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.
Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A.
Rong
R
Fu
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
Pei
P
Wang
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
Weiping
W
Ma
Program of Molecular Diagnosis, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.|Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
Ayumu
A
Taguchi
Program of Molecular Diagnosis, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
Chee-Hong
CH
Wong
Program of Molecular Diagnosis, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.
Qing
Q
Zhang
Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas, U.S.A.
Adi
A
Gazdar
Program of Molecular Diagnosis, Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.|Department of Clinical Cancer Prevention, University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
Samir M
SM
Hanash
Sichuang Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
Qinghua
Q
Zhou
Department of Population Health, New York University, New York, U.S.A.
Hua
H
Zhong
Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
Ziding
Z
Feng
D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009154:Mutation; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods; D012984:Software
Allele-specific expression; Breast cancer tumors; Differential expression; Likelihood ratio test; RNA-seq