  High-throughput sequencing of RNA transcripts (RNA-seq) has become the method
of choice for detection of differential expression (DE). Concurrent with the
growing popularity of this technology there has been a significant research
effort devoted towards understanding the statistical properties of this data
and the development of analysis methods. We report on a comprehensive
evaluation of the commonly used DE methods using the SEQC benchmark data set.
We evaluate a number of key features including: assessment of normalization,
accuracy of DE detection, modeling of genes expressed in only one condition,
and the impact of sequencing depth and number of replications on identifying DE
genes. We find significant differences among the methods with no single method
consistently outperforming the others. Furthermore, the performance of
array-based approach is comparable to methods customized for RNA-seq data.
Perhaps most importantly, our results demonstrate that increasing the number of
replicate samples provides significantly more detection power than increased
sequencing depth.
