title: EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.
abstract: BACKGROUND
  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.
  
  
  METHODS
  An R package containing examples and sample datasets is available at Bioconductor.
  
  
  BACKGROUND
  kendzior@biostat.wisc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, WI, USA, Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Ning
authors.initials: N
authors.lastname: Leng
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, WI, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Li
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Brian E
authors.initials: BE
authors.lastname: McIntosh
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Bao Kim
authors.initials: BK
authors.lastname: Nguyen
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Bret
authors.initials: B
authors.lastname: Duffin
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Shulan
authors.initials: S
authors.lastname: Tian
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA, Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA, Department of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, CA, USA and.
authors.forename: James A
authors.initials: JA
authors.lastname: Thomson
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
authors.forename: Colin N
authors.initials: CN
authors.lastname: Dewey
authors.affiliation: Regenerative Biology, Morgridge Institute for Research, Madison, WI, USA.
authors.forename: Ron
authors.initials: R
authors.lastname: Stewart
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
authors.forename: Christina
authors.initials: C
authors.lastname: Kendziorski
mesh_terms: D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
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