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  • DESeq2: bad experimental design and "not full rank" problems

    Hi all.

    In my lab, we have some old RNA-seq data that were produced by other users in the past. The experimental design was completely wrong, but I would like to analyze them in order to decide whether I should repeat the experiments or drop the whole idea altogether. I have already tried a few things, but I am afraid that all I see is technical noise.

    Code:
    Sample	Condition	Prep	Treatment
    1	KD	A	shRNA
    2	KD	A	shRNA
    3	KD	A	shRNA
    4	ctrl	B	shRNA
    5	ctrl	B	shRNA
    6	ctrl	B	shRNA
    7	ctrl	B	shRNA
    8	wt	C	no
    9	wt	C	no
    10	wt	C	no
    11	wt	C	no
    12	wt	C	no
    13	wt	C	no
    14	wt	C	no
    I have performed DE analysis of KD vs ctrl and I got 1200 DE genes. Using contrasts, I have check wt vs ctrl and I got 70 DE genes. However, the general profile of both comparisons seems very similar (through heatmaps). Is there a way to correct for the different preps? I have seen in my recent data that the batch effect can be huge, but is there a way to extract some meaningful information from this set of data? Or it’s not worth trying to make sense of them at all?

    When I tried ~ Treatment*Condition or ~ Prep + Condition, I got the error that the model matrix is not full rank (obviously).

    Any suggestions will be greatly appreciated, or any experiences with problematic experimental designs!

    Cheers
    --Katerina

  • #2
    If you happen to have a set of genes that you know a prioi to be constantly expressed regardless of treatment then you could use those for normalization and correcting the batch effect correction. If not, there's nothing you can do.

    Comment


    • #3
      Yes, I was afraid of that.

      I have already considered such a solution, but I do not really *know* of such genes. I can make some educated guesses but that's about it.

      Thanks anyway.
      --Katerina

      Comment

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