RNAseq: Does it Really Matter How You Analyze the Data?

This is certainly an interesting paper that will make the statisticians working on low-level processing of RNAseq data unhappy. Our experience has been in agreement with their final statement -

Our results suggest that RNA-Seq analysis should be extremely biology-aware, and special effort should be devoted to optimizing the last stage of the analysis, i.e. search for the functional patterns that form a unique signature of cellular response to the conditions of interest.

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Making sense of RNA-Seq data: from low-level processing to functional analysis

Numerous methods of RNA-Seq data analysis have been developed, and there are more under active development. In this paper, our focus is on evaluating the impact of each processing stage; from pre-processing of sequencing reads to alignment/counting to count normalization to differential expression testing to downstream functional analysis, on the inferred functional pattern of biological response. We assess the impact of 6,912 combinations of technical and biological factors on the resulting signature of transcriptomic functional response. Given the absence of the ground truth, we use two complementary evaluation criteria: a) consistency of the functional patterns identified in two similar comparisons, namely effects of a naturally-toxic medium and a medium with artificially reconstituted toxicity, and b) consistency of results in RNA-Seq and microarray versions of the same study. Our results show that despite high variability at the low-level processing stage (read pre- processing, alignment and counting) and the differential expression calling stage, their impact on the inferred pattern of biological response was surprisingly low; they were instead overshadowed by the choice of the functional enrichment method. The latter have an impact comparable in magnitude to the impact of biological factors per se.