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  • rammohanshukla
    Junior Member
    • Mar 2013
    • 4

    Deseq2 design clarification

    Hi, I am working with human postmortem samples wherein I have 5 groups of subjects:

    A. Control: 20 samples
    B. First episode: 20 samples
    C. First episode Remission: 15 samples
    D. Second episode: 20 samples
    E. Second episode Remission: 15 samples


    Note that the progression of A to B to C to D to E is expected to be in phase (something like sinusoidal curve)

    I also have other parameters for the samples, which include: Age, Sex, RIN value, PMI (postmortem interval), pH of the sample


    I wish to test the difference of the 5 groups in ANOVA like analysis in DEseq2 but after controlling for Age, Sex, RIN value, PMI (postmortem interval), pH of the sample

    My phenotype table looks like this:

    Sample Age Sex Rin PMI pH DiseaseStage
    1 binned factor binned binned binned Control
    2 Control
    3 First episode
    . First episode
    . First episode
    . .
    . .
    90 Second episode Remission



    This is my design
    dds <- DESeqDataSet(se, design = ~ Age+Sex+RIN+PMI+pH+DiseaseStage)

    dds <- DESeq(dds, test = "LRT", reduced = ~ Age+Sex+RIN+PMI+pH)
    results(dds)

    I am hoping that I get 1 p-value for ANOVA like comparisons between the disease stage after controlling for Age, Sex, RIN, PMI and pH
    Also, I would like to know how many variables I can control for in the design? For instance, here I am using 5 (Age, Sex, RIN, PMI and pH) is it okay? If yes, is there any rule to how many variables to control for in the design?

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