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-   -   GSEA for RNA-seq data (http://seqanswers.com/forums/showthread.php?t=31817)

sazz 07-11-2013 12:52 AM

GSEA for RNA-seq data
 
Hello,

We have performed an RNA-seq analysis with Tuxedo Tools. There are 2 conditions and 3 replicates for each, and we have detected differentially expressed genes with 0.01 q cut-off.

Now, it is time to get meaningful results by comparing with certain gene sets. I have used DAVID at the first step, then now, I am trying to use GSEA, but I have some questions in my mind.

- Should I load all the replicate FPKM values for each condition as input?
- Should I load all the genes without cut off?

Note: They say, you can use "Fpkm_trackingToGct" program from Genepattern. In this case you load the fpkm_tracking file of cuffdiff output and it gives you an output in .gct format which is going to be used as input in GSEA. In this case, that output includes the overall calculated value for 3 replicates (not all of them seperately). If this is the case, I assume I should use a cut-off when loading the genes because the program won't be able to calculate the variation btw replicates, but I also don't know if this leads to some bias.

yzzhang 10-18-2013 12:16 PM

Hi, sazz, have solve your problem? I also wonder these things. If you get good solution, could you kindly share it? Thanks in advance.

sazz 10-20-2013 05:01 AM

Hi yzzhang,

I wrote to gsea developers, that's the answer:

"The best approach would be to create a GCT file where rows are gene identifiers
(ideally, unique instances of human gene symbols), columns are the biological
replicates for each of two phenotypes and the values are FPKM values.

In any case, you should avoid filtering your data in any way because this would
significantly reduce power of GSEA."

But apart from this problem, I don't trust the ranking methods, ttest or signal2noise are not that much suitable for that kind of analysis. Even the formulas of those methods cares about the variation between replicates, it does not fit to the logic of CuffDiff significancy calculations. You can check the ranked list at the end, and you will see that the ones with very low expressions but also with a low variancy, are not in the "significant list" in CuffDiff but in high ranks at GSEA output, probably because of their low expressions; so a formula that considers the q-value of CuffDiff and log2fold change would be best; so you can do a pre-ranking; but I haven't found something like this yet; and also my statistics is not that good and I can't figure it out on my own.

Actually in the paper of "Differential analysis of gene regulation at transcript resolution with RNA-seq" of Cole Trapnell, they use GSEA after RNA-seq and in the methods part, they say:

"Enrichment for up- or downregulation sets of genes from the REACTOME pathway database was computed by running GSEA against the fold-change ranked list of genes in the experiment. Ranking was based on Cuffdiff 2–derived fold change."

So they say, they used fold change at the CuffDiff result but this can't be that simple, just disregarding the q-value. I asked to Cole Trapnell by mail but he didn't respond.

yzzhang 10-20-2013 05:47 AM

Hi, sazz,
Thanks a lot. I appreciate your help, and I will think if this method is suitable for my data. Thanks again.


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