EBSeq-HMM: A Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

Ning Leng, Yuan Li, Brian E. McIntosh, Bao Kim Nguyen, Bret Duffin, Shulan Tian, James A. Thomson, Colin N. Dewey, Ron Stewart, Christina Kendziorski

Research output: Contribution to journalArticlepeer-review


Motivation: With improvements in next-generation sequencing technologies and reductions in price, ordered RNA-seq experiments are becoming common. Of primary interest in these experiments is identifying genes that are changing over time or space, for example, and then characterizing the specific expression changes. A number of robust statistical methods are available to identify genes showing differential expression among multiple conditions, but most assume conditions are exchangeable and thereby sacrifice power and precision when applied to ordered data. Results: We propose an empirical Bayes mixture modeling approach called EBSeq-HMM. In EBSeq-HMM, an auto-regressive hidden Markov model is implemented to accommodate dependence in gene expression across ordered conditions. As demonstrated in simulation and case studies, the output proves useful in identifying differentially expressed genes and in specifying gene-specific expression paths. EBSeq-HMM may also be used for inference regarding isoform expression. Availability and implementation: An R package containing examples and sample datasets is available at Bioconductor.

Original languageEnglish (US)
Pages (from-to)2614-2622
Number of pages9
Issue number16
StatePublished - Jan 19 2015

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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