This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology
Revealing functionally coherent subsets using a spectral clustering and an information integration approach
1 Department of Biochemistry and Molecular Biology, Medical University of South Carolina, Charleston, SC 29425, USA
2 Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA
3 Departments of Ophthalmology and Neurosciences, Medical University of South Carolina, Charleston, SC 29425, USA
4 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15232, USA
BMC Systems Biology 2012, 6(Suppl 3):S7 doi:10.1186/1752-0509-6-S3-S7Published: 17 December 2012
Contemporary high-throughput analyses often produce lengthy lists of genes or proteins. It is desirable to divide the genes into functionally coherent subsets for further investigation, by integrating heterogeneous information regarding the genes. Here we report a principled approach for managing and integrating multiple data sources within the framework of graph-spectrum analysis in order to identify coherent gene subsets.
We investigated several approaches to integrate information derived from different sources that reflect distinct aspects of gene functional relationships including: functional annotations of genes in the form of the Gene Ontology, co-mentioning of genes in the literature, and shared transcription factor binding sites among genes. Given a list of genes, we construct a graph containing the genes in each information space; then the graphs were kernel transformed so they could be integrated; finally functionally coherent subsets were identified using a spectral clustering algorithm. In a series of simulation experiments, known functionally coherent gene sets were mixed and recovered using our approach.
The results indicate that spectral clustering approaches are capable of recovering coherent gene modules even under noisy conditions, and that information integration serves to further enhance this capability. When applied to a real-world data set, our methods revealed biologically sensible modules, and highlighted the importance of information integration. The implementation of the statistical model is provided under the GNU general public license, as an installable Python module, at: http://code.google.com/p/spectralmix webcite.