So I create a DeseqDataset like this:
dds<-DESeqDataSetFromMatrix (countData=countdata,colData=coldata,design=~A+B)
here A is one factor containing levels A1. A2 ; B is the other factor containg levels B1. B2
So the default
ddsMF<-DESeq(dds)
will tell me the influence of B1 and B2
-----
IF I want to look into factor A . I have two methods
first: results(ddsMF,contrast=c('A','A1','A2'))
second: dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds,full=design(dds), reduced = ~ B)
So I want to know : what is the difference between above two methods for 'A' ?
dds<-DESeqDataSetFromMatrix (countData=countdata,colData=coldata,design=~A+B)
here A is one factor containing levels A1. A2 ; B is the other factor containg levels B1. B2
So the default
ddsMF<-DESeq(dds)
will tell me the influence of B1 and B2
-----
IF I want to look into factor A . I have two methods
first: results(ddsMF,contrast=c('A','A1','A2'))
second: dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds,full=design(dds), reduced = ~ B)
So I want to know : what is the difference between above two methods for 'A' ?
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