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  • TheSeqGeek
    Member
    • Feb 2014
    • 40

    #1

    DESeq2 p-adj or p-value

    What is more accurate to use the padj or pvalue in RNA-seq?


    Additionally, after my analysis I got a bunch NA for p-values.

    Anyone know why this is the case or how to fix it?

    Some have padj values but not pval and some both show NA for values.

    Any thoughts?
  • areyes
    Senior Member
    • Aug 2010
    • 165

    #2
    Hi @TheSeqGeek,

    You have to use the adjusted p-values.

    Everytime you do multiple tests (not only with RNA-seq data), you need to correct for what you would expect to be significant just by the number of tests you are performing. Say, if you tests 1000 hypothesis, you expect 100 (10%) of your data to have a p-value lower than 0.1. The adjusted p-values will already consider and correct for this.

    The wikipedia page for "Multiple comparisons problem" can be of more help, the references from that page will point to relevant literature and methods for correcting for multiple testing.

    Comment

    • Wallysb01
      Senior Member
      • Feb 2011
      • 286

      #3
      The “NA” issue is from not having enough read support to do a test. I believe the default is 10, but you can change that. This helps you because you aren’t going to be even attempting tests on lowly expressed genes, thereby lessing the impact of multiple testing corrections. In my samples, it seems about half of the genes aren’t even tested. So, with maybe ~22K genes or so, this means 11K tests to do multiple-test corrections on.

      Comment

      • TheSeqGeek
        Member
        • Feb 2014
        • 40

        #4
        Thank you for the quick reply.

        With regard to NA. Some of the samples that have NA have more than 100 mapped reads. Is the 10 referring to number of mapped reads or the difference between conditions?

        Comment

        • dpryan
          Devon Ryan
          • Jul 2011
          • 3478

          #5
          It's likely that many of the genes were either flagged as having outlier samples (see discussion of Cook's distance in the vignette) or were simply filtered during the automatic independent filtering step, which increases power.

          Comment

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