Discovering Transcriptional Modules from Bayesian Data Fusion

Savage, R.S. and Ghahramani, Z. and Griffin, J.E. and de la Cruz, B.J. and Wild, D.L. (2010) Discovering Transcriptional Modules from Bayesian Data Fusion. Bioinformatics, 26 (12). pp. 1158-1167. ISSN 1367-4803. (The full text of this publication is not available from this repository)

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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 geneby- 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: Article
Projects: [132] Managing the data explosion in post-genomic biology with fast Bayesian computational methods
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jim Griffin
Date Deposited: 29 Jun 2011 14:35
Last Modified: 22 Nov 2011 16:27
Resource URI: http://kar.kent.ac.uk/id/eprint/24865 (The current URI for this page, for reference purposes)
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