title: 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 also introduce the Bayesian minimum mean squared error (MMSE) conditional error estimator and demonstrate its computation over the feature space. In addition, we demonstrate superior or leading class performance over an RNA-Seq dataset containing two lung cancer tumor types from The Cancer Genome Atlas (TCGA).
  
  
  CONCLUSIONS
  Through model-based, optimal Bayesian classification, we demonstrate superior classification performance for both synthetic and real RNA-Seq datasets. A tutorial video and Python source code is available under an open source license at http://bit.ly/1gimnss .
authors.affiliation: Department of Electrical Engineering in Texas A&M University, 3128 TAMU, College Station, 77843, TX, USA. jknight@tamu.edu.
authors.forename: Jason M
authors.initials: JM
authors.lastname: Knight
authors.affiliation: Department of Veterinary Physiology and Pharmacology in Texas A&M University, 3128 TAMU, College Station, 77843, TX, USA. iivanov@cvm.tamu.edu.
authors.forename: Ivan
authors.initials: I
authors.lastname: Ivanov
authors.affiliation: Department of Electrical Engineering in Texas A&M University, 3128 TAMU, College Station, 77843, TX, USA. edward@ece.tamu.edu.|Center for Bioinformatics and Genomics Systems Engineering, Texas A&M University, College Station, 77843, TX, USA. edward@ece.tamu.edu.
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics; D001499:Bayes Theorem; D002294:Carcinoma, Squamous Cell / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D012313:RNA / Q000235:genetics*
chemical_list: D012313:RNA