My current dataset has three conditions, and we've measured the activity levels of 10,000 genes in each condition. Replicated 8 times.

Using 10,000 linear models, we determine for each pair of conditions (ie for each of three contrasts) the number of genes with significantly different activity levels. This is [standard procedure][1] for this kind of microarray data. 

We find:

 * 2000 genes have significantly different activity levels between A and B
 * 1500 genes have significantly different activity levels between A and C
 *  100 genes have significantly different activity levels between B and C

This suggests that conditions B and C are more similar to each other than to C. PCA suggests the same result.
Is there any way for us to quantify the extent to which "conditions B and C are more similar to each other than to C (ie to put a p-value on it?)

Thanks for your help, and apologies if this question is trivial. 

Kind regards,


  [1]: http://bioinf.wehi.edu.au/limma/