[BioC] Significant p-values disappear in limma

[James W. MacDonald]

michael watson (IAH-C) wrote:
> Hi Sean
> 
> Unfortunately this one is out of my control (as usual), but I have much
> smaller p-values with 4 arrays before, and even with 3 arrays.  Also
> note that in one of my four-array experiments, EVERY single p-value was
> 0.9999963 after adjusting for the fdr - that's over 4600 spots, all with
> the same p-value.  

Mick,

I see this sort of thing all the time, and what it means is that you 
don't have any evidence for differential expression between your two 
groups.

One of the things I do to check the quality of a given set of data is a 
principal components analysis. A plot of the first two PCs is usually a 
very good indication of how well your downstream analysis is going to 
turn out.

For instance, I just looked at 17 Affy chips from three different sample 
types. Only one group clustered together on a PCA plot, and this cluster 
was within a larger cluster of the other two groups (in other words, the 
different groups did not cluster separately). I knew from this that I 
would not be able to show any differential expression, and when I did 
the statistics my smallest adjusted p-value was something like 0.5.

I bet if you did a PCA with your data you will see something very similar.

Best,

Jim


> 
> Finally, note that the SWIRL dataset has only 4 arrays and limma
> produces many, many p-values <= 0.05.
> 
> So, although I admit 4 arrays is far from ideal in terms of power,
> something is nagging me that that's not it, and it certainly wouldn't
> explain why over 4600 spots all have the same adjusted p-value - would
> it?
> 
> Cheers
> Mick  
> 
> -----Original Message-----
> From: Sean Davis [mailto:sdavis2 at mail.nih.gov] 
> Sent: 05 January 2005 12:49
> To: michael watson (IAH-C)
> Cc: bioconductor at stat.math.ethz.ch
> Subject: Re: [BioC] Significant p-values disappear in limma
> 
> 
> 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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> 
> 
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-- 
James W. MacDonald
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109