<p>It makes more sense to evaluate by separate the pathway or gene set you want and see if in the three datasets result in a coherent message than to merge these datasets, as you will mix batch effects and treatment effects.</p>

<p>When in several datasets one looks for the same statistic it is usually considered meta analysis, not integration. </p>

CC BY-SA 3.0
2017-11-22T11:56:32.383Z
2017-11-22T11:56:32.383Z
llrs
<p>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.</p>

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

<p>Here is how I'm planing to proceed:</p>

<p>i.e</p>

<pre><code>dataset1 : 3x Controls , 3x Treatment1
dataset2 : 2x Controls , 2x Treatment2
dataset3 : 2x Controls , 2x Treatment3
</code></pre>

<p>After merging</p>

<pre><code>7x Controls , 3x Treatment1 , 2x Treatment2 , 2x Treatment3
</code></pre>

<p>Then I will execute the GSEA for controls , Treatmen1 , Treatment2 and Treatment3 separably.</p>

<p>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.</p>

CC BY-SA 3.0
2017-11-22T10:48:24.380Z
Is there any reason you don't want to or can't do both?
Mitra
CC BY-SA 3.0
2017-11-22T10:56:23.587Z
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
CC BY-SA 3.0
2017-11-22T11:48:20.817Z
@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.
J. Doe
CC BY-SA 3.0
2017-11-22T11:50:22.413Z
@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](https://www.bioconductor.org/packages/release/bioc/vignettes/frma/inst/doc/frma.pdf)
J. Doe
CC BY-SA 3.0
2017-11-22T11:52:03.293Z
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
<microarray><gse>
Integration of different microarray dataset to run GSEA
# Integration of different microarray dataset to run GSEA

(Asked by: username_0 on 2017-11-22)

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.

**Tags:** microarray, gse
> **Comments:**
> **username_1**: Is there any reason you don't want to or can't do both?  
> **username_2**: 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.  
> **username_0**: @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.  
> **username_0**: @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](https://www.bioconductor.org/packages/release/bioc/vignettes/frma/inst/doc/frma.pdf)  
> **username_2**: 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.  

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## 1 answers

### Answer by username_2 on 2017-11-22. Score: 2

It makes more sense to evaluate by separate the pathway or gene set you want and see if in the three datasets result in a coherent message than to merge these datasets, as you will mix batch effects and treatment effects.

When in several datasets one looks for the same statistic it is usually considered meta analysis, not integration.


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