1. A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data.
- Author
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Fu, Rong, Wang, Pei, Ma, Weiping, Taguchi, Ayumu, Wong, Chee‐Hong, Zhang, Qing, Gazdar, Adi, Hanash, Samir M., Zhou, Qinghua, Zhong, Hua, and Feng, Ziding
- Subjects
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RNA sequencing , *SINGLE nucleotide polymorphisms , *LIKELIHOOD ratio tests , *ALLELES , *STATISTICAL methods in mutation , *MATHEMATICAL models - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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