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 for this kind of microarray data (http://bioinf.wehi.edu.au/limma/).

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, Yannick

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 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,