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

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalBiometrics
DOIs
StateAccepted/In press - 2016

Keywords

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

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

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  • Cite this

    Fu, R., Wang, P., Ma, W., Taguchi, A., Wong, C. H., Zhang, Q., Gazdar, A., Hanash, S. M., Zhou, Q., Zhong, H., & Feng, Z. (Accepted/In press). A statistical method for detecting differentially expressed SNVs based on next-generation RNA-seq data. Biometrics. https://doi.org/10.1111/biom.12548