A Hierarchical Bayesian Model for Estimating and Inferring Differential Isoform Expression for Multi-Sample RNA-Seq Data.

ABSTRACT: RNA-Seq has drastically changed our ways of studying transcrip-tomes in providing more precise estimates of gene expression, including isoform-specific expression. Most of the available methods for RNA-Seq data focus on one sample at a time. We present in this paper a Poisson-Gamma hierarchical model for multi-sample RNA-Seq data analysis in order to simultaneously estimate isoform-specific expression and to identify differentially expressed iso-forms. Our model has the advantage of borrowing information across all samples in estimating expression levels, which can improve the estimates drastically, particularly for low abundance isoforms. Furthermore, our hierarchical model has the ability to account for overdispersion in the data and also can incorporate sample-specific covariates in the

SUBMITTER: Vardhanabhuti S 

PROVIDER: S-EPMC3669631 | biostudies-literature | 2013 May

REPOSITORIES: biostudies-literature