The authors provide a step-by-step guide and outline a strategy using currently available statistical tools that results in a conservative list of differentially expressed genes. We also discuss potential sources of error in RNA-Seq analysis that could alter interpretation of global changes in gene expression.
When comparing statistical tools, the negative binomial distribution-based methods, edgeR and DESeq, several limitations of these analytic tools were revealed, including evidence for overly stringent parameters for determining statistical significance of differentially expressed genes as well as increased type II error for high abundance transcripts.
Because of the high variability between methods for determining differential expression of RNA-Seq data, the authors suggest using several bioinformatics tools, as outlined here, to ensure that a conservative list of differentially expressed genes is obtained. They also conclude that despite these analytical limitations, RNA-Seq provides highly accurate transcript abundance quantification that is comparable to qRT-PCR.
- Yendrek CR, Ainsworth EA, Thimmapuram J. (2012) The bench scientist’s guide to statistical analysis of RNA-Seq data. BMC Res Notes 5(1), 506. [article]