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  • [DESeq2] multiple variables analysis

    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' ?

  • #2
    The Wald test and the Likelihood ratio test are just different statistical tests (like the t-test vs the rank test). With this setup, they are testing for the same thing: differences due to A controlling for B. In DESeq2, they tend to perform very similarly in these two group comparisons.

    If there were more than 2 levels to A, then the LRT is testing all levels at once, while the Wald test in DESeq2 is for comparing two groups, using 'contrast'. Since A has only two levels, the Wald and LRT are testing the same thing here.

    Note that for the second, you can just do:

    dds <- DESeq(dds, test="LRT", reduced=~B)

    Comment


    • #3
      Originally posted by Michael Love View Post
      The Wald test and the Likelihood ratio test are just different statistical tests (like the t-test vs the rank test). With this setup, they are testing for the same thing: differences due to A controlling for B. In DESeq2, they tend to perform very similarly in these two group comparisons.

      If there were more than 2 levels to A, then the LRT is testing all levels at once, while the Wald test in DESeq2 is for comparing two groups, using 'contrast'. Since A has only two levels, the Wald and LRT are testing the same thing here.

      Note that for the second, you can just do:

      dds <- DESeq(dds, test="LRT", reduced=~B)
      Thang you!

      Comment

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