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  • tellsparck
    replied
    Thanks for the quick replies, dpryan and Michael Love!

    Leave a comment:


  • Michael Love
    replied
    Devon's always got the right answer

    If you want to know more about filtering out genes with few counts, we have a section on this (independent filtering) in the "Theory" part of the DESeq2 vignette.

    A reference on independent filtering:

    Bourgon R, Gentleman R, Huber W.
    "Independent filtering increases detection power for high-throughput experiments."
    Proc Natl Acad Sci U S A. 2010 May 25;107(21):9546-51. doi: 10.1073/pnas.0914005107. Epub 2010 May 11.
    With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by …

    Leave a comment:


  • dpryan
    replied
    DESeq2 will filter them out for you, you needn't do it manually.

    Leave a comment:


  • What to do with genes with very low counts in a dataset

    Hi all,
    I have a dataset from a single cell RNASeq project and there are many genes with very few counts (1 or 2 in a few samples) in the data set. While this is important info (tells us that these genes are not expressed), how would one handle them for a differential expression analysis (I use DESeq2)? Should I delete them or keep them? For me this means the difference between having a dataset with either ~17000 genes or ~11,500 genes (if I exclude genes with an average count less than 3).
    Thanks for your thoughts on this!

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