AI Article Synopsis
- The study presents a new method to identify transcriptional modules (TMs) by integrating gene expression and transcription factor binding data using a hierarchical Dirichlet process mixture model.
- By analyzing data on a gene-by-gene basis, the model effectively separates co-expressed genes from co-regulated ones, enabling more accurate identification of gene clusters that share similar transcriptional structures.
- The results indicate that this approach yields clusters with greater functional coherence compared to existing methods, simplifying the identification of significant groups of genes.
Article 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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2881394 | PMC |
| http://dx.doi.org/10.1093/bioinformatics/btq210 | DOI Listing |
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