Discovering Transcriptional Modules by Bayesian Data Integration

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2010 Jun 10

PMID 20529901

Citations 38

Authors

Richard S Savage

Zoubin Ghahramani

Jim E Griffin

Bernard J de la Cruz

David L Wild

Affiliations

Soon will be listed here.

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.

Availability: If interested in the code for the work presented in this article, please contact the authors.

Supplementary Information: Supplementary data are available at Bioinformatics online.

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