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
p1, p2, p3 as pathways.
g1, g2, g3 belong to
g3, g4, g5, g6belong to
g2, g6, g7, g8, g9, g10 belong to
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).