A Statistical Method for Detecting Differentially Expressed SNVs Based on Next-Generation RNA-seq Data

作者：Fu, Rong; Wang, Pei^*; Ma, Weiping; Taguchi, Ayumu; Wong, Chee-Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M.; Zhou, Qinghua; Zhong, Hua; Feng, Ziding

来源：Biometrics, 2017, 73(1): 42-51.

DOI：10.1111/biom.12548

• Allele-specific expression
• Breast cancer tumors
• Differential expression
• Likelihood ratio test
• RNA-seq

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.

• 出版日期2017-3
• 单位四川大学