Question: What to choose as background genes in GO enrichment analysis
0
gravatar for tianshenbio
6 months ago by
tianshenbio70
tianshenbio70 wrote:

I tried both clusterprofiler and goseq for GO enrichment analysis. However, I got 4-5 times more GO terms enriched in goseq than in clusterprofiler since p-values calculated in goseq are generally smaller. This is because I used all genes in the genome as background genes so the background ratio is small and p is also smaller. Then what genes I should use as background genes? here are some of my options:

  1. All genes in the genome
  2. All genes with GO terms in the genome
  3. Genes detected in my samples
  4. Genes detected in my samples with GO terms
ADD COMMENTlink modified 6 months ago by Papyrus540 • written 6 months ago by tianshenbio70
2

I personally use the genes that were actually used in the DE analysis, so in my case all genes that survive the FilerByExpr step of edgeR.

ADD REPLYlink written 6 months ago by ATpoint41k
1

I would use 3 or 4 depending on your input list, if it contains only genes with GO term then 4, if not (which is the correct way IMHO) then 3.

ADD REPLYlink written 6 months ago by Asaf8.4k
3
gravatar for Papyrus
6 months ago by
Papyrus540
Papyrus540 wrote:

I would use all of the genes that were analyzed in your experiment: these are those on which you performed the differential expression analyses (you maybe previously filtered them to remove low-expression genes, etc.), because (under assumption of independence) those are the ones which had a chance of appearing as DEGs.

Regarding filtering out genes which do not map to GO terms, you can control for this at the GO enrichment step: the goseq function can control this with its use_genes_without_cat argument. And by default (most recent version) these genes are ignored in the enrichment testing.

ADD COMMENTlink modified 6 months ago • written 6 months ago by Papyrus540

Thank you for your answer! Very helpful

ADD REPLYlink written 6 months ago by tianshenbio70
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