A Bayesian nonparametric mixture model for selecting genes and gene subnetworks

TL;DR

This paper introduces a Bayesian nonparametric mixture model that effectively selects genes and gene subnetworks from high-throughput data, incorporating network information and capturing complex expression behaviors.

Contribution

It presents a novel nonparametric Bayesian approach for gene selection that accounts for network structure and expression patterns, with efficient algorithms for posterior inference.

Findings

Accurately identifies genes associated with clinical outcomes.

Effectively captures genes with specific expression behaviors.

Demonstrates superior performance on simulated and real data.

Abstract

It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.