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
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
![Big Grin](https://www.seqanswers.com/core/images/smilies/biggrin.png)
B:C 676 523 972 640 676
B
![Big Grin](https://www.seqanswers.com/core/images/smilies/biggrin.png)
C
![Big Grin](https://www.seqanswers.com/core/images/smilies/biggrin.png)
comparison of RNA-seq methods
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