Question: p-value correction in meta-analysis of cancer cohorts
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gravatar for Bontus
3.5 years ago by
Bontus80
Netherlands
Bontus80 wrote:

Dear all,

I would like to perform a meta-analysis on DNA mutations in cancer using two completely independent cohorts and my aim is to identify events that occur more often in a subgroup of samples compared to controls.
My analysis is based on a Fisher test to find the mutations occurring significantly more often in the subgroup, which is performed separately for each gene, thus I am subsequently correcting p-values by FDR.

This analysis is currently performed independently for each cohort, so I end up with two lists of genes that are mutated in the subgroup significantly more often.

To 'integrate' both cohorts, my idea was to simply select genes with a significant FDR (say q < 0.05) in both analyses and then see how big the overlap is. However, since the second cohort is suffering from low sample size and I might lose a lot of real events, I was also thinking of a more direct way of validating, by using cohort 1 as discovery and cohort 2 as validation data set.

This is where my question comes in:

If I select genes by FDR in cohort 1 (q < 0.05) and then only test these genes in cohort 2 to reduce the number of tests, do I still need to do a p-value correction for the results of my validation? After all, I would expect most of these genes to be indeed different and if I am not mistaken, FDR / Bonferroni would in this case throw out too many true positives to control the number of false positives.

Any help is greatly appreciated!

ADD COMMENTlink written 3.5 years ago by Bontus80
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