Hi all,
We have a large RNAseq data set. Apart from identifying differentially
expressed genes with these data we are also interested in classification in
terms of developing a pronostic and diagnostic classifier.

Normally, our approach would utilize a machine learning classifier, as SVM,
and typically proceed with a nested cross-validation approach.


The vast majority of these programs and packages have been designed
utilizing microarray data.

Are there any reasonable biases which one should consider before using such
already published approaches on RNAseq data?

Do the distributions of the different data types matter at all?

If so, does an application exist using an SVM taking into consideration
RNAseq raw counts?

Thanks,

Michael

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