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  • ugolino
    replied
    Is my post poorly formulated or simply inane? I wonder why is it being ignored?

    thanks

    Leave a comment:


  • ugolino
    replied
    here is the table again

    DESeq edgeR
    comparison FDR 5% FC>2 FDR 5% FC>2 overlap
    A/B 599 467 896 604 599
    A/C 0 0 na
    A/D 550 400 879 573 548
    B/C 676 523 972 640 676
    B/D 0 0 na
    C/D 623 443 879 559 622

    Leave a comment:


  • advice on DESeq and EdgeR output from bacterial RNA-seq

    Hi there,

    I'm analyzing RNA-seq data from a bacterial monoculture exposed to 4 conditions. There are 3 bioreps for each condition and after aligning to reference using bowtie2, getting read counts with htseq (eliminated rRNA), I used both DESeq and edgeR (with TMM) to infer DE or genes. I hope the table below is clear. Column 2 and 3 are DESeq, 4 and 5 edgeR and col 6 is the overlap. FDR with BH correction, FC is fold change. EdgeR detects all the genes that DESeq detects as DE, and appreciably more. I'm really struggling to decide whether to accept only the overlap between the two, or give a chance to the genes additionally detected by edgeR. Recent literature implies slightly higher false pos rate for edgeR at n=3; on the other hand DESeq is sometimes qualified as over-conservative. But I'm no statistician, I'm a biologist and would much appreciate your insight.

    thanks!

    DESeq edgeR
    comparison FDR 5% FC>2 FDR 5% FC>2 overlap
    A:B 599 467 896 604 599
    A:C 0 0 na
    A 550 400 879 573 548
    B:C 676 523 972 640 676
    B 0 0 na
    C 623 443 879 559 622


    comparison of RNA-seq methods



    During the last 3 years, a number of approaches for the normalization of RNA sequencing data have emerged in the literature, differing both in the type of bias adjustment and in the statistical strategy adopted. However, as data continue to accumulate, there has been no clear consensus on the approp …

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