Latent Process Decomposition of Next-Generation Sequencing Data

• Activity Number: 523
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Sanvesh Srivastava*+ and R. W. Doerge
• Companies: Purdue University and Purdue University
• Keywords: next-generation sequencing ; hierarchical Bayesian model ; variational inference ; Gap statistic ; high-dimensional count data ; latent variables

Abstract

We present a novel approach to probabilistically model high-dimensional count data in an unsupervised way using a three-level hierarchical Bayesian model. Its application is explored in the context of next-generation sequencing data for the purpose of identifying subsets of genes with consistent expression patterns, and that explain a large portion of variability. Each sample is modeled as a finite mixture of Poisson random variables over an underlying set of latent variables, that are assumed to correspond to biological functions. Each biological function is further modeled as an infinite mixture over an underlying set of biological function probabilities. We call this model Latent Process Decomposition (LPD) due to its biological motivation. LPD combines ideas from machine learning and resampling-based methods, and uses a computationally efficient variational method for parameter estimation. LPD is implemented as an R/Bioconductor package called themes.