# Integration of different microarray dataset to run GSEA

I'm planning to run a GSE Analysis on some microarray datasets. Until now what I have done is to pre-processed them using frma algorithm.

The reason I chose frma instead of gcrma or rma is that I wanted to merge these different dataset into one.

Here is how I'm planing to proceed:

i.e

dataset1 : 3x Controls , 3x Treatment1
dataset2 : 2x Controls , 2x Treatment2
dataset3 : 2x Controls , 2x Treatment3


After merging

7x Controls , 3x Treatment1 , 2x Treatment2 , 2x Treatment3


Then I will execute the GSEA for controls , Treatmen1 , Treatment2 and Treatment3 separably.

So the question is if it's more reasonable to follow the path that I described or to run the GSEA on the separate datasets (for example for each control ) and then get the intersect of the gene sets of these three repetitions.

• Is there any reason you don't want to or can't do both? Nov 22 '17 at 10:48
• What have you done to control that each dataset is (presumably) done under different conditions and thus have batch effects? Are the controls the same in each dataset? Could you point me what is the frma algorithm ? It is the first time I read about it.
– llrs
Nov 22 '17 at 10:56
• @Mitra : No there is no such a reason. I'm just asking to see if something is more reasonable or makes more statistical sense than the other. Nov 22 '17 at 11:48
• @Llopis : At the merging stage I will remove the batch effects with comBat method (I could also try others as well). Here is a documentation for frma Nov 22 '17 at 11:50
• Thanks, take into account that this will remove too the effect of each treatment. as it cannot distinguish between the batch effect and the treatment effect.
– llrs
Nov 22 '17 at 11:52