GAGE enrichment GO terms, KEGG pathways

BioLion · 11-14-2017, 05:42 AM

Hello,
I am currently trying to find the enriched pathways following a RNAseq analysis using DESeq2, in a plant species where there is no reference genome. However, it does not work as I expected.

I used the bioconductor package “mygene” in order to get entrez identifiers corresponding to my uniprot identifiers that I had in my genes list. I then constructed my fold changes table using the log2 fold change obtained by using DESeq2, and having for names the entrez identifiers.

Code:

head(foldchanges)

111151 6920 100303206 24328 8668 20517
0.5957113 -0.4976848 0.4986454 -0.1833950 -0.3897194 0.5718210

However, then when I use the gage function, I have troubles finding the enriched pathways. My data actually comes from plants, so I thought of using the kegg’s pathways from Arabidopsis thaliana as such:

Code:

kegg.ath=kegg.gsets("ath",id.type="entrez")
kegg.ath.sigmet=kegg.ath$kg.sets[kegg.ath$sigmet.idx]
keggres=gage(foldchanges, gsets=kegg.ath.sigmet, same.dir=TRUE)

But I get this kind of results:

$greater
p.geomean stat.mean p.val q.val set.size exp1
ath00970 Aminoacyl-tRNA biosynthesis NA NaN NA NA 0 NA
ath02010 ABC transporters NA NaN NA NA 0 NA

However when I tried by curiosity to use the homo sapiens pathway with the following code, it seems to work better…

Code:

data(kegg.sets.hs)
data(sigmet.idx.hs)
kegg.sets.hs=kegg.sets.hs[sigmet.idx.hs]
keggres=gage(foldchanges, gsets=kegg.sets.hs, same.dir=TRUE)

$greater
p.geomean stat.mean p.val q.val set.size exp1
hsa00010 Glycolysis / Gluconeogenesis 0.1426633 1.10118079 0.1426633 0.9257147 10 0.1426633
hsa00240 Pyrimidine metabolism 0.1566751 1.03046501 0.1566751 0.9257147 13 0.1566751

Can someone give me a clue about what is going on?

Best regards,
BioLion

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11-14-2017, 05:42 AM	#1
BioLion Junior Member Location: France Join Date: Apr 2014 Posts: 6	GAGE enrichment GO terms, KEGG pathways Hello, I am currently trying to find the enriched pathways following a RNAseq analysis using DESeq2, in a plant species where there is no reference genome. However, it does not work as I expected. I used the bioconductor package “mygene” in order to get entrez identifiers corresponding to my uniprot identifiers that I had in my genes list. I then constructed my fold changes table using the log2 fold change obtained by using DESeq2, and having for names the entrez identifiers. Code: head(foldchanges) 111151 6920 100303206 24328 8668 20517 0.5957113 -0.4976848 0.4986454 -0.1833950 -0.3897194 0.5718210 However, then when I use the gage function, I have troubles finding the enriched pathways. My data actually comes from plants, so I thought of using the kegg’s pathways from Arabidopsis thaliana as such: Code: kegg.ath=kegg.gsets("ath",id.type="entrez") kegg.ath.sigmet=kegg.ath$kg.sets[kegg.ath$sigmet.idx] keggres=gage(foldchanges, gsets=kegg.ath.sigmet, same.dir=TRUE) But I get this kind of results: $greater p.geomean stat.mean p.val q.val set.size exp1 ath00970 Aminoacyl-tRNA biosynthesis NA NaN NA NA 0 NA ath02010 ABC transporters NA NaN NA NA 0 NA However when I tried by curiosity to use the homo sapiens pathway with the following code, it seems to work better… Code: data(kegg.sets.hs) data(sigmet.idx.hs) kegg.sets.hs=kegg.sets.hs[sigmet.idx.hs] keggres=gage(foldchanges, gsets=kegg.sets.hs, same.dir=TRUE) $greater p.geomean stat.mean p.val q.val set.size exp1 hsa00010 Glycolysis / Gluconeogenesis 0.1426633 1.10118079 0.1426633 0.9257147 10 0.1426633 hsa00240 Pyrimidine metabolism 0.1566751 1.03046501 0.1566751 0.9257147 13 0.1566751 Can someone give me a clue about what is going on? Best regards, BioLion