Hi, I am currently performing differential expression analysis using DESeq2.

I want to filter out lowly expressed genes, although I read on another post here that this may not be necessary because IndependentFiltering within results() kind of does that. However, I am comparing different approaches for differential expression analysis and I need to follow the same "criteria" kind of.

What I want to know is, what is the difference between

Code should be placed in three backticks as shown below

DESeqDataSetFromMatrix()
# and
DESeq()

I have seen some performing filtering before utilising DESeq() function

dds <- DESeqDataSetFromMatrix(countData = countData,
                              colData = metaData,
                              design = ~ condition) 

keep <- rowSums(counts(dds) >= 10) >= 10
dds <- dds[keep,]

dds <- DESeq(dds)
normalizedCounts <- counts(dds, normalized=TRUE)

Whilst the developer utilised DESeq() function and then performed filtering

dds <- DESeqDataSetFromMatrix(countData = countData,
                              colData = metaData,
                              design = ~ condition) 
dds <- DESeq(dds)
dds <- estimateSizeFactors(dds)

# Apply the filtering criteria
idx <- rowSums(counts(dds, normalized=TRUE) >= 10) >= 10
dds <- dds[idx,]

dds <- DESeq(dds)

So I just want to understand which approach is the right one and why :)

Thanks

Please follow the manual.

• Make the dataset with DESeqDataSetFromMatrix()
• Apply prefiltering, either as in https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#pre-filtering or use something like edgeR::filterByExpr, to get what in the vignette is called keep
• filter the dataset via dds <- dds[keep,]
• run DESeq()
• get results via results() or lfcShrink(), see vignette which covers all that

This is estimateSizeFactors() is part of DESeq() so skip that. Filter on raw, nor normalized counts, see vignette.