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  • 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

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