Statistical approach to assign functional regulators in gene expression compendia: Online Journal of Bioinformatics.

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Online Journal of Bioinformatics ©

Volume 11 (1): 145-160, 2010.

Statistical approach to assign functional regulators in gene expression compendia.

Forcheh Chiara Anyiawung¹, Geert Verbeke¹^;² Dirk Valkenborg¹^;²^;⁴, Hui Zhao³,

Kathleen Marchal³, and Kristof Engelen³

¹Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, Kapucijnenvoer 35, Blok D, bus 7001, B3000 Leuven, ²Interuniversity Institute for Biostatistics and statistical Bioinformatics,Universiteit Hasselt, Agoralaan 1, B3590 Diepenbeek, ³Department of Microbial and Molecular Systems, Katholieke Universiteit Leuven, Leuven, ⁴Flemish Institute for Technological Research (VITO), Boeretang 200, B2400, Mol, Belgium

ABSTRACT

Anyiawung FC, Verbeke G Valkenborg D, Zhao H, Marchal K, Engelen K, Statistical approach to assign functional regulators in gene expression compendia, Onl J Bioinform, 11 (1): 145-160, 2010. Current developments in high-throughput techniques for life-sciences, such as microarray technology, have resulted in a rapid accumulation of expression datasets for many types of organisms. Obtaining the rich biological information from such data sets is challenging. In order to address this challenge, we present a novel approach for assessing regulators responsible for the differential gene expression observed between two conditions (functional relationships) based on gene expression datasets from publicly available data sources. The method is embedded in a sound statistical framework, which integrates infinite mixture modeling with regression analysis. The application of this statistical tool is demonstrated in the study of gene regulation in Escherichia coli data, leading to the detection of biologically relevant associations between environmental conditions and regulators.

Keywords: Functional genomics, Finite mixture model, Regression analysis, Gene expression data

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