A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. Researchers from the Memorial Sloan-Kettering Cancer Center performed a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. They considered a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. They found significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Their results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.
- F, Khanin R, Liang Y, Pirun M, Krek A, Zumbo P, Mason CE, Socci ND, Betel D. (2013) Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data. Genome Biol 14(9), R95. [abstract]