MCMC implementation of the optimal Bayesian classifier for non-Gaussian models: model-based RNA-Seq classification.

ABSTRACT:

Background

Sequencing datasets consist of a finite number of reads which map to specific regions of a reference genome. Most effort in modeling these datasets focuses on the detection of univariate differentially expressed genes. However, for classification, we must consider multiple genes and their interactions.

Results

Thus, we introduce a hierarchical multivariate Poisson model (MP) and the associated optimal Bayesian classifier (OBC) for classifying samples using sequencing data. Lacking closed-form solutions, we employ a Monte Carlo Markov Chain (MCMC) approach to perform classification. We demonstrate superior or equivalent classification performance compared to typical classifiers for two synthetic datasets and over a range of classification problem difficulties. We a

SUBMITTER: Knight JM 

PROVIDER: S-EPMC4265360 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature