Hi forum,
I have a list of differentially expressed genes from an RNA Seq analysis that I wish to do Gene ontology analysis of.Is it better to analyze up regulated and down regulated genes separately? As I understand it, Go analysis does not consider the direction of change -only the gene names are considered... Thanks for your input!
I don't think it makes sense to do this. Just because a gene is up-regulated or down-regulated does not mean that the process, function or component that the gene is annotated to is also respectively directionally regulated. There is no directionality associated to how a gene impacts a GO term (aside from some GO terms having directionality like "negative regulation of cell proliferation"). This is one of the limitations to GO analysis. It is specifically for this reason that one should consider all DEGs when performing a GO analysis. Then use advanced pruning algorithms like Elim or Weight as proposed by Alexa et al (2006) to identify truly enriched GO terms.
You can read more about this here: http://eepurl.com/bubBe9
Your link takes me to the following place:
http://us6.campaign-archive1.com/?u=c55d2c432a0f7a17ba0a260bc&id=65d0584514
Which just looks like an advertisement. Either way, I'm not sure how the 'elim' algorithm does anything to address the subject of up/down enrichment.
You should read the article linked by tiago.
Check this: "Separate enrichment analysis of pathways for up- and downregulated genes"
Yes, when the enrichment software you're using doesn't consider the fold change, you should run up and down genes separately. I think this is because GO enrichment usually looks for significant enrichment with respect to the whole set of GO annotations for that species.
You should take care to check for similar/same terms between groups. Otherwise, look for GO/pathway software that considers fold change.
I'd like to restart this discussion because I didn't quite understand the result (contradictory answers).
Is there consensus now, 5 years after this discussion? What should we do?
Honestly, it's up to you and what question you're trying to answer. I've done both.
Sometimes I just want to see what pathways are perturbed by a drug, so I just throw in all my differentially expressed genes. I don't care if the pathway goes up or down or if some genes in the pathway go up while other genes in the pathway go down; I only care about the pathway being changed in some way.
Other times, I care about direction. For example, I recently compared tumor cells to the cells in the surrounding environment. It's interesting to note "Oh, replication, proliferation, etc. pathways go up while immune, stroma, etc. pathways go down" when comparing tumor cells relative to cells in the environment.
Since I'm doing GO annotation just now, I came up with the same question and landed here. Here's my 2p, without too much thinking...
In favour of keeping up- and down-regulated genes together: other things being equal, you should have more power because you rely on larger sets of DE genes. Also, you test fewer hypotheses so fewer chances of false-positive calls.
In favour of separate analysis: imaging you have 5 genes up-regulated and they are all in the same pathway; you also have 50 genes downregulated scattered across pathways. I think that testing the 55 genes together will obscure the pathway enriched for the 5 genes. Is this a far-fetched scenario? Dunno, but I don't think so... You can have a treatment that specifically affects a pathway and many genes move in the same direction in that pathway (e.g., genes associated with DNA replication). This in turn causes havoc in a number of other pathways in a seemingly random manner.
I think I'm leaning towards separate analyses but I can easily be persuaded otherwise. In practice, try both and try to pick the most sensible results without fooling yourself.
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Yes, I have seen some papers where upregulated and downregulated genes are categorized separately on GO term;
Once you get significant level of GO term in the subset of genes, you can go for pathway or network analysis to see the enrichment of your detected genes in certain pathway or process
C: Hypergeometric test in GSEA