variance stabilized or log transformed data for DE analysis

Question

[tonja.r]

In DESeq they write:

How do I use the variance stabilized or rlog transformed data for differential testing?

The variance stabilizing and rlog transformations are provided for applications other than differential testing, for example clustering of samples or other machine learning applications. For differential testing we recommend the DESeq function applied to raw counts as outlined in Section 1.4.

Variance grows with mean in RNA-seq. With the variance stabilization variance does not depend on mean anymore. But how does it influence differential testing?

Answer 1

[Sam]

That depends on the software that you are using. There was a huge debate in the field of RNA Sequencing as to whether if one should use the normalized count or raw count for differential expression and you can find some great discussions here and here (Simon's posts are especially helpful)

Now if you are using DESeq or DESeq2 for your differential expression analysis, then you shouldn't worry about the variance stabilization or log transformation for DESeq uses the raw count data for its analysis. These functions were provided such that you can have a good visualization of data, for example plotting the PCA plot. Hope this helps