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  • Independent filtering by using fold change in DESeq2

    Hi everyone,
    Thanks for reading my post.

    I know DESeq2 uses normalized mean count as independent filtering to improve the performance of multiple testing error correction. I would like to know is it possible I can add the criteria, |log2FC| > 1, together with normalized mean count as the independent filtering? If so, how could I add this criteria? by using "filter=" or "filterFun=" argument? How to write the command?

    What I know now is to change the "lfcThreshold=" to 1, but this only change the null hypothesis from 0 to 1, not actually filter any genes.

    Many Thanks in advance!

  • #2
    No you can't use fold-change in independent filtering, it wouldn't then be independent. You can filter genes after the fact for the fold-change threshold you want.

    Comment


    • #3
      Originally posted by dpryan View Post
      No you can't use fold-change in independent filtering, it wouldn't then be independent. You can filter genes after the fact for the fold-change threshold you want.
      Hi dpryan,
      Thanks for your reply. You mean I should run DESeq for DE analysis first and then filter out the genes with small fold change? If so, could I only perform multiple testing error correction for those genes remained instead of all the genes tested? Since this would give me more significant DE genes with big fold change.

      Thanks.

      Comment


      • #4
        But that is a violation of independent filtering principle, you cannot filter for something you are testing for. Obviously it is great for your p-values, but also completely invalid and should be considered p-value-hacking.

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        • #5
          Originally posted by wdecoster View Post
          But that is a violation of independent filtering principle, you cannot filter for something you are testing for. Obviously it is great for your p-values, but also completely invalid and should be considered p-value-hacking.
          Hi wdecoster,
          Thanks for you reply.
          So nothing we can do by using fold change to filter the genes? any suggestion? Thanks

          Comment


          • #6
            You can filter genes by fold-change after calculating p-values and adjusting for multiple testing. If you do anything like that beforehand odds are good that you'll end up with me as a reviewer and I will guarantee that your paper is rejected.

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