Hi. there are two issues that I have.

1. Raw read count used as input data for deseq(). Is it really okay? I referenced that information on following website.http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#why-un-normalized-counts But, I want to try normalizing raw count into RLE(Relative Log Expression).

2. What does default statistical method of DEseq()? nbinomialwaldtest?

thank you for your help.

# setup
condition <- factor(c(c,c,c,t,t,t))
coldata <- data.frame(row.names = colnames(Counts),condition)
dds <- DESeqDataSetFromMatrix(countData = Counts, colData = coldata, design = ~condition)

# Estimate size factors using Relative Log Expression method
dds <- estimateSizeFactors(dds, type = c("ratio")) <------------- I thought this ratio type is about RLE normalization. Is that right?
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds) <--------- Is this same with just "DESeq(dds)"?

# Retrieve normalized counts
normcounts <- counts(dds, normalized=TRUE) <----------- How can I apply this to dds? dds$countData <- normcounts?

dds <- DESeq(dds)
res <- results(dds,contrast = c("condition",t,c))

This is all answered in the vignette which you even reference (what DESeq() does, how to get normalized counts and to use raw counts). I also answered your other question before that default normalization is what people nowadays call RLE deseq2 RLE normalization

You even reference the vignette, but if you don't trust it (written by the developer) then I don't see what anyone could say to help you.