Enter the body of text here Recently, I use the DESeq2 to analyze a data, but there is a question is that the DESeq2 tutorials point the quickly start ，which use the code ```

dds <- DESeqDataSetFromMatrix(countData = cts,
                              colData = coldata,
                              design= ~ batch + condition)
dds <- DESeq(dds)
resultsNames(dds) # lists the coefficients
res <- results(dds, name="condition_trt_vs_untrt")
# or to shrink log fold changes association with condition:
res <- lfcShrink(dds, coef="condition_trt_vs_untrt", type="apeglm")

```.
So, I want to know the normalization when and how what for the difference gene, and that the VST and RLOG method processed data, which could for different gene expression analysis?

If you're asking whether any of vst or rlog are used for differential expression then no, neither is used for DE. Please see the FAQs in the vignette. These transformations are intended for visualization, clustering etc, the DE testing does not use it.

http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#how-do-i-use-vst-or-rlog-data-for-differential-testing