Below is the command I used to run Dmrcate and enrichment analysis.
dmrCate<-cpg.annotate("array", BetaValue, what="Beta", arraytype ="EPIC", analysis.type="differential",,fdr=0.01) dmrcoutput <- dmrcate(dmrCate,lambda=500, C=5, pcutoff="fdr",min.cpgs=1,mc.cores=1) wgbs.ranges_all <- extractRanges(dmrcoutput, genome = "hg19")
enrichment_GO <- goregion(wgbs.ranges_all,collection = "GO", array.type = "EPIC") enrichment_GO <- enrichment_GO[order(enrichment_GO$P.DE),]
I have got the following results too
"ONTOLOGY" "TERM" "N" "DE" "P.DE" "FDR" "GO:0005654" "CC" "nucleoplasm" 3501 1911.66666666667 5.78302322878527e-28 1.31685221942669e-23 "GO:0043231" "CC" "intracellular membrane-bounded organelle" 11367 5316.54285714286 8.60779986601549e-27 9.80041053745193e-23 "GO:0043229" "CC" "intracellular organelle" 13162 6092.37619047619 8.98325926857439e-24 6.25634156984302e-20
Is there any ways I could fetch the genes that are used/involved to identify in each of these pathways?
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bassanio •
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Hi,
I arrive a little late but I had the same problem and this is how I deal with it:
1)Convert your DMR gene list into Entrez Gene ID
EntrezGenes = mapIds(org.Hs.eg.db, genes, 'ENTREZID', 'SYMBOL')
2)Retrieve .getGO function that can be found in this script : https://rdrr.io/bioc/missMethyl/src/R/gometh.R
go=.getGO
3)Intersect your gene list with GO of interest gene list
which(EntrezGenes%in%go$idList$`GO:0000000`)
This can't be 100% accurate because goregion is designed to cope with CpGs that map to multiple genes but it could help.
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Hi,
Do any one have any input on this?