Apply DESeq2::vst() transformation to the data.

This will use the DESeq2::dispersionFunction() learned from the dataset used for model creation.

Usage

compute_vst(matr)

Arguments

matr

Input raw counts matrix.

Value

A matrix with transformed values. R Matrix rows are genes, matrix columns are samples.

Details

If your pipeline/protocol is different than the one used for training, you would probably need to use some sort of a batch-correction.