Savage, RS and Ghahramani, Z and Griffin, JE and de la Cruz, BJ and Wild, DL (2010) Discovering transcriptional modules by Bayesian data integration. Bioinformatics, 26. i158-i167.Full text not available from this repository.
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.
|Uncontrolled Keywords:||Bayes Theorem Binding Sites Gene Expression Profiling Multigene Family Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Proteins Transcription Factors|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||09 Dec 2016 17:12|
|Last Modified:||30 Mar 2017 04:58|