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  • Need advice on analysing RNA-seq time series

    Hi everyone,

    I have performed RNA-seq from different stages of embryogenesis. Some stages have two, three or four replicates, while some have none. I want to identify a set of genes that I can say are associated with embryogenesis in general - of all stages.

    What I have done so far is to use DESeq2 and performed pairwise DE-tests between all the different stages and extracted the significant genes from each test, pooled them and extracted the unique genes. But I am not sure if this is the best way (or even a correct way) to do it. And I guess I should perform some kind of statistical test on my procedure, like permutations or something.

    Any advice on how to analyse this data is greatly appreciated.

    Thanks,

    Jon

  • #2
    hi Jon,

    The likelihood ratio test (see the section in the vignette) can be used to test for differences at any stage. Here, you specify a reduced formula where you remove the stage variable from the design (so if your design is ~ stage, then reduced = ~ 1).

    Mike

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    • #3
      Originally posted by Michael Love View Post
      hi Jon,

      The likelihood ratio test (see the section in the vignette) can be used to test for differences at any stage. Here, you specify a reduced formula where you remove the stage variable from the design (so if your design is ~ stage, then reduced = ~ 1).

      Mike
      Thanks!

      Jon

      Comment


      • #4
        Originally posted by Michael Love View Post
        hi Jon,

        The likelihood ratio test (see the section in the vignette) can be used to test for differences at any stage. Here, you specify a reduced formula where you remove the stage variable from the design (so if your design is ~ stage, then reduced = ~ 1).

        Mike
        Dear Mike,

        Sorry for asking novice questions.
        I have only one variable to my design (condition) that includes all the different stages as well as the non-reproductive stages (which I want to use as controls). When I created the dataset I used design = ~ condition, but will this be correct if I want to use reduced = ~ 1 as you suggested?

        Jon

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        • #5
          Using full = ~ condition and reduced = ~ 1 tests for any difference in counts related to the levels of condition. this will include genes where stage 1 is different than control, and genes where stage 2 is different than stage 1, etc.

          Comment


          • #6
            The basic idea here is you’re looking for genes in which the beta term in the equation expression = beta * stage + constant is significantly different from zero. This is a generalized linear model.

            So you’re not going to get genes that are just different between stage 1 and stage 2, then do what ever else in stages 3-5 or what not. You’re going to be finding genes that consistently show a trend, up or down, as stage progresses.

            You can also put any other confounding variables in there. So if you have different batches of embryos in any way or different sequencing run/library prep days, you may want to try to control for that by adding another variable (design = ~ condition + batch, reduce = ~ batch).

            Comment


            • #7
              hi Wally,

              re: "show a trend" that would be true if stage was a numeric, but I guess Jon coded it is a factor.

              Comment

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