Hi

I'd like to have a discussion about statistics for transcriptomics using next-generation sequencing (if there hasn't already been one - if there has, then please someone point me to it!)

What we're seeing in the literature, and here at IAH, are datasets where someone has sequenced the transcriptome of two samples using something like Illumina.  These have been mapped to known sequences and counts produced.

So what we have is something like this:

geneA: 22000 sequences from 260000 match in sample 1, 43000 sequences from 507000 in sample 2.

It's been suggested that one possible approach would be to construct 2x2 contingency tables and perform Fisher's exact test or the Chi-squared test, as has been applied to SAGE data.
However, I've found that when I do that, the p-values for this type of data are incredibly, incredibly small, such that over 90% of my data points are significant, even after adjusting for multiple testing.  I assume/hope that this is because these tests were not designed to cope with this type of data.

For instance, applying Fisher's test to the example above yields a p-value of 3.798644e-23.

As I see it there are three possibilities:
1) I'm doing something wrong
2) These tests are totally inappropriate for this type of data
3) All of my data points are highly significantly different

I'm thinking that 2 is probably true, though I wouldn't rule out 1.

Any thoughts and comments are very welcome,

Mick


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