  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.
