alexa EBSeq-HMM: a Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Data Mining in Genomics & Proteomics

Author(s): Leng N, Li Y, McIntosh BE, Nguyen BK, Duffin B,

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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: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.
This article was published in Bioinformatics and referenced in Journal of Data Mining in Genomics & Proteomics

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