Question: Using GAGE to Analyze Pathway Enrichment Directly from Fold Change Data
0
gravatar for JMallory
17 months ago by
JMallory0
JMallory0 wrote:

I have been using the following tutorial by Stephen Turner and Will Bush to look at some RNA-seq data.

http://www.gettinggeneticsdone.com/2015/12/tutorial-rna-seq-differential.html

Looking into GAGE's documentation, it looks like they are using it in a somewhat non-standard way. Specifically, it looks like they are using it to conduct a GSEA-esque analysis, feeding it a vector of fold changes annotated by Entrez IDs and looking for enrichment within pathways contained in the kegg.sets.hs object.

Were this a standard GSEA analysis, I would order transcripts by log2 fold change prior to analysis. In this use case of GAGE, should transcripts also be rank ordered prior to analysis? Running it both ways appears to make a large difference, at least in the case of my data.

ADD COMMENTlink modified 17 months ago by h.mon24k • written 17 months ago by JMallory0
0
gravatar for tarek.mohamed
17 months ago by
tarek.mohamed240
tarek.mohamed240 wrote:

Hi,

Could you please clarify in details what are the two approaches you used for your analysis.

Tarek

ADD COMMENTlink written 17 months ago by tarek.mohamed240
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gravatar for h.mon
17 months ago by
h.mon24k
Brazil
h.mon24k wrote:

Running it both ways appears to make a large difference, at least in the case of my data.

By "running it both ways" you mean you tried with ordered and unordered logFC vectors? I had the same doubt in the past, when I tried GAGE with ordered and unordered logFC vectors results were the same.

ADD COMMENTlink written 17 months ago by h.mon24k

Yes. It is my understanding now that this may be caused by geneIDs in a given ontology list mapping to multiple transcripts of the same gene in the data. I believe GAGE is looking for a one-to-one mapping between a measure of DE and a gene, not multiple measures of DE for, say, different isoforms of a gene mapping to common geneIDs in the ontology list.

In this sense, it does not appear to be the best approach for RNA-seq data and certainly doesn't take into account things like read length and expression biases. I have since started to work with ontology enrichment analysis tools such as GOseq specifically tailored to RNA-seq data. It is a shame, because Pathview appeared very nice for generating easily understood figures.

ADD REPLYlink written 17 months ago by JMallory0
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