Hi all,
In the 2013 nature RNA-seq protocol by Anders S. et al. (http://www.nature.com/nprot/journal/....2013.099.html), they state: "In edgeR, it is recommended to remove features without at least 1 read per million in n of the samples, where n is the size of the smallest group of replicates"
In my case, this would be filtering on n=15 of a total of 96 samples.
Would you always apply low count-filtering regardless of DEG analysis method? (DESeq2, edgeR, limmaVoom) or would you simply include all genes and then afterwards look at the count values for any identified DEG?
Cheers,
Leon
In the 2013 nature RNA-seq protocol by Anders S. et al. (http://www.nature.com/nprot/journal/....2013.099.html), they state: "In edgeR, it is recommended to remove features without at least 1 read per million in n of the samples, where n is the size of the smallest group of replicates"
In my case, this would be filtering on n=15 of a total of 96 samples.
Would you always apply low count-filtering regardless of DEG analysis method? (DESeq2, edgeR, limmaVoom) or would you simply include all genes and then afterwards look at the count values for any identified DEG?
Cheers,
Leon
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