Discovering transcriptional modules by Bayesian data integration.

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 facto

SUBMITTER: Savage RS 

PROVIDER: S-EPMC2881394 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature