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  • #16
    Originally posted by areyes View Post
    Hi Jose,

    The default formula would test for differences in exon usage in any of the conditions.

    If you are interested in the specific changes between H24-CTRL and between H48-CTRL it is better to subset the data accordingly and run the analysis separately.

    Alejandro

    Hi, Alejandro

    I have a similar situation. One thing that I am a little worried is, when seperating the data into 2 (part 1 with H24 and control, and part 2 with H48 and control) and do whole DEXSeq analyses seperately, the adjusted p-values may not have been adjusted enough. The BH correction may need to be applied to all the p-values of the tests from the 2 analyses. I am not sure if I am right or not on this?

    Thanks,
    Xiayu

    Comment


    • #17
      Hello all,
      I am having a similar problem to the posts above. I have been using edgeR for gene level expression differences and need help translating the contrasts I have been using in edgeR over to a factorial model in DEXseq.

      In edgeR I have combined all of my experimental factors into one, for example:
      Sample Group
      Sample1 Drug.2h
      Sample2 Drug.0h
      Sample3 Placebo.2h
      Sample4 Placebo.0h
      ect for replicates...

      and contrast setup is nearly identical to one described in the edgeR vignette:
      (Drug.2h-Drug.0h)-(Placebo.2h-Placebo.0h)

      However I am at a loss at how do to this comparison using a factorial model in DEXseq.
      Should I break up my factors like so:

      Sample Treat Time
      1 Sample1 Placebo 0h
      2 Sample2 Placebo 0h
      5 Sample5 Placebo 2h
      6 Sample6 Placebo 2h
      7 Sample1 Drug 0h
      8 Sample2 Drug 0h
      11 Sample5 Drug 2h
      12 Sample6 Drug 2h


      and then use the following factorial models?
      formulaFullModel = ˜ sample + exon + treatment:exon + time:exon + treatment:time:exon
      formulaReducedModel = ˜ sample + exon + treatment:exon + time:exon

      Thanks ahead of time for your consideration.

      Comment


      • #18
        Hi @GreboGuru,

        I think that looks reasonable, you would be testing for those cases where the treatment has a specific effect in exon usage in a specific time point.

        Alejandro

        Comment


        • #19
          Alejandro,
          Hello back and thank you for your feedback, it is invaluable. Running the analysis now.

          Comment


          • #20
            See its paper: http://genome.cshlp.org/content/22/10/2008.full
            Note that we test against the null hypothesis that none of the conditions influences exon usage, and hence, if there are more than two different conditions ρ, we aim to reject the null hypothesis already if any one of the conditions causes differential exon usage.
            So I think the answer is Yes

            Comment


            • #21
              Continuing on my post above, I am now trying to generate the fold change between treatments corrected by the effect of time. Using the nomenclature from my post above this is the fold change I would like to generate : (Drug.2h/Drug.0h)/(Placebo.2h/Placebo.0h).

              If you will remember, DEU was tested for using the following formulae :

              formulaFullModel = ˜ sample + exon + treatment:exon + time:exon + treatment:time:exon
              formulaReducedModel = ˜ sample + exon + treatment:exon + time:exon

              I am now trying to generate the fold changes that occur using the testForDEU() function. However, this command only takes one variable and my model uses two.

              In my scenario (see post above for full explanation) I am thinking that the model that needs to be fit in testForDEU() is '~ sample + time*treatment* exon. With the denominator being 'Placebo'

              My questions are: is this the right model to use to obtain the desired fold changes?
              can this model be used in testForDEU() with some small modifications?

              Comment


              • #22
                I'm using DEXSeq for the analysis of 16 human tissues. I'm trying to identify exons that are differentially expressed in one tissue against all other tissues. I ran the whole pipeline of DEXSeq on all the sample for all tissues. What I understand is when I have exon with differential usage in the results table, that means it is differentially expressed in at least one tissue but I can't determine which one. I also can't determine if it is differentially expressed on more than one tissue, is that right? is there anyway to know such information?

                Comment


                • #23
                  Hi,

                  I want to conduct DEU analysis for three different tissues of different conditions namely F, LR and LR.
                  Here I used the code in DEXseq package of R to make sample table and design. The input data is counts.txt file from FeatureCounts Here I used the code

                  Code:
                  sampleTable = data.frame(
                    row.names = c("F1","F2","LR1","LR2","LV1","LV2"),
                    condition = factor(rep(c("F","LR","LV"),each=2)),
                    libType = c("paired-end","paired-end","paired-end", "paired-end","paired-end", "paired-end"))
                  
                  sampleTable
                  
                  dxd <- DEXSeqDataSet(cc,design=~sample + exon + condition:exon, featureID=as.factor(exon_ids),
                                       groupID=as.factor(clusters),sampleData=sampleTable)
                  but I only get 'exon fold change' in between F and LR, F and LV but I could not get the exon fold change between LV and LR. kindly suggest the solution to get comparision in between all three conditions.

                  Thank you,

                  Aniket.

                  Comment

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