NPEBseq: nonparametric empirical bayesian-based procedure for differential expression analysis of RNA-seq data.

ABSTRACT:

Background

RNA-seq, a massive parallel-sequencing-based transcriptome profiling method, provides digital data in the form of aligned sequence read counts. The comparative analyses of the data require appropriate statistical methods to estimate the differential expression of transcript variants across different cell/tissue types and disease conditions.

Results

We developed a novel nonparametric empirical Bayesian-based approach (NPEBseq) to model the RNA-seq data. The prior distribution of the Bayesian model is empirically estimated from the data without any parametric assumption, and hence the method is "nonparametric" in nature. Based on this model, we proposed a method for detecting differentially expressed genes across different conditions. We also extended this method to

SUBMITTER: Bi Y 

PROVIDER: S-EPMC3765716 | biostudies-literature | 2013 Aug

REPOSITORIES: biostudies-literature