1. EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments
- Author
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Yuan Li, James A. Thomson, Bret Duffin, Christina Kendziorski, Ron Stewart, Ning Leng, Shulan Tian, Brian E. McIntosh, Colin N. Dewey, and Bao Kim Nguyen
- Subjects
Statistics and Probability ,Gene isoform ,Computer science ,Bayesian probability ,Inference ,RNA-Seq ,computer.software_genre ,Biochemistry ,Bioconductor ,Gene expression ,Humans ,Hidden Markov model ,Molecular Biology ,Gene ,Regulation of gene expression ,Sequence Analysis, RNA ,Gene Expression Profiling ,High-Throughput Nucleotide Sequencing ,Bayes Theorem ,Original Papers ,Expression (mathematics) ,Computer Science Applications ,Gene expression profiling ,Computational Mathematics ,Gene Expression Regulation ,Computational Theory and Mathematics ,Data mining ,Sequence Analysis ,computer ,Software - Abstract
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. Contact: kendzior@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
- Published
- 2015