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  • Cuffdiff time series with two groups

    Hi!
    I have 4 sample groups. Diabetics and healthy at two time point, before and after intervention.

    So, how can I analyse changes in gene expression in the diabetic groups, CONTROLLED for by the healthy group.

    This means I have two discrete groups, but at two timepoint.

    How to tell this to Cuffdiff?

    Thanks!

  • #2
    You can't, you'll have to use something else. Cuffdiff is only useful for very basic designs.

    Edit: I should note that you could make the pair-wise comparisons with cuffdiff, but that's not what you want. Give DESeq2/edgeR/limma a try, they're MUCH more flexible.
    Last edited by dpryan; 10-10-2013, 11:46 AM.

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    • #3
      Wow. Thats a bummer.

      What do you suggest? Im currently testing EdgeR.

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      • #4
        I'm quite happy with DESeq2. I've used edgeR again recently but have been a bit unhappy with it calling things significant due simply to an outlier sample (DESeq2 flags these on a per-gene basis, though I've also been bitten by this once in a partial knock-out dataset).

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        • #5
          Ok, sounds like DESeq is the way to go.

          Thanks a lot!

          Im thinking of running Cuffdiff 2.1.1, EdgeR & DEseq. Then compare everything and decide what to use in the publication.

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          • #6
            Originally posted by sindrle View Post
            Im thinking of running Cuffdiff 2.1.1, EdgeR & DEseq. Then compare everything and decide what to use in the publication.
            I suspect that's a pretty common route people take. Just make sure to validate a few candidates and then base your tool of choice on that rather than simply which gives the bigger list of DE genes!

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            • #7
              Thanks for the advice!
              Any suggestion on candidates btw? I already have 5 genes tested with qPCR.

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              • #8
                Try to pick a couple that aren't called DE by all of them. That should help determine which of the models better fit your dataset.

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                • #9
                  Thats a great tip!!
                  Thank you.

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                  • #10
                    Can you please guide me on using DEseq2 for this purpose?
                    I dont quite understand how to input:

                    Healthy at baseline, healthy at timepoint 2
                    &
                    Normals at baseline, normals at timepoint 2

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                    • #11
                      Originally posted by sindrle View Post
                      Can you please guide me on using DEseq2 for this purpose?
                      I dont quite understand how to input:

                      Healthy at baseline, healthy at timepoint 2
                      &
                      Normals at baseline, normals at timepoint 2
                      There are two ways one could look at that, I'll just give you one. Suppose you had 8 samples evenly divided into timepoints and normal/healthy groups:

                      Code:
                      status <- factor(c(rep("healthy",4), rep("normal",4)), levels=("normal", "healthy")
                      timepoints <- factor(c(rep(c(1,2), 4)))
                      des <- formula(~timepoints+status)
                      You can then use "des" as the design for your experiment. Alternatively, swap a "*" for the "+" on the last line to include an interaction term, which you probably want.

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                      • #12
                        You are awsome!
                        Some day I hope Ill repay in some way

                        Comment

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