Discovering transcriptional modules by Bayesian data integration.
BACKGROUND
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.


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Savage
Richard S
RS
Systems Biology Centre, University of Warwick, Coventry, CV4 7AL, UK.
Ghahramani
Zoubin
Z
Griffin
Jim E
JE
de la Cruz
Bernard J
BJ
Wild
David L
DL
D001499:Bayes Theorem; D001665:Binding Sites; D020869:Gene Expression Profiling / Q000379:methods*; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism*
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors