I want to train a binary classifier using cancer RNA-seq data. After differential expression analysis, I selected some top DEGs as features for glmnet training, however the performances were quite bad, I think this may be due to tumor heterogeneity. 

Therefore I plan to do feature engineering, like using the sum of expression values of genes in one pathway as one feature. 

I saw some paper used the raw counts (but microarray data) and some paper used normalized RNA-Seq counts. I feel a bit confused, what kind of counts should be added up here? raw counts? TPM or CPM or log-CPM? need to do normalization like TMM? or depends on which algorithm?
I also found this post: [Difference between CPM and TPM and which one for downstream analysis?](https://bioinformatics.stackexchange.com/questions/2298/difference-between-cpm-and-tpm-and-which-one-for-downstream-analysis).

The answers say TPM is better than CPM since TPM accounts for transcript length differences including more information, while the better one is values after normalization and transformation using `vst` or `rlog`.
