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  • Question related to analysis using DESeq2

    Hi All,

    I have expression data for a mutant where a drug is injected over time with increasing dosage (no replicates and no drug was injected at time 0). I would like to identify genes that change in expression significantly over time. I used the following design in DEseq2 where time is assumed to be a continuous variable.

    ddsp <- DESeqDataSet(dds, ~ time)
    ddsp <- DESeq(ddsp, test="LRT", reduced = ~ 1)
    resp <- results(ddsp)

    is this the correct design? since I have only one mutant, are there other possible ways to answer the same question?

  • #2
    If time is a numeric vector:

    class(dds$time)

    then this will find genes which have linear relationship between log counts and time.

    (note to other people reading the thread: if you have replicates, it's preferable to include time as a factor, such that each time point gets its own parameter in the model)

    If you have more than 3 time points, you can include a quadratic term, which will also find genes which have up-down or down-up patterns in addition to linear relationships. That would look like a design of ~ time + I(time^2) and reduced design ~ 1.

    Comment


    • #3
      Thanks Michael ,

      To follow up on my original post, I am trying to compare the results between DESeq and DESeq 2 and found the output was very different (correlation=0.59), is that expected? I have read other posts and learned that we expect more genes to be differentially expressed using DESeq2 compared to DESeq which is what I got. The code for DESeq follows


      ### using DESeq ##############
      cds = newCountDataSet(data, expdesign)
      cds <- estimateSizeFactors( cds )
      sizeFactors( cds )
      cds = estimateDispersions( cds, method="blind", sharingMode="fit-only" )
      fit1 <- fitNbinomGLMs( cds, count ~ time )
      fit0 <- fitNbinomGLMs( cds, count ~ 1 )
      res<-nbinomGLMTest( fit1, fit0 )

      Comment


      • #4
        question related to analysis using DESeq2

        Hi,

        when using the following design to test for differentila expression

        ddsTC <- DESeqDataSet(dds, ~ time)
        ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ 1)
        resTC <- results(ddsTC)

        How to interprest the log2foldchange in the results file?
        I find most of the genes with significant pvalues have a very low log2fold change.

        thanks


        Originally posted by Michael Love View Post
        If time is a numeric vector:

        class(dds$time)

        then this will find genes which have linear relationship between log counts and time.

        (note to other people reading the thread: if you have replicates, it's preferable to include time as a factor, such that each time point gets its own parameter in the model)

        If you have more than 3 time points, you can include a quadratic term, which will also find genes which have up-down or down-up patterns in addition to linear relationships. That would look like a design of ~ time + I(time^2) and reduced design ~ 1.

        Comment


        • #5
          Read the section on the likelihood ratio test in the help pages:

          ?results

          Comment


          • #6
            Thanks Michael for your help
            I have one more question , do I need to perform a between-sample normalization before using the following design ?

            ddsTC <- DESeqDataSet(dds, ~ time)
            ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ 1)
            resTC <- results(ddsTC)

            thanks

            Comment


            • #7
              Any help will greatly be appreciated.

              Comment


              • #8
                the 'DESeq' function does normalization internally

                Comment

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