102678" "ENSG00000166091" "ENSG00000197852" "ENSG00000143028"
## [13] "ENSG00000167470" "ENSG00000072954" "ENSG00000183520" "ENSG00000122574"
## [17] "ENSG00000077274" "ENSG00000101638" "ENSG00000168556" "ENSG00000197616"
## [21] "ENSG00000120669" "ENSG00000147255" "ENSG00000218416" "ENSG00000077009"
## [25] "ENSG00000100979" "ENSG00000170290" "ENSG00000163249" "ENSG00000228672"
## [29] "ENSG00000105088" "ENSG00000004848"mrna_dn <- gt[which(gt$Gene.stable.ID %in% mrna_dn),]
mrna_dn <- unique(mrna_dn$Gene.name)
mrna_dn <- mrna_dn[mrna_dn != ""]
mrna_dn## [1] "SOHLH2" "IGSF1" "ST8SIA5" "PLTP" "CMTM5" "ARX"
## [7] "CAPN6" "RRAGD" "SLN" "ING2" "MYH6" "FGF9"
## [13] "DEPTOR" "C10orf71" "UTP11" "A2M" "MAL" "CTNNA3"
## [19] "PROB1" "STON2" "WIPF3" "CCNYL1" "APOE" "OLFM2"
## [25] "MIDN" "NMRK2" "TMEM38A" "INKA2" "SYPL2"Fetch target gene database
#options(timeout=1000)
#download.file("https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2022/cache/download/9.0/hsa_MTI.xlsx",
#destfile="hsa_MTI.xlsx",)
mirtarbase <- read_excel("hsa_MTI.xlsx")
# mir up
mir_up_gn <- mir_up$Gene.name
mir_up_gn2 <- gsub("$","-",gsub("mir","hsa-miR-",mir_up_gn))
mir_up_gn2 <- gsub("a2","a",mir_up_gn2)
up_targets <- lapply(mir_up_gn2,function(x) {
a <- unique(unlist(as.vector(mirtarbase[grep(x,mirtarbase$miRNA),"Target Gene"])))
y <- gsub("-$","",x)
b <- unique(unlist(as.vector(mirtarbase[which(mirtarbase$miRNA %in% y),"Target Gene"])))
d <- unique(c(a,b))
return(d)
})
lapply(up_targets,length)## [[1]]
## [1] 314
##
## [[2]]
## [1] 679
##
## [[3]]
## [1] 115up_targets <- unique(unlist(up_targets))
up_targets## [1] "RHOA" "PPP2R2A" "LATS2" "FOXP3"
## [5] "SELE" "CASR" "YY1" "RET"
## [9] "NUMB" "NFAT5" "KLF13" "JAZF1"
## [13] "HOXC13" "ETS1" "ITGA5" "MPRIP"
## [17] "MMP16" "RDX" "CXCL12" "ARPC5"
## [21] "FZD3" "DMD" "TIAM1" "ICAM1"
## [25] "DKK1" "DACT3" "WASF3" "HIF1AN"
## [29] "SATB2" "PRKCE" "RASA1" "STK40"
## [33] "MCM2" "CDK1" "CREG1" "MLH1"
## [37] "MET" "INHBA" "RAN" "ACADVL"
## [41] "XK" "CIPC" "PPP2R5C" "MCM4"
## [45] "RICTOR" "C1orf198" "DBR1" "PPIA"
## [49] "ARL6IP5" "NUS1" "TNKS2" "SLC9A6"
## [53] "PPIL2" "ZC3H18" "RPS7" "MED12"
## [57] "CYP27B1" "HIST1H2BC" "PTPRJ" "AGO1"
## [61] "DNAAF5" "HIST1H2BK" "RPA1" "TXNDC5"
## [65] "AKAP8L" "FLNA" "ATP2A2" "NDFIP2"
## [69] "TRIB3" "NUP188" "RPS4Y1" "GLI2"
## [73] "RPL37A" "EXOSC5" "DDX19A" "UBA6"
## [77] "NOL9" "DNAJC5" "RETREG3" "PEX19"
## [81] "PPP4R3B" "TRRAP" "RANGAP1" "TNRC6B"
## [85] "NOP56" "PCSK1N" "ZNF275" "C2CD5"
## [89] "NFATC2IP" "HIST1H2BJ" "SRRM2" "GYG1"
## [93] "ZC3H12C" "LRRC59" "GHITM" "EDC3"
## [97] "MYO1D" "STOML2" "EXOC6" "HOXA7"
## [101] "SFXN1" "ILF2" "SRC" "MAP4K4"
## [105] "RAB27A" "TBXA2R" "EMSY" "SESN2"
## [109] "CRKL" "SP1" "ARID1A" "PLEKHB2"
## [113] "TMEM109" "ZNF805" "CHMP4B" "BACH1"
## [117] "SNRNP27" "NCBP2" "FOXC1" "YWHAE"
## [121] "BAHD1" "ABCB9" "RHOBTB1" "SP7"
## [125] "NUDT3" "C15orf52" "SLC16A9" "FOXD4"
## [129] "FOXD4L5" "FOXD4L4" "FOXD4L1" "MICA"
## [133] "PAPOLG" "PARP11" "ZDHHC6" "RBM38"
## [137] "HSPA6" "SPRED1" "SMG1" "SDC4"
## [141] "RNF111" "PHF12" "PDE4D" "TSPAN1"
## [145] "FBXL5" "DPM2" "ATP5A1" "ARF1"
## [149] "HOXD3" "MZT1" "MXRA7" "REXO2"
## [153] "POLR3E" "IL5RA" "RPL35A" "NF2"
## [157] "ZNF485" "INTU" "TXNIP" "CDC42SE1"
## [161] "KLHL15" "FRS2" "DCK" "RSRC1"
## [165] "ZNF587" "ZNF641" "SLC38A1" "BCAS4"
## [169] "PARP1" "FRK" "KIAA0391" "SFT2D2"
## [173] "FBXL7" "AR" "CNBP" "SLC30A5"
## [177] "ZNF71" "PPIC" "GUF1" "ZNF460"
## [181] "TOR1AIP1" "ZNF331" "C9orf64" "C19orf12"
## [185] "ECHDC1" "EPB41L4B" "TRAF1" "ZNF678"
## [189] "CCDC127" "ZIK1" "SYDE2" "SUPT7L"
## [193] "PYURF" "PUM2" "PTPDC1" "NUP43"
## [197] "LAPTM4A" "RAB18" "CBX3" "FZD1"
## [201] "GTF2E1" "CCNT1" "SPRTN" "CRK"
## [205] "SLC18B1" "RPL7L1" "PRPF38A" "QSER1"
## [209] "CKAP2L" "PAPLN" "GABRB1" "CDH13"
## [213] "SRPX2" "BLOC1S4" "LILRA2" "TET3"
## [217] "LIPG" "IPP" "PPM1L" "RPL27A"
## [221] "RPL12" "AGO2" "XPO6" "ACBD7"
## [225] "JARID2" "AKNA" "AFF1" "PNPT1"
## [229] "MAFF" "NLGN1" "PAX6" "DPYSL5"
## [233] "FAM193A" "MCMBP" "E2F2" "NECTIN4"
## [237] "XRCC5" "C1QTNF9" "DOCK1" "SMAD4"
## [241] "SGPP2" "SOX4" "BAP1" "IL25"
## [245] "SDHA" "SPRED2" "SPRY1" "SPRY3"
## [249] "SPRY4" "GNA13" "FOXO3" "STMN1"
## [253] "DICER1" "SLC1A2" "BRWD1" "LMNB2"
## [257] "ANKRD52" "AP2B1" "CASKIN2" "EFNB1"
## [261] "KLHDC10" "KMT2B" "MTSS1L" "NFE2L1"
## [265] "NFIC" "NPM1" "PHLDA1" "PRRC2B"
## [269] "RAB1B" "RAB5B" "SERTAD2" "SFN"
## [273] "VPS26B" "DNAJB4" "ZNF614" "FOXJ3"
## [277] "GIGYF1" "MAGEA12" "MAGEA3" "MAGEA6"
## [281] "OTUD4" "PLAGL2" "RASA4" "TAPBP"
## [285] "TMEM9" "ZBTB39" "NUCB1" "C17orf99"
## [289] "TMEM182" "KIAA1737" "HEATR2" "FAM134C"
## [293] "SMEK2" "C11orf30" "PVRL4" "HIF1A"
## [297] "GAS1" "App" "BACE1" "BMP2"
## [301] "RUNX2" "PTEN" "ACOX1" "MMP3"
## [305] "SLC20A1" "AGO3" "RASA2" "CELSR2"
## [309] "CGNL1" "TINCR" "WLS" "C14orf101"
## [313] "NXF1" "U2SURP" "NLK" "GATA6"
## [317] "CDX2" "PLAG1" "BCL2" "PROX1"
## [321] "KAT2B" "CDKN1B" "ZNF763" "DDIT4"
## [325] "ATM" "HIPK2" "BCL2L11" "HRAS"
## [329] "RNF2" "RALA" "SIRT1" "PRAP1"
## [333] "DUSP6" "PTPN11" "DUSP5" "PTPN22"
## [337] "FOS" "MTMR3" "KLF6" "MCL1"
## [341] "XIAP" "GPR78" "NANOG" "LFNG"
## [345] "LRRC17" "CHRFAM7A" "CD46" "RASSF6"
## [349] "FXYD6" "KCTD3" "TSHR" "ZNF558"
## [353] "C8A" "ARL6IP6" "ZNF426" "ATF7IP2"
## [357] "PRR4" "TCF21" "PHOX2A" "PLPBP"
## [361] "HACD3" "GSTM2" "FSIP1" "KBTBD3"
## [365] "PTPRZ1" "WNT3A" "TUSC1" "LRRN3"
## [369] "TMEM45A" "ARF6" "C1orf109" "TAF15"
## [373] "PLXDC2" "NMRK2" "WNT2" "ATG10"
## [377] "PRDX3" "ZNF652" "RTEL1-TNFRSF6B" "GCNT1"
## [381] "PCDHB8" "ENAH" "ZNF25" "S100A1"
## [385] "PLA2G4C" "NOL4" "SIX6" "FKBP10"
## [389] "SMCHD1" "OR11A1" "INCENP" "LPGAT1"
## [393] "CLUAP1" "LYSMD3" "CCDC6" "BAG2"
## [397] "GPR83" "PTGS2" "ANKRD13C" "RLF"
## [401] "FBXO28" "ZNF350" "TIAL1" "RNF34"
## [405] "LCLAT1" "JCAD" "ZNF35" "PITPNB"
## [409] "SCD" "H3F3B" "GATAD2B" "LGALSL"
## [413] "TGIF2" "MOB1A" "SLC35B4" "FAM160A2"
## [417] "NUP58" "GPRIN3" "H1F0" "ARHGAP12"
## [421] "SPRY2" "TGFBR3" "TMED4" "MAP2K1"
## [425] "PUM1" "TRIM2" "FBXO33" "NRP1"
## [429] "FAM47B" "CCNG1" "BRMS1L" "OTUD1"
## [433] "ATP6V0E1" "WNT16" "CST5" "SH3BGRL"
## [437] "GPR137B" "OFCC1" "IQCG" "NKX3-2"
## [441] "OTX2" "ROPN1L" "TMEM14A" "TAF2"
## [445] "IDS" "FRA10AC1" "COL27A1" "EPHA5"
## [449] "DCST1" "ZNF562" "EYA4" "CHL1"
## [453] "TAAR6" "SLCO2A1" "HMGB2" "HERC3"
## [457] "BTBD3" "SRPK2" "DNAJC7" "ANKRD1"
## [461] "CFI" "MRPS14" "HEY2" "MTMR12"
## [465] "ACOT12" "PCLAF" "USP28" "AMMECR1"
## [469] "BPGM" "DSCR8" "UGT3A1" "HSD17B3"
## [473] "GADD45G" "FBXO34" "KLRC4" "MOB3B"
## [477] "FKBP7" "TBX4" "TMPRSS11A" "SNAI2"
## [481] "SLC7A11" "NUDT12" "COPS2" "ZNF12"
## [485] "PRLR" "PLCL2" "ZNF594" "METAP1"
## [489] "HSPA13" "NR6A1" "YOD1" "SLC37A3"
## [493] "FBXO11" "ZNF445" "TM9SF3" "ATP8A1"
## [497] "TMEM64" "MOB1B" "GNAI3" "TAB2"
## [501] "SRSF7" "DDX3X" "KRAS" "LBR"
## [505] "KLHL42" "TMEM132B" "AFTPH" "ZNF148"
## [509] "NOTCH2" "NFYB" "NOTCH1" "HOOK3"
## [513] "SIK2" "FAM222B" "RPS8" "STAG2"
## [517] "PFKFB2" "ZEB2" "MAZ" "RPL14"
## [521] "KCTD2" "UBA2" "DDX27" "FAT1"
## [525] "HDAC6" "TMEM192" "LAMA3" "HUWE1"
## [529] "ND2" "HNRNPAB" "OCA2" "AP1M1"
## [533] "UCHL1" "PGD" "ZFP36L2" "AKAP12"
## [537] "PABPC1" "GANAB" "PHPT1" "H2AFY"
## [541] "TEAD4" "BRCA1" "MTCL1" "KIAA0100"
## [545] "PPP1R9A" "MGAT5" "TNIP1" "PBX3"
## [549] "TIMP1" "PGR" "COL16A1" "PPP3CA"
## [553] "ATG5" "CD4" "TGFBRAP1" "TGFBR1"
## [557] "TNFRSF11B" "PCDHAC1" "PCDHAC2" "PCDHA1"
## [561] "PCDHA10" "PCDHA11" "PCDHA12" "PCDHA13"
## [565] "PCDHA2" "PCDHA3" "PCDHA4" "PCDHA5"
## [569] "PCDHA6" "PCDHA7" "PCDHA8" "PDGFRA"
## [573] "BMP3" "SOX5" "MAP3K3" "TAB3"
## [577] "PDAP1" "MAPK1IP1L" "BMPR2" "SMAD2"
## [581] "MADD" "ACAN" "MAP3K10" "PCDHB6"
## [585] "MMP14" "E2F5" "RAP1B" "C12orf29"
## [589] "NHLRC3" "CCNK" "PAPD5" "CARM1"
## [593] "LDLR" "C2orf69" "44806" "TNPO1"
## [597] "PTBP3" "CUL5" "PRRC2C" "PEBP1"
## [601] "PNRC2" "IPO5" "RCOR1" "KIF2C"
## [605] "CHD9" "LRRC8D" "ZNF136" "KIF3B"
## [609] "TTPAL" "NCOA3" "FNDC3B" "PRKCD"
## [613] "SLC25A37" "CHCHD7" "ZBTB33" "GRK2"
## [617] "ELK4" "ZNF268" "ZFAND6" "LMAN1"
## [621] "MRPL34" "PDIA6" "CHMP2B" "DRAM1"
## [625] "TMEM94" "ZNF440" "ZNF439" "NSD2"
## [629] "KIAA1551" "PMAIP1" "FKBP1A" "HIGD2A"
## [633] "ID4" "DAZAP2" "ZFP36L1" "SLC35G2"
## [637] "MTX3" "ABCG2" "PHACTR2" "SASH1"
## [641] "MOSPD1" "SRGAP1" "UBL3" "PHACTR4"
## [645] "EREG" "TERT" "RGS5" "IFNG"
## [649] "AHR" "STAT3" "WIF1" "TWIST1"
## [653] "MAPK1" "CDKL2" "DCAF4" "RSF1"
## [657] "FAM96A" "CAMK4" "VPS4A" "VMP1"
## [661] "UBN2" "SLC25A25" "RPRD2" "PPP2R5E"
## [665] "KLHDC3" "MIGA2" "CBX4" "CAPRIN2"
## [669] "ARL5C" "ZFAND2B" "TAOK1" "FKBP1C"
## [673] "ZNF83" "HSPA1B" "ZNF669" "ZNF788"
## [677] "ZNF781" "ZNF667" "ZNF487" "DGS2"
## [681] "EPS8" "MAN1A2" "FKBP14" "ZNF791"
## [685] "ZFP69B" "NPM3" "TCF23" "HSP90B1"
## [689] "CTC1" "GJB7" "ZNF138" "WT1"
## [693] "ZDHHC15" "XPNPEP3" "MTPAP" "NOX5"
## [697] "ZNF699" "TXLNG" "TRPC5" "TBL1XR1"
## [701] "SEC61A2" "PHOX2B" "PHC3" "MKL2"
## [705] "HEPHL1" "COG5" "ATP8B1" "ANKRD50"
## [709] "PLPP3" "ZNF844" "ZNF780B" "NCOA7"
## [713] "ZNF266" "KRBOX4" "SCAMP2" "TMCC1"
## [717] "FOXL1" "ZNF107" "SPIRE1" "SMU1"
## [721] "RNF6" "HMGA2" "CSNK1A1" "ARSJ"
## [725] "ARPP19" "ZNF415" "ZNF616" "SUV39H2"
## [729] "FAM3C" "ZNF846" "ZNF23" "TUBB2A"
## [733] "MIER3" "GTPBP3" "IRAK1BP1" "SURF6"
## [737] "THYN1" "FADS6" "RABGEF1" "PPP3CB"
## [741] "MYO1C" "DMRT2" "IL17REL" "SLC35G3"
## [745] "GRAMD1B" "UNC5B" "REPS1" "NOS1AP"
## [749] "ZC3HAV1L" "RPS6KA3" "AP5M1" "TNFRSF13B"
## [753] "ATP1A3" "ZNF829" "WDR72" "HFM1"
## [757] "SPTLC3" "ARFIP2" "SCN8A" "SMCR8"
## [761] "ATXN7" "TLDC1" "RBM25" "TRAFD1"
## [765] "ZADH2" "WDR13" "PRR11" "GJD3"
## [769] "AP3M2" "ZNF556" "ZNF852" "MUC20"
## [773] "PRRG4" "TIMM10B" "ZNF253" "DNAJC3"
## [777] "TK1" "WNK1" "TOPBP1" "TEF"
## [781] "RNMT" "ADAM17" "RSBN1L" "DYNLL2"
## [785] "CFHR3" "KLHL24" "PDK3" "ZNF419"
## [789] "RNF187" "ZNF597" "CENPO" "FAM13A"
## [793] "LAIR1" "RLIM" "CYP1B1" "MAPK14"
## [797] "VCAM1" "PNMA8B" "LAPTM4B" "PGK1"
## [801] "MTHFD1L" "MYLK" "CHST4" "ZNF490"
## [805] "ZNF449" "PNKD" "EPRS" "TEAD1"
## [809] "KRTAP21-2" "MSX2" "EN2" "ZNF664"
## [813] "ZNF121" "ZBTB4" "ULK1" "TUBB"
## [817] "TSG101" "TMF1" "TMEM30A" "TFRC"
## [821] "TBC1D7" "TBC1D13" "STX2" "STAG1"
## [825] "SSX2IP" "SRGN" "SORT1" "SLC7A1"
## [829] "SLC38A2" "SLC19A2" "SLC10A7" "SIPA1L1"
## [833] "SHOC2" "RP2" "RHOG" "RGS16"
## [837] "RAB2B" "PURB" "PGAP1" "PER2"
## [841] "PBRM1" "OSBPL3" "NMT2" "FAM192A"
## [845] "NIN" "NCAPG" "NAA50" "MTUS1"
## [849] "MPP5" "KMT2E" "LPCAT1" "LONRF1"
## [853] "KPNA1" "EFCAB14" "WASHC5" "KDM5A"
## [857] "INO80D" "IL1A" "HIST1H3D" "HECW2"
## [861] "GOT1" "GOLGA8B" "GOLGA1" "GNS"
## [865] "GK5" "G3BP2" "FSD1L" "CCNQ"
## [869] "EPS15" "EED" "DYNC1LI2" "DDX52"
## [873] "CPOX" "CPEB4" "CLCC1" "CCL22"
## [877] "CCDC88C" "ELMSAN1" "GSKIP" "BLOC1S2"
## [881] "BAZ2A" "ATP2B1" "ATG2B" "ASB1"
## [885] "ARRDC3" "ARRB2" "APOL6" "ALDH9A1"
## [889] "AFF4" "ACYP1" "CTDSPL" "TUSC3"
## [893] "MEG3" "PHLPP2" "GPD1L" "ALDH1A1"
## [897] "CDKN1A" "BAX" "RASSF1" "INPP4B"
## [901] "CTNNB1" "TCF4" "VEGFA" "PRKN"
## [905] "EGR1" "NRAS" "RUNX1" "CEBPA"
## [909] "SAMHD1" "REPIN1" "SLC7A5" "ZC3H7B"
## [913] "ARHGDIA" "MARK2" "ADCY9" "DCBLD2"
## [917] "RPL13A" "FAM27E2" "NSG2" "OCIAD2"
## [921] "PSD" "SUFU" "CADPS2" "PRAMEF11"
## [925] "PRAMEF15" "PRAMEF26" "PRAMEF4" "PRAMEF9"
## [929] "ATXN2" "BASP1" "HNRNPH1" "WDFY3"
## [933] "BDNF" "HOXA11" "ESR1" "PROSC"
## [937] "PTPLAD1" "TAF6L" "KIAA1462" "NUPL1"
## [941] "TMEM257" "KIAA0101" "SOGA2" "C12ORF29"
## [945] "C2ORF69" "ADRBK1" "KIAA0195" "WHSC1"
## [949] "FAM73B" "ZNF487P" "PPAP2B" "PNMAL2"
## [953] "KIAA0196" "FAM58A" "OCLN" "TFAM"
## [957] "SFRP4" "EGFR" "ZHX2" "POSTN"
## [961] "CDH11" "SERPINB1" "RB1" "CCND1"
## [965] "CARD11" "LINC00703" "BMP10" "AGO4"
## [969] "DNMT3A" "CBLB" "CCAT1" "MECP2"
## [973] "CELSR3" "ITGA6" "LGI2" "IGF1R"
## [977] "SEC24A" "MYC" "CALU" "EDN1"
## [981] "PEX11B" "RRM2" "SGCD" "ZNF774"
## [985] "OLR1" "NUCB2" "RECK" "AGTRAP"
## [989] "LYPLA2" "GLO1" "NECAB3" "ZNRF3"
## [993] "ZNF385A" "YWHAZ" "XPOT" "SP2"
## [997] "SMARCD1" "SLC5A6" "RNF126" "PANK3"
## [1001] "KMT2D" "KLHDC8B" "KIF3A" "KIAA0930"
## [1005] "KCTD21" "IL2RB" "GM2A" "FAM83G"
## [1009] "EFHD2" "E2F3" "CFL2" "BSCL2"
## [1013] "ATG9A" "AREL1" "ABHD2" "ABHD14B"
## [1017] "GNB1L" "FAM71B" "SNX19" "LASP1"
## [1021] "LAMA5" "HSDL1" "NAV1" "RAB11FIP4"
## [1025] "PLCG2" "RBFOX2" "ZNF644" "DIABLO"
## [1029] "C12orf49" "DIRAS2" "CTSB" "DVL3"
## [1033] "LEFTY1" "NUP155" "CBX5" "SURF4"
## [1037] "NCS1" "TTLL7" "EIF2S3" "SNTN"
## [1041] "RAX" "ARL5A" "C10orf111" "ZNF277"
## [1045] "PTPN3" "IPO9" "KCNMB1" "ASGR2"
## [1049] "CTDSP1" "CXCL8" "FGF19" "KDM2A"
## [1053] "MRPL44" "MSX1" "MYH9" "P4HB"
## [1057] "POLL" "PPIB" "SRF" "SUSD1"
## [1061] "TOR2A" "TRAPPC10" "CLDN12" "EIF5AL1"
## [1065] "ESCO1" "HARBI1" "IFITM3" "MEAF6"
## [1069] "NR2F6" "ZBTB34" "REEP3" "SCYL3"
## [1073] "ENO1"length(up_targets)## [1] 1073# mir dn
mir_dn_gn <- mir_dn$Gene.name
mir_dn_gn2 <- gsub("$","-",gsub("mir","hsa-miR-",mir_dn_gn))
dn_targets <- lapply(mir_dn_gn2,function(x) {
a <- unique(unlist(as.vector(mirtarbase[grep(x,mirtarbase$miRNA),"Target Gene"])))
y <- gsub("-$","",x)
b <- unique(unlist(as.vector(mirtarbase[which(mirtarbase$miRNA %in% y),"Target Gene"])))
d <- unique(c(a,b))
return(d)
})
lapply(dn_targets,length)## [[1]]
## [1] 139
##
## [[2]]
## [1] 169dn_targets <- unique(unlist(dn_targets))
dn_targets## [1] "SOX6" "FOXO4" "PDCD4" "ETS1" "PMAIP1" "TXNIP"
## [7] "ETNK1" "TGOLN2" "ADO" "NAPG" "IGFBP4" "MTDH"
## [13] "DR1" "ABLIM1" "IQGAP2" "MLANA" "METTL7A" "EPC2"
## [19] "CELF1" "STYX" "ZNF844" "ZNF460" "APOBEC3F" "VPS35"
## [25] "TSC22D3" "SSRP1" "SIKE1" "PTGFRN" "PAQR5" "LIN54"
## [31] "LIMA1" "LIFR" "TYRP1" "MYO1D" "ZSCAN16" "SUGT1"
## [37] "MAF" "SLC25A12" "SPTSSA" "POM121C" "KPNA5" "WDR76"
## [43] "UQCRB" "RAB5C" "KLHL15" "HIST1H3B" "EIF4A2" "HIST1H2AD"
## [49] "DCAF12L2" "C11orf54" "MC2R" "SRRM1" "PROSER2" "RPS4Y1"
## [55] "CD93" "INIP" "CAPRIN2" "ASAP3" "GDE1" "PPP6C"
## [61] "NAT8L" "AXIN2" "MDM2" "TRIM59" "YTHDF1" "NT5C3A"
## [67] "PGAM4" "FOXO1" "FIGN" "FEM1B" "GLO1" "QRFPR"
## [73] "UBN2" "UBE2V2" "SLC7A2" "PTMA" "GXYLT1" "EIF2AK4"
## [79] "FXYD6" "ACOT2" "BTLA" "CACNA1B" "ELK4" "ARHGAP12"
## [85] "TFAM" "MAPK1" "BMPR1A" "DGKG" "DNAH9" "BMP10"
## [91] "WWC2" "DNAH8" "UBN1" "EEF1E1" "TBC1D22A" "CFAP65"
## [97] "MAPK10" "NRIP1" "PRR13" "SRXN1" "UTP18" "TEF"
## [103] "PDS5A" "FGD4" "RTL8C" "PDE12" "NLN" "CSTF1"
## [109] "RARS2" "VAV3" "MEF2C" "CXCL8" "DAZAP2" "PLEKHG5"
## [115] "RNMT" "CDKN1A" "MAMLD1" "MDM4" "ASAP1" "BCL2L11"
## [121] "HNRNPC" "44626" "TMEM178B" "TRAF3IP1" "WASHC2C" "ZNF107"
## [127] "LEP" "PSMB9" "MAML3" "TTF2" "CCDC108" "FAM127A"
## [133] "KCNN3" "ALDH1L2" "CUL1" "KIF13A" "LUC7L" "NR5A1"
## [139] "ICE2" "FBXO28" "PI4K2B" "RNF11" "SKIL" "CBX3"
## [145] "RACGAP1" "TPBG" "SP8" "BRCC3" "PPP2R1B" "TUBB2A"
## [151] "CYP1B1" "FAM199X" "SNAP25" "RAPH1" "RPLP1" "GAS1"
## [157] "ANP32B" "RAP1B" "CEP97" "TBRG1" "SLC28A2" "HDHD2"
## [163] "STXBP5L" "SAR1B" "RNF125" "LRRC8A" "JMJD1C" "MYLIP"
## [169] "HMGN2" "ZFP36L1" "PRR23A" "KYAT3" "LNPK" "LRIG3"
## [175] "KPNA3" "DSN1" "ARSK" "SOCS5" "MAP3K5" "SINHCAF"
## [181] "CPEB2" "SYNCRIP" "SON" "BTG2" "KIF21A" "ATP8B4"
## [187] "UFM1" "SRRM4" "BUD23" "FOXP1" "KIAA1210" "UNKL"
## [193] "ATF6" "TPGS1" "ACO1" "RWDD1" "PCDH19" "SLC2A6"
## [199] "SLC35G1" "PAPSS2" "TCN2" "CCT4" "PCDHB11" "FSIP2"
## [205] "CARNMT1" "PKHD1" "PTAFR" "OSTM1" "CLEC17A" "RABGEF1"
## [211] "LRIG1" "FAM9C" "FBLIM1" "ZDHHC20" "UBFD1" "SUMO2"
## [217] "SIN3A" "SENP1" "S1PR1" "RYK" "PPT1" "PPM1F"
## [223] "OLFML2A" "HNRNPDL" "TSC22D2" "CAV1" "HEY2" "MTRF1L"
## [229] "PINX1" "MAK16" "HOMEZ" "ZSWIM9" "KCTD20" "CAPZA2"
## [235] "MIA3" "TRAPPC6B" "IBTK" "ADAMTS15" "CACNA1C" "CACNB2"
## [241] "Qk" "MSTN" "QKI" "AMBRA1" "TUBB" "GPATCH3"
## [247] "RBPJ" "SLIT3" "SPRY4" "ARHGEF39" "GNG5" "CDH2"
## [253] "HMGN3" "LCOR" "RAB11FIP4" "ZNF688" "NUDCD3" "CCBL2"
## [259] "KIAA1715" "FAM60A" "WBSCR22" "C9orf41" "C19orf68" "ADGRL2"
## [265] "STK26"length(dn_targets)## [1] 265miR target Enrichment with FORA
reactome <- gmtPathways("ReactomePathways_2023-11-28.gmt")
kegg <- gmtPathways("c2.cp.kegg_medicus.v2023.2.Hs.symbols.gmt")
summary(unlist(lapply(reactome,length)))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 15.00 49.36 44.00 2607.00summary(unlist(lapply(kegg,length)))## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.00 8.00 13.00 15.59 19.00 92.00# make a gene list - some of the ensembl IDs dont have a symbol (~2000 of ~17000)
gl<-gt[gt$Gene.stable.ID %in% rownames(df_mrna_f),"Gene.name"]
# make a non-redundant list of gene symbols
bg <- unique(gl[which(gl!="")])
length(gl)## [1] 17628length(bg)## [1] 15265First try with whole genome as background.
wgbg <- unique(gt$Gene.name)
wgbg <- wgbg[which(wgbg!="")]
setdiff(up_targets,wgbg)## [1] "HIST1H2BC" "HIST1H2BK" "HIST1H2BJ" "C15orf52" "ATP5A1" "KIAA0391"
## [7] "MTSS1L" "KIAA1737" "HEATR2" "FAM134C" "SMEK2" "C11orf30"
## [13] "PVRL4" "App" "C14orf101" "H3F3B" "FAM160A2" "H1F0"
## [19] "ND2" "H2AFY" "PAPD5" "44806" "KIAA1551" "FAM96A"
## [25] "ZNF788" "DGS2" "MKL2" "TLDC1" "EPRS" "FAM192A"
## [31] "MPP5" "HIST1H3D" "ELMSAN1" "FAM27E2" "PROSC" "PTPLAD1"
## [37] "KIAA1462" "NUPL1" "TMEM257" "KIAA0101" "SOGA2" "C12ORF29"
## [43] "C2ORF69" "ADRBK1" "KIAA0195" "WHSC1" "FAM73B" "ZNF487P"
## [49] "PPAP2B" "PNMAL2" "KIAA0196" "FAM58A" "CCAT1" "C12orf49"
## [55] "C10orf111"length(setdiff(up_targets,wgbg))## [1] 55length(setdiff(up_targets,wgbg))/length(up_targets)## [1] 0.05125815up_wg_res <- fora(pathways=reactome, genes=up_targets, universe=wgbg, minSize = 5)## Warning in fora(pathways = reactome, genes = up_targets, universe = wgbg, : Not
## all of the input genes belong to the universe, such genes were removednbg = length(wgbg)
nsel = length(intersect(up_targets,wgbg))
up_wg_res$foldenrichment <- (up_wg_res$overlap / nsel ) / ( up_wg_res$size / nbg )
head(up_wg_res)## pathway pval padj overlap size
## 1: Signal Transduction 3.531590e-62 7.052585e-59 227 2588
## 2: Gene expression (Transcription) 1.549856e-56 1.547531e-53 165 1528
## 3: Generic Transcription Pathway 2.491551e-53 1.658542e-50 144 1233
## 4: RNA Polymerase II Transcription 1.912269e-52 9.547004e-50 150 1359
## 5: Disease 1.091849e-42 4.360845e-40 158 1791
## 6: Immune System 1.435853e-35 4.778998e-33 161 2118
## overlapGenes foldenrichment
## 1: ADAM17,ADCY9,AGO1,AGO2,AGO3,AGO4,... 3.416394
## 2: AFF4,AGO1,AGO2,AGO3,AGO4,AKAP8L,... 4.205978
## 3: AGO1,AGO2,AGO3,AGO4,AR,ARID1A,... 4.548894
## 4: AFF4,AGO1,AGO2,AGO3,AGO4,AR,... 4.299106
## 5: ACAN,ADAM17,ADCY9,AGTRAP,AP1M1,AP2B1,... 3.436116
## 6: ADAM17,AP1M1,AP2B1,AREL1,ARF1,ARPC5,... 2.960781nrow(up_wg_res)## [1] 1997nrow(subset(up_wg_res,padj<0.05 & foldenrichment >2))## [1] 661up_wg_res_sets <- subset(up_wg_res,padj<0.05 & foldenrichment >2)$pathway
setdiff(dn_targets,wgbg)## [1] "HIST1H3B" "HIST1H2AD" "44626" "CCDC108" "FAM127A" "Qk"
## [7] "CCBL2" "KIAA1715" "FAM60A" "WBSCR22" "C9orf41" "C19orf68"length(setdiff(dn_targets,wgbg))## [1] 12length(setdiff(dn_targets,wgbg))/length(dn_targets)## [1] 0.04528302dn_wg_res <- fora(pathways=reactome, genes=dn_targets, universe=wgbg, minSize = 5)## Warning in fora(pathways = reactome, genes = dn_targets, universe = wgbg, : Not
## all of the input genes belong to the universe, such genes were removednbg = length(wgbg)
nsel = length(intersect(dn_targets,wgbg))
dn_wg_res$foldenrichment <- (dn_wg_res$overlap / nsel ) / ( dn_wg_res$size / nbg )
head(dn_wg_res)## pathway pval padj overlap size
## 1: Metabolism of proteins 8.933815e-13 1.163718e-09 43 1934
## 2: Disease 1.301879e-12 1.163718e-09 41 1791
## 3: Signal Transduction 1.748199e-12 1.163718e-09 50 2588
## 4: Gene expression (Transcription) 1.615781e-11 8.066785e-09 36 1528
## 5: RNA Polymerase II Transcription 5.674276e-11 2.105760e-08 33 1359
## 6: Immune System 6.326771e-11 2.105760e-08 42 2118
## overlapGenes foldenrichment
## 1: ADAMTS15,ARSK,AXIN2,BRCC3,CAPZA2,CCT4,... 3.484541
## 2: ADAMTS15,BCL2L11,CAV1,CDKN1A,CUL1,CYP1B1,... 3.587748
## 3: ARHGAP12,ARHGEF39,AXIN2,BCL2L11,BMP10,BMPR1A,... 3.027885
## 4: BCL2L11,BTG2,CAV1,CBX3,CDKN1A,CSTF1,... 3.692434
## 5: BCL2L11,BTG2,CAV1,CBX3,CDKN1A,CSTF1,... 3.805644
## 6: ATP8B4,BCL2L11,BTLA,CAPZA2,CD93,CDKN1A,... 3.107828nrow(dn_wg_res)## [1] 1997nrow(subset(dn_wg_res,padj<0.05 & foldenrichment >2))## [1] 155dn_wg_res_sets <- subset(dn_wg_res,padj<0.05 & foldenrichment >2)$pathwayNow try with proper background.
setdiff(up_targets,bg)## [1] "FOXP3" "SELE" "CASR" "RET"
## [5] "XK" "HIST1H2BC" "HIST1H2BK" "TXNDC5"
## [9] "PCSK1N" "HIST1H2BJ" "HOXA7" "TBXA2R"
## [13] "SP7" "NUDT3" "C15orf52" "FOXD4"
## [17] "FOXD4L5" "FOXD4L4" "FOXD4L1" "HSPA6"
## [21] "TSPAN1" "ATP5A1" "HOXD3" "IL5RA"
## [25] "KIAA0391" "PYURF" "GABRB1" "LILRA2"
## [29] "C1QTNF9" "IL25" "MTSS1L" "PRRC2B"
## [33] "SFN" "MAGEA12" "MAGEA3" "MAGEA6"
## [37] "C17orf99" "KIAA1737" "HEATR2" "FAM134C"
## [41] "SMEK2" "C11orf30" "PVRL4" "App"
## [45] "MMP3" "TINCR" "C14orf101" "CDX2"
## [49] "ZNF763" "PRAP1" "NANOG" "CHRFAM7A"
## [53] "RASSF6" "TSHR" "C8A" "TCF21"
## [57] "PHOX2A" "PTPRZ1" "WNT3A" "WNT2"
## [61] "RTEL1-TNFRSF6B" "S100A1" "NOL4" "SIX6"
## [65] "OR11A1" "H3F3B" "FAM160A2" "H1F0"
## [69] "FAM47B" "WNT16" "CST5" "OFCC1"
## [73] "OTX2" "ROPN1L" "DCST1" "CHL1"
## [77] "TAAR6" "SLCO2A1" "CFI" "HEY2"
## [81] "ACOT12" "DSCR8" "UGT3A1" "HSD17B3"
## [85] "KLRC4" "TBX4" "TMPRSS11A" "PRLR"
## [89] "MAZ" "ND2" "OCA2" "H2AFY"
## [93] "PGR" "PCDHAC1" "PCDHAC2" "PCDHA1"
## [97] "PCDHA10" "PCDHA11" "PCDHA12" "PCDHA13"
## [101] "PCDHA2" "PCDHA3" "PCDHA4" "PCDHA5"
## [105] "PCDHA6" "PCDHA7" "PCDHA8" "BMP3"
## [109] "PAPD5" "44806" "KIAA1551" "TERT"
## [113] "IFNG" "WIF1" "CDKL2" "FAM96A"
## [117] "ARL5C" "ZNF788" "DGS2" "TCF23"
## [121] "GJB7" "PHOX2B" "MKL2" "FADS6"
## [125] "DMRT2" "IL17REL" "SLC35G3" "TNFRSF13B"
## [129] "ATP1A3" "WDR72" "TLDC1" "GJD3"
## [133] "PRRG4" "CFHR3" "LAIR1" "PNMA8B"
## [137] "CHST4" "EPRS" "KRTAP21-2" "FAM192A"
## [141] "MPP5" "IL1A" "HIST1H3D" "CCL22"
## [145] "ELMSAN1" "CEBPA" "FAM27E2" "NSG2"
## [149] "PSD" "PRAMEF11" "PRAMEF15" "PRAMEF26"
## [153] "PRAMEF4" "PRAMEF9" "PROSC" "PTPLAD1"
## [157] "KIAA1462" "NUPL1" "TMEM257" "KIAA0101"
## [161] "SOGA2" "C12ORF29" "C2ORF69" "ADRBK1"
## [165] "KIAA0195" "WHSC1" "FAM73B" "ZNF487P"
## [169] "PPAP2B" "PNMAL2" "KIAA0196" "FAM58A"
## [173] "CARD11" "LINC00703" "BMP10" "CCAT1"
## [177] "LGI2" "OLR1" "IL2RB" "BSCL2"
## [181] "GNB1L" "FAM71B" "C12orf49" "DIRAS2"
## [185] "LEFTY1" "SNTN" "RAX" "C10orf111"
## [189] "ASGR2" "CXCL8" "FGF19" "TOR2A"length(setdiff(up_targets,bg))## [1] 192length(intersect(up_targets,bg))## [1] 881length(up_targets)## [1] 1073length(setdiff(up_targets,bg))/length(up_targets)## [1] 0.1789376up_bg_res <- fora(pathways=reactome, genes=up_targets, universe=bg, minSize = 5)## Warning in fora(pathways = reactome, genes = up_targets, universe = bg, : Not
## all of the input genes belong to the universe, such genes were removednbg = length(bg)
nsel = length(intersect(up_targets,bg))
up_bg_res$foldenrichment <- (up_bg_res$overlap / nsel ) / ( up_bg_res$size / nbg )
head(up_bg_res)## pathway pval padj overlap
## 1: Signal Transduction 9.620197e-22 1.727787e-18 204
## 2: Generic Transcription Pathway 2.478318e-19 2.225529e-16 137
## 3: Gene expression (Transcription) 1.036131e-18 6.202970e-16 158
## 4: RNA Polymerase II Transcription 8.491643e-18 3.812748e-15 143
## 5: Disease 1.652687e-15 5.936451e-13 150
## 6: Signaling by Receptor Tyrosine Kinases 3.576543e-14 1.070579e-11 66
## size overlapGenes foldenrichment
## 1: 1832 ADAM17,ADCY9,AGO1,AGO2,AGO3,AGO4,... 1.929415
## 2: 1076 AGO1,AGO2,AGO3,AGO4,AR,ARID1A,... 2.206120
## 3: 1349 AFF4,AGO1,AGO2,AGO3,AGO4,AKAP8L,... 2.029392
## 4: 1193 AFF4,AGO1,AGO2,AGO3,AGO4,AR,... 2.076904
## 5: 1357 ACAN,ADAM17,ADCY9,AGTRAP,AP1M1,AP2B1,... 1.915280
## 6: 414 ADAM17,AP2B1,ARF6,ATP6V0E1,BAX,BDNF,... 2.762260nrow(up_bg_res)## [1] 1796nrow(subset(up_bg_res,padj<0.05 & foldenrichment >2))## [1] 227up_bg_res_sets <- subset(up_bg_res,padj<0.05 & foldenrichment >2)$pathway
setdiff(dn_targets,bg)## [1] "IQGAP2" "TYRP1" "HIST1H3B" "HIST1H2AD" "DCAF12L2" "MC2R"
## [7] "CD93" "QRFPR" "BTLA" "CACNA1B" "DNAH9" "BMP10"
## [13] "DNAH8" "CFAP65" "SRXN1" "CXCL8" "44626" "LEP"
## [19] "CCDC108" "FAM127A" "NR5A1" "SP8" "SNAP25" "PRR23A"
## [25] "SRRM4" "KIAA1210" "TPGS1" "PTAFR" "CLEC17A" "HEY2"
## [31] "Qk" "CCBL2" "KIAA1715" "FAM60A" "WBSCR22" "C9orf41"
## [37] "C19orf68"length(setdiff(dn_targets,bg))## [1] 37length(intersect(dn_targets,bg))## [1] 228length(dn_targets)## [1] 265length(setdiff(dn_targets,bg))/length(dn_targets)## [1] 0.1396226dn_bg_res <- fora(pathways=reactome, genes=dn_targets, universe=bg, minSize = 5)## Warning in fora(pathways = reactome, genes = dn_targets, universe = bg, : Not
## all of the input genes belong to the universe, such genes were removednbg = length(bg)
nsel = length(intersect(dn_targets,bg))
dn_bg_res$foldenrichment <- (dn_bg_res$overlap / nsel ) / ( dn_bg_res$size / nbg )
head(dn_bg_res)## pathway pval padj
## 1: FOXO-mediated transcription 1.261597e-06 0.002265829
## 2: FOXO-mediated transcription of cell cycle genes 2.717299e-06 0.002440135
## 3: Cytokine Signaling in Immune system 6.682545e-05 0.040006169
## 4: Disease 1.132751e-04 0.045194440
## 5: Regulation of beta-cell development 1.258197e-04 0.045194440
## 6: NOTCH2 intracellular domain regulates transcription 2.584770e-04 0.077370788
## overlap size overlapGenes foldenrichment
## 1: 8 54 BCL2L11,CAV1,CDKN1A,FOXO1,FOXO4,MSTN,... 9.918778
## 2: 5 16 CAV1,CDKN1A,FOXO1,FOXO4,MSTN 20.922423
## 3: 21 543 BCL2L11,CDKN1A,CUL1,EIF4A2,FOXO1,HNRNPDL,... 2.589294
## 4: 38 1357 ADAMTS15,BCL2L11,CAV1,CDKN1A,CUL1,CYP1B1,... 1.874846
## 5: 4 18 FOXO1,MAML3,MAMLD1,RBPJ 14.878168
## 6: 3 9 MAML3,MAMLD1,RBPJ 22.317251nrow(dn_bg_res)## [1] 1796nrow(subset(dn_bg_res,padj<0.05 & foldenrichment >2))## [1] 4dn_bg_res_sets <- subset(dn_bg_res,padj<0.05 & foldenrichment >2)$pathwayv1 <- list("WG up"=up_wg_res_sets,"BG up"=up_bg_res_sets,
"WG dn"=dn_wg_res_sets,"BG dn"=dn_bg_res_sets )
plot(euler(v1),quantities = TRUE,main="Effect of background list")
Now compare miR targets up and down together with observed up and down.
v2 <- list("up DGE"=mrna_up, "dn DGE"=mrna_dn,
"up miR targets"=up_targets, "dn miR targets"=dn_targets)
plot(euler(v2),quantities = TRUE, main="miR targets and DEGs?")
Interesting how low the enrichment is.
Now check with 100 genes.
mrna_up <- rownames(head(subset(dge_mrna,log2FoldChange >0 ),129))
str(mrna_up)## chr [1:129] "ENSG00000162849" "ENSG00000203805" "ENSG00000155011" ...mrna_up <- unique(gt[which(gt$Gene.stable.ID %in% mrna_up),"Gene.name"])
mrna_up <- mrna_up[mrna_up != ""]
str(mrna_up)## chr [1:100] "AGKP1" "APP" "CLTCL1" "SIPA1L1-AS1" "GTF3AP2" "CD226" ...mrna_dn <- rownames(head(subset(dge_mrna,log2FoldChange <0 ),105))
str(mrna_dn)## chr [1:105] "ENSG00000175899" "ENSG00000130203" "ENSG00000140022" ...mrna_dn <- unique(gt[which(gt$Gene.stable.ID %in% mrna_dn),"Gene.name"])
mrna_dn <- mrna_dn[mrna_dn != ""]
str(mrna_dn)## chr [1:100] "MT-RNR1" "MT-CO2" "MT-ND5" "SOHLH2" "PPARGC1A" "FREM2" ...v2 <- list("up DGE"=mrna_up, "dn DGE"=mrna_dn,
"up miR targets"=up_targets, "dn miR targets"=dn_targets)
plot(euler(v2),quantities = TRUE, main="miR targets and DEGs?")
Now check with 1000 genes.
mrna_up <- rownames(head(subset(dge_mrna,log2FoldChange >0 ),1288))
str(mrna_up)## chr [1:1288] "ENSG00000162849" "ENSG00000203805" "ENSG00000155011" ...mrna_up <- unique(gt[which(gt$Gene.stable.ID %in% mrna_up),"Gene.name"])
mrna_up <- mrna_up[mrna_up != ""]
str(mrna_up)## chr [1:1000] "ARSDP1" "CDY4P" "PRKY" "UTY" "AGKP1" "ZNF343" "LINC01669" ...mrna_dn <- rownames(head(subset(dge_mrna,log2FoldChange <0 ),1080))
str(mrna_dn)## chr [1:1080] "ENSG00000175899" "ENSG00000130203" "ENSG00000140022" ...mrna_dn <- unique(gt[which(gt$Gene.stable.ID %in% mrna_dn),"Gene.name"])
mrna_dn <- mrna_dn[mrna_dn != ""]
str(mrna_dn)## chr [1:1000] "MT-RNR1" "MT-RNR2" "MT-ND1" "MT-CO1" "MT-CO2" "MT-ATP6" ...v2 <- list("up DGE"=mrna_up, "dn DGE"=mrna_dn,
"up miR targets"=up_targets, "dn miR targets"=dn_targets)
plot(euler(v2),quantities = TRUE, main="miR targets and DEGs?")
Now check with 250 genes.
mrna_up <- rownames(head(subset(dge_mrna,log2FoldChange >0 ),321))
str(mrna_up)## chr [1:321] "ENSG00000162849" "ENSG00000203805" "ENSG00000155011" ...mrna_up <- unique(gt[which(gt$Gene.stable.ID %in% mrna_up),"Gene.name"])
mrna_up <- mrna_up[mrna_up != ""]
str(mrna_up)## chr [1:250] "AGKP1" "APP" "CLTCL1" "C21orf62" "APCDD1L" "MN1" "HDAC8" ...mrna_dn <- rownames(head(subset(dge_mrna,log2FoldChange <0 ),266))
str(mrna_dn)## chr [1:266] "ENSG00000175899" "ENSG00000130203" "ENSG00000140022" ...mrna_dn <- unique(gt[which(gt$Gene.stable.ID %in% mrna_dn),"Gene.name"])
mrna_dn <- mrna_dn[mrna_dn != ""]
str(mrna_dn)## chr [1:250] "MT-RNR1" "MT-RNR2" "MT-CO2" "MT-ATP6" "MT-ND3" "MT-ND4" ...Now pathway analysis of long RNA-seq
Use top 250 genes in either direction for enrichment analysis. There was a paper by Tarca recommending this.
mrna_up_res <- fora(pathways=reactome, genes=mrna_up, universe=bg, minSize = 5)
nbg = length(bg)
nsel = length(intersect(mrna_up,bg))
mrna_up_res$foldenrichment <- (mrna_up_res$overlap / nsel ) / ( mrna_up_res$size / nbg )
head(mrna_up_res)## pathway
## 1: Extracellular matrix organization
## 2: Collagen biosynthesis and modifying enzymes
## 3: Collagen chain trimerization
## 4: Collagen formation
## 5: Regulation of Insulin-like Growth Factor (IGF) transport and uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)
## 6: Collagen degradation
## pval padj overlap size
## 1: 5.814354e-07 0.001044258 16 217
## 2: 2.898106e-06 0.002602499 8 55
## 3: 1.663602e-05 0.009959432 6 34
## 4: 2.241904e-05 0.010066149 8 72
## 5: 4.851678e-05 0.017427228 8 80
## 6: 7.614645e-05 0.022071903 6 44
## overlapGenes foldenrichment
## 1: ADAMTS16,APP,BMP4,COL11A1,COL15A1,COL27A1,... 4.502120
## 2: COL11A1,COL15A1,COL27A1,COL4A4,COL8A1,COL8A2,... 8.881455
## 3: COL11A1,COL15A1,COL27A1,COL4A4,COL8A1,COL8A2 10.775294
## 4: COL11A1,COL15A1,COL27A1,COL4A4,COL8A1,COL8A2,... 6.784444
## 5: APP,BMP4,CDH2,FN1,GAS6,MEN1,... 6.106000
## 6: COL11A1,COL15A1,COL4A4,COL8A1,COL8A2,MMP2 8.326364nrow(mrna_up_res)## [1] 1796nrow(subset(mrna_up_res,padj<0.05 & foldenrichment >0))## [1] 10mrna_up_res_sets <- subset(mrna_up_res,padj<0.05 & foldenrichment >1)$pathway
mrna_dn_res <- fora(pathways=reactome, genes=mrna_dn, universe=bg, minSize = 5)
nbg = length(bg)
nsel = length(intersect(mrna_dn,bg))
mrna_dn_res$foldenrichment <- (mrna_dn_res$overlap / nsel ) / ( mrna_dn_res$size / nbg )
head(mrna_dn_res)## pathway
## 1: FASTK family proteins regulate processing and stability of mitochondrial RNAs
## 2: tRNA processing in the mitochondrion
## 3: rRNA processing in the mitochondrion
## 4: Mitochondrial RNA degradation
## 5: Muscle contraction
## 6: Ion homeostasis
## pval padj overlap size
## 1: 1.238109e-08 2.223644e-05 7 19
## 2: 4.018142e-08 3.608291e-05 7 22
## 3: 7.929370e-08 4.747049e-05 7 24
## 4: 1.085999e-07 4.876136e-05 7 25
## 5: 3.030982e-06 1.088729e-03 12 139
## 6: 2.759865e-05 8.261197e-03 6 37
## overlapGenes foldenrichment
## 1: MT-ATP6,MT-CO2,MT-ND3,MT-ND4,MT-ND5,MT-RNR1,... 22.495789
## 2: MT-ATP6,MT-CO2,MT-ND3,MT-ND4,MT-ND5,MT-RNR1,... 19.428182
## 3: MT-ATP6,MT-CO2,MT-ND3,MT-ND4,MT-ND5,MT-RNR1,... 17.809167
## 4: MT-ATP6,MT-CO2,MT-ND3,MT-ND4,MT-ND5,MT-RNR1,... 17.096800
## 5: ATP2A1,CALM1,CAMK2B,CAMK2D,KCNJ2,MME,... 5.271367
## 6: ATP2A1,CALM1,CAMK2B,CAMK2D,SLN,TRDN 9.901622nrow(mrna_dn_res)## [1] 1796nrow(subset(mrna_dn_res,padj<0.05 & foldenrichment >0))## [1] 9mrna_dn_res_sets <- subset(mrna_dn_res,padj<0.05 & foldenrichment >1)$pathwayv3 <- list("dn miR target"=dn_bg_res_sets,
"up miR target"=up_bg_res_sets,
"up mRNA"=mrna_up_res_sets,
"dn mRNA"=mrna_dn_res_sets)
plot(euler(v3),quantities = TRUE, main="miR target and DE pathways")