I was wondering if there is any way to apply classification algorithms (e.g random forest) on microarray data but not using the genes as predictors/features but the pathway they belong to.
The thing is that the expressions of the genes that belong to the same pathway should somehow, grouped together and transformed into a new value that will describe that pathway for each specific sample.
Let me give an example.
Assuming that we have g1, g2, g3, g4, g5, g6, g7, g8, g9, g10
as genes
and p1, p2, p3
as pathways.
And g1, g2, g3
belong to p1
while g3, g4, g5, g6
belong to p2
and g2, g6, g7, g8, g9, g10
belong to p3
One thought is to calculate the mean expression of the above gene groups per sample and then run the classification algorithm on top of these values. But I would like to hear/read your opinion on such an approach and also your suggestions on using a different method to do this.
The final goal would be the extraction of the most significant classification predictors (pathways).