A statistical method for detecting differentially expressed SNVs based on next‐generation RNA‐seq data. Issue 1 (8th June 2016)

• Record Type: Journal Article
• Authors: Fu, Rong; Wang, Pei; Ma, Weiping; Taguchi, Ayumu; Wong, Chee‐Hong; Zhang, Qing; Gazdar, Adi; Hanash, Samir M.; Zhou, Qinghua; Zhong, Hua; Feng, Ziding
• Is Part Of: Biometrics. Volume 73:Issue 1(2017)
• Page Start: 42
• Page End: 51
• Publication Date: 2016-06-08
• Subjects: Allele‐specific expression -- Breast cancer tumors -- Differential expression -- Likelihood ratio test -- RNA‐seq; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12548
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: 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.