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Discovering transcriptional modules by Bayesian data integration

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Savage, Richard S., Ghahramani, Zoubin, Griffin, Jim E., De la Cruz, Bernard J. and Wild, David L.. (2010) Discovering transcriptional modules by Bayesian data integration. Bioinformatics, Vol.26 (No.12). pp. 158-167. ISSN 1367-4803

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Official URL: http://dx.doi.org/10.1093/bioinformatics/btq210

Abstract

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history
Divisions:	Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH):	Genes -- Research, Gene expression, Transcription factors, Dirichlet principle
Journal or Publication Title:	Bioinformatics
Publisher:	Oxford University Press
ISSN:	1367-4803
Date:	15 June 2010
Volume:	Vol.26
Number:	No.12
Page Range:	pp. 158-167
Identification Number:	10.1093/bioinformatics/btq210
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC)
Grant number:	EP/F027400/1 (EPSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/3277