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I'm using cuffdiff 2.1.1 to look for differential gene expression between two conditions. Each condition has 2 biological replicates. The results I get look promising from a log fold change perspective - lots of genes that I would expect to be affected have the highest fold change values. However, from a q-value perspective very few of the results look promising, having q values > 0.05.

My question is, given 2 replicates, how is the q value calculated? Surely if there was high variance between replicates but the difference between experimental conditions was also large then the q-values would be correspondingly high?

In this case, can I, or should I, really trust the q-values?

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not sure about q-value but p-value is based on statistical test and in case of differential expression it is based on t-test. I am not sure about what cuffdiff does. you can refer its manual or supplementary info for details. – WYSIWYG Aug 23 '13 at 12:46
If after a few days you don't get good answers here, consider reasking the question here: It's basically stackexchange specifically for bioinformatics. About a third of the questions over there are about next generation sequencing. – swbarnes2 Jan 9 '14 at 18:01

q-value is the corrected p-value to account for multiple testing (i.e. you are testing thousands of genes). Those with q-value <0.05 are significant experiment-wise. Cuffdiff uses Benjamini-Hochberg correction to compute FDR (i.e. q-value). The calculation does not depend on the number of replicates it's based on the distribution of p-values those, yes, are influenced by the number of replicates. The more replicates you have the better).

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