©1996-2019. All Rights Reserved. Online Journal of Bioinformatics . You may not store these pages in any form except for your own personal use. All other usage or distribution is illegal under international copyright treaties. Permission to use any of these pages in any other way besides the before mentioned must be gained in writing from the publisher. This article is exclusively copyrighted in its entirety to OJB publications. This article may be copied once but may not be, reproduced or re-transmitted without the express permission of the editors. This journal satisfies the refereeing requirements (DEST) for the Higher Education Research Data Collection (Australia). Linking:To link to this page or any pages linking to this page you must link directly to this page only here rather than put up your own page.
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
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