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  • DESeq2 Multiple Contrast Correction

    Should an additional p-value correction be made for running multiple contrasts? Say for example I have 12 samples and with one factor that has 4 levels, A, B, C, and D.

    No code has to be inserted here.If I run DESeq using Wald tests to determine the differentially expressed genes and then compare each level to the others using contrasts like this:

    Code:
    dds <- DESeqDataSetFromMatrix(countData = countData,
            colData = colData,
            design = ~ condition)
    
    dds <- DESeq(dds)
    
    results(dds, contrast=c("condition","A","B"))
    results(dds, contrast=c("condition","A","C"))
    results(dds, contrast=c("condition","A","D"))
    results(dds, contrast=c("condition","B","C"))
    results(dds, contrast=c("condition","B","D"))
    results(dds, contrast=c("condition","C","D"))
    Does running multiple contrasts require an additional correction to the adjusted p-value reported in the results object?

  • #2
    We only provide automatic adjustment in the gene dimension. The decideTests function in limma does provide functionality for comparing across contrasts. when I see what appears to me to be opportunistic pair-wise comparisons, I just try to discourage it in favor of EDA: e.g. progammatically testing hundreds of pairs instead of having a preset hypothesis.

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