Hi

I'd like to use EDAseq normalized data as input of DESeq2.

I found the vignette for DESeq but not for DESeq2.

Is there a way to do DESeq2 DEA starting from  EDAseq normalized data ? 

I understand I can use this

dds <- DESeqDataSetFromMatrix(countData = cts,

                              colData = coldata,

                              design = ~ condition)

to extract the data I need and then use them in the formula BUT

doing this I miss all the information of the SeqExpressionSet object and the normalizations done by EDAseq. 

Thanks,

Pietro

Hi Pietro,

first, the DESeq2 vignette can be found at this link.

As you will see, Mike did a great job at covering as many use cases as possible and he has a section on how to import gene-specific size factors from EDASeq and similar packages: https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#sample-gene-dependent-normalization-factors

In short, you have to use the offset=TRUE option in EDASeq to compute the offsets and pass them to DESeq2 as explained in the DESeq2 vignette (note the transformation!).

Sorry, I was unclear in my question.

I know the DESeq2 vignette but in the EDAseq I didn't find how to handle DESeq2 while there is EgdeR and DESeq.

I was wondering if there was something like this (reported in EDAseq):

## ----deseq------------------------------------------------

library(DESeq)

counts <- as(dataNorm, "CountDataSet")

sizeFactors(counts) <- rep(1,4)

counts <- estimateDispersions(counts)

res <- nbinomTest(counts, "wt", "mut")

head(res)

BUT for DESseq2.

What I do is: 

## ----deseq2------------------------------------------------

library(DESeq2)
# data is data from EDAseq example
dataOffset <- withinLaneNormalization(data,"gc",
                                      which="full",offset=TRUE)
dataOffset <- betweenLaneNormalization(dataOffset,
                                       which="full",offset=TRUE)

dds <- DESeqDataSetFromMatrix(countData = dataOffset@assayData$normalizedCounts,
                              colData = pData(dataOffset),
                              design = ~ conditions)

EDASeqNormFactors <- exp(-1 * offst(dataOffset))
normalizationFactors(dds) <- EDASeqNormFactors
# dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomWaldTest(dds)
res <- results(dds)
res <- res[order(res$pvalue),]
head(res)

Thank you,

P