Dear Bioconductor users,

I am working with RNA-seq data (raw counts) and I want to perform regularized cox regression modelling  using glmnet package. First, I have performed VST transformation that makes RNA-seq data homoscedastic. Next do i have to set the argument of glmnet function standardize= TRUE for variable standardization (all variables to have unit variance) prior to fitting the model sequence and then use the resulting unstandardized coefficients to rank the selected features (genes) or in my case the default standardization is not appropriate ?

Thank you for your time in advance!!

Sincerely,

Panagiotis Mokos

hi Panagiotis,

The glmnet software is optimized to have unit variance predictors, so I can see how you got to this dilemma.

Scaling (for each gene, across samples) and VST are to some degree at odds. The VST shrinks technical variance so that biological differences are not overwhelmed. And doing so it outperforms simply transformations such as log(x + 1). But then if you force all genes to have unit variance, you undo that effect, increasing technical noise which was just shrunk.

I'd suggest you use the VST, then use a variance filter on the VST data to remove genes with minimal variance (take a look at the meanSdPlot to get a sense of the genes which likely have no biological signal, see vignette), then feed the remaining genes to glmnet with standardize=TRUE.