
Hi. 
During a DE analysis, done with limma ebayes and toptable, the histogram of the p-values doesn't show a high number of low p-values, and not even an uniform distribution, which I may expect under the null hypothesis of no-differentiation (right?), but something totally skewed to the right: low freqencies for the small p-values and increasing frequencies as the p-value on th x-axis increases.

here it is

http://img522.imageshack.us/img522/2169/testkpv.jpg

(I'm pretty sure about the correctness of the test. I did it on other comparisons and it always gave nice and straightforward results).
I work on peptide arrays and I measure the immune-response.
Since the comparison I'm doing is between sick vs healthy individuals for a pathology BUT all the individuals were also diagnosed as sick for ANOTHER pathology, I explained the phenomenon with a confounding effect of the second pathology, which is altering the immune-response, and for which the data are not controlled.
But, from the data point of view, how can I comment on the graph? Does anybody have an idea what we can say of a histogram of p-values where we have few low p-values and many more high p-values?

Thanks in advance

G
 		 	   		  
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