Open Access
June 2015 hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data
Shiqi Cui, Subharup Guha, Marco A. R. Ferreira, Allison N. Tegge
Ann. Appl. Stat. 9(2): 901-925 (June 2015). DOI: 10.1214/15-AOAS815


We introduce hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for potential co-expression of neighboring genes. In addition, hmmSeq employs an integrated approach to studies with technical or biological replicates, automatically adjusting for any extra-Poisson variability. Moreover, for cases when paired data are available, hmmSeq includes a paired structure between treatments that incoporates subject-specific effects. To perform parameter estimation for the hmmSeq model, we develop an efficient Markov chain Monte Carlo algorithm. Further, we develop a procedure for detection of differentially expressed genes that automatically controls false discovery rate. A simulation study shows that the hmmSeq methodology performs better than competitors in terms of receiver operating characteristic curves. Finally, the analyses of three publicly available RNA-seq data sets demonstrate the power and flexibility of the hmmSeq methodology. An R package implementing the hmmSeq framework will be submitted to CRAN upon publication of the manuscript.


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Shiqi Cui. Subharup Guha. Marco A. R. Ferreira. Allison N. Tegge. "hmmSeq: A hidden Markov model for detecting differentially expressed genes from RNA-seq data." Ann. Appl. Stat. 9 (2) 901 - 925, June 2015.


Received: 1 February 2014; Revised: 1 January 2015; Published: June 2015
First available in Project Euclid: 20 July 2015

zbMATH: 06499936
MathSciNet: MR3371341
Digital Object Identifier: 10.1214/15-AOAS815

Keywords: Bayesian hierarchical model , first order dependence , next-generation sequencing , overdispersion , serial correlation

Rights: Copyright © 2015 Institute of Mathematical Statistics

Vol.9 • No. 2 • June 2015
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