I have processed my read count data from RNA-seq with both limma/voom and DESeq2 method. After that, I plotted the log fold change for both methods. The result was okay with most of the genes has "similar" fold change and the plot looks roughly a diagonal line. Now, I want to further analysis the data with some statistical method/machine learning technique. I know I need to use the log expression for each genes as input for basic machine learning method (clustering, regression, etc) and from DESeq2, I can use rlog function which the documentation states that:
This function transforms the count data to the log2 scale in a way which minimizes differences between samples for rows with small counts, and which normalizes with respect to library size. The rlog transformation produces a similar variance stabilizing effect as
rlog is more robust in the case when the size factors vary widely. The transformation is useful when checking for outliers or as input for machine learning techniques such as clustering or linear discriminant analysis
My question is, which is better ones, using limma/voom log expression (I think the output of voom) or DESeq2? I'm not really familiar with how voom processed the transformation from raw count to log expression. For DESeq2, it seems the transformation is simpler because it doesn't use weighting for precision like in voom.