Hi Mike,

I think another  reason is the small sample size and many gene
pairs.So randomly significant pairs would be expect which generate high
FDR.   I don't know if there is a better solution. I would choose top
ranking genes with big fold change and small p value.

Jack

2012/6/25 efthimiosm <efthimiosm@bii.a-star.edu.sg>

> Hi all,
>
> My name is Mike and I am a post-doctoral fellow in Bioinformatics. I have
> a question regarding multiple testing p-values adjustment and I wonder if
> someone could give me a piece of advice.
>
> I have multiple gene pairs (approximately 8,256) composed by all possible
> combinations of 129 genes. For each pair A-B (A different from B) four
> values are recorded: number of tumors found in both A and B (TT),  number
> of tumors only in A (TF), number of tumors only in B (FT), number of tumors
> found neither in A nor in B (FF). The data are in the form of 2x2
> contingency tables. E.g.
>
> Gene 1    Gene 2    TT    TF    FT    FF
> g1    g2    5    1    1    27
> g1    g3    4    1    1    28
> g2    g3    4    2    0    28
> ...
> ...
> ...
>
> Notice that each gene is paired with all others and thus it is represented
> 128 times in this list. I want to find which of the 8,256 gene pairs
> (tests) show significant associations between rows (in A, not in A) and
> columns (in B, not in B) by Fisher or Barnard test. Subsequently I have to
> perform p-value adjustment for multiple testing.
>
> At 5% I find approximately 500 significant gene pairs but, naturally, all
> p-value adjustment procedures I tried (for independent tests: BH, q-value;
> for dependent tests: BY, adaptiveBH and BlaRoq from package "multtest")
> produce adj. p-values > 0.3. I think that the problem is that the highly
> dependent nature of the data (50% of the genes have very small number of
> mutations which gives high p-values for all pair they generate) affects
> dramatically the adjustment procedure.
>
> Is there a better way (method) to run the p-values adjustment?
>
> Do you think if I created multiple lists of gene pairs, where each gene is
> represented only once, and then estimate q-value (multiple q-values for
> each pair) would be an appropriate solution?
>
>
> Thank you,
> Mike
>
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