[BioC] Significant p-values disappear in limma

[Naomi Altman]

Well, 5% x 4600 = 230, so even a rough guess puts the FDR at 230/360 which 
is pretty high.  Qvalues are the level FDR at which the gene becomes 
significant.  If NsigA is the number of significa genes at level A,  FDR is 
approximately A*4600/NsigA,   so you need a relatively large value of NsigA 
to have a small Qvalue.

A log2 ratio is 5 is not that huge in the scheme of things, especially with 
only 4 arrays.  Why not have a look at the actual expression values on the 
arrays?

--Naomi

At 07:48 AM 1/5/2005 -0500, Sean Davis wrote:
>It seems that with only two experiments (with accompanying dye-swaps), it 
>is certainly possible that you don't have enough power to detect a 
>difference.  Can you do more experiments?
>
>Sean
>
>On Jan 5, 2005, at 6:58 AM, michael watson ((IAH-C)) wrote:
>
>>Hi
>>
>>Sorry to labour the point, but following on from my last mail, I have
>>four arrays in a replicated dye swap experiment.  After carrying out the
>>analysis in limma, I find that 360 out of 4600 genes have an unadjusted
>>p-value <= 0.05.  However, when I adjust these using adjust="fdr", all
>>of these disappear, and I have p-values of 0.5 and upwards.  My B
>>statistics seem much lower than in other analyses I have done, even
>>though the t-statistics are still quite large, as are (some of) the M
>>and A values.
>>
>>I was just wondering if anyone had seen this before and could shed some
>>light on what this might say about my data.  When the top gene from
>>topTable() has log2 ratios of 4.11, 5.51, 3.53 and 4.3, yet has an
>>adjusted p-value of 0.2790644 and a B value of only 1.080982225, I
>>figure something must be badly wrong somewhere...
>>
>>Thanks in advance
>>
>>Mick
>>
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Naomi S. Altman                                814-865-3791 (voice)
Associate Professor
Bioinformatics Consulting Center
Dept. of Statistics                              814-863-7114 (fax)
Penn State University                         814-865-1348 (Statistics)
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