On taming the effect of transcript level intra-condition count variation during differential expression analysis: A story of dogs, foxes and wolves
Fig 2
Over expressed transcripts pre- and post-filtering using simulated data.
The number of transcripts identified by DESeq2 as being over expressed both prior to (light gray) and post (dark gray) filtering of count datasets within each of the one hundred iterations performed at each level of introduced random intra-condition count variation. Each iteration involved initially simulating ten count datasets divided into conditions A and B following which DESeq2 was run to attempt to identify the one hundred transcripts selected for over representation as described in the methods. Following this the ten simulated datasets were filtered using TVscript with a 95th percentile threshold to generate corresponding filtered datasets (divided into corresponding conditions A’ and B’) on which DESeq2 was re-run.