A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data

In this article, we propose a new statistical method—MutRSeq—for detecting differentially expressed single nucleotide variants (SNVs) based on RNA-seq data. Specifically, we focus on nonsynonymous mutations and employ a hierarchical likelihood approach to jointly model observed mutation events as well as read count measurements from RNA-seq experiments. We then introduce a likelihood ratio-based test statistic, which detects changes not only in overall expression levels, but also in allele-specific expression patterns. In addition, this method can jointly test multiple mutations in one gene/pathway. The simulation studies suggest that the proposed method achieves better power than a few competitors under a range of different settings. In the end, we apply this method to a breast cancer data set and identify genes with nonsynonymous mutations differentially expressed between the triple negative breast cancer tumors and other subtypes of breast cancer tumors.