title: A Bayesian Framework for the Classification of Microbial Gene Activity States.
abstract: Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive genes, failing to leverage a priori knowledge of gene co-regulation, failing to account for differences between genes, and failing to provide statistically meaningful confidence estimates. We propose a flexible Bayesian approach to classifying gene activity states based on a Gaussian mixture model. The model integrates genome-wide transcriptomics data from multiple conditions and information about gene co-regulation to provide activity state confidence estimates for each gene in each condition. We compare the performance of our novel method to existing methods on both simulated data and real data from 907 E. coli gene expression arrays, as well as a comparison with experimentally measured flux values in 29 conditions, demonstrating that our method provides more consistent and accurate results than existing methods across a variety of metrics.
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA.
authors.forename: Craig
authors.initials: C
authors.lastname: Disselkoen
authors.affiliation: Department of Biostatistics, School of Public Health, University of MichiganAnn Arbor, MI, USA; Department of Statistics, University of TexasAustin, TX, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Greco
authors.affiliation: Department of Biostatistics, Harvard University Boston, MA, USA.
authors.forename: Kaitlyn
authors.initials: K
authors.lastname: Cook
authors.affiliation: Department of Statistics, Baylor University Waco, TX, USA.
authors.forename: Kristin
authors.initials: K
authors.lastname: Koch
authors.affiliation: Department of Biostatistics, Harvard University Boston, MA, USA.
authors.forename: Reginald
authors.initials: R
authors.lastname: Lerebours
authors.affiliation: Department of Mathematics, University of Denver Denver, CO, USA.
authors.forename: Chase
authors.initials: C
authors.lastname: Viss
authors.affiliation: Department of Applied Mathematics and Statistics, Johns Hopkins University Baltimore, MD, USA.
authors.forename: Joshua
authors.initials: J
authors.lastname: Cape
authors.affiliation: Department of Biostatistics, University of Iowa Iowa City, IA, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Held
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA.
authors.forename: Yonatan
authors.initials: Y
authors.lastname: Ashenafi
authors.affiliation: Department of Statistics, Texas A&M University College Station, TX, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Fischer
authors.affiliation: Department of Computer Science, Hope College Holland, MI, USA.
authors.forename: Allyson
authors.initials: A
authors.lastname: Acosta
authors.affiliation: Department of Biology, Hope College Holland, MI, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Cunningham
authors.affiliation: Department of Biology, Hope College Holland, MI, USA.
authors.forename: Aaron A
authors.initials: AA
authors.lastname: Best
authors.affiliation: Department of Computer Science, Hope College Holland, MI, USA.
authors.forename: Matthew
authors.initials: M
authors.lastname: DeJongh
authors.affiliation: Department of Mathematics, Statistics and Computer Science, Dordt College Sioux Center, IA, USA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Tintle
keywords: Bayesian model; bacteria; gene activity; gene expression; metabolic modeling