Question: Appropriate input of merged datasets regarding batch effect correction with ComBat in R
gravatar for svlachavas
2.2 years ago by
Greece/Athens/National Hellenic Research Foundation
svlachavas610 wrote:

Dear Community,

i would like to ask a very specific question about the less "erroneous" procedure regarding the implementation of ComBat and batch effect correction in microarray datasets. In detail, my goal is to test a 39 gene signature that i have aqcuired, through a feature selection procedure in R-based on a training microarray dataset-, in 5 independent datasets, regarding its discriminatory power for a two class-label disease status. All of the testing datasets are from the same platform. Next, i would first perform separate normalization in dataset, then merge them and perform batch effect correction prior testing the classifier. Thus, my crusial question is that i should normalize and batch correct the datasets with all the available probesets, and then subset the merged dataset with the same 39 gene symbols i mentioned above (for the subsequent testing of the classifier) ? In order except for the normalization also for the batch effect correction to be beneficial for taking into account  the signals of all probesets? Or my approach is incorrect, and i should subset after normalization each dataset to these 39 genes? 

ADD COMMENTlink modified 2.2 years ago by chris86380 • written 2.2 years ago by svlachavas610
gravatar for chris86
2.2 years ago by
UCL, United Kingdom
chris86380 wrote:

If your signature is reproducible you should be able to separately normalise each data set without merging them and doing batch correction and apply your classification algorithm within a dataset to groups. You may have to batch correct individual datasets, but I would not do it all together for this purpose.

ADD COMMENTlink modified 2.2 years ago • written 2.2 years ago by chris86380
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