Bioinformatics Advance Access originally published online on May 31, 2007
Bioinformatics 2007 23(15):1936-1944; doi:10.1093/bioinformatics/btm280
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bayesian modelling of shared gene function
1Department of Biotechnology, BOKU University, Vienna, Austria, 2School of Biosciences, Cardiff University, UK, 3Department of Molecular Medicine & Pathology, University of Auckland, New Zealand 4Department of Pathology, 5Department of Genetics and Cambridge Computational Biology Institute and 6Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: Biological assays are often carried out on tissues that contain many cell lineages and active pathways. Microarray data produced using such material therefore reflect superimpositions of biological processes. Analysing such data for shared gene function by means of well-matched assays may help to provide a better focus on specific cell types and processes. The identification of genes that behave similarly in different biological systems also has the potential to reveal new insights into preserved biological mechanisms.
Results: In this article, we propose a hierarchical Bayesian model allowing integrated analysis of several microarray data sets for shared gene function. Each gene is associated with an indicator variable that selects whether binary class labels are predicted from expression values or by a classifier which is common to all genes. Each indicator selects the component models for all involved data sets simultaneously. A quantitative measure of shared gene function is obtained by inferring a probability measure over these indicators.
Through experiments on synthetic data, we illustrate potential advantages of this Bayesian approach over a standard method. A shared analysis of matched microarray experiments covering (a) a cycle of mouse mammary gland development and (b) the process of in vitro endothelial cell apoptosis is proposed as a biological gold standard. Several useful sanity checks are introduced during data analysis, and we confirm the prior biological belief that shared apoptosis events occur in both systems. We conclude that a Bayesian analysis for shared gene function has the potential to reveal new biological insights, unobtainable by other means.
Availability: An online supplement and MatLab code are available at http://www.sykacek.net/research.html#mcabf
Contact: peter{at}sykacek.net
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Joaquin Dopazo
Received on September 21, 2006; revised on May 8, 2007; accepted on May 18, 2007
CiteULike 
Connotea 
Del.icio.us What's this?