Parallel Integration of Heterogeneous Genome-Wide Data Sources

Heterogeneous genome-wide data sources capture information on various aspects of complex biological systems. For instance, transcriptome, interactome and phenome-level information may be derived from mRNA expression data, protein-protein interaction networks, and biomedical literature corpora. Each source provides a distinct "view" of the same domain, but potentially encodes different biologically-relevant patterns. Effective integration of such views can provide a richer, more informative model of an organism's functional modules than that produced on a single view alone. Existing machine learning strategies for information fusion largely focus on the production of a consensus model that reflects patterns shared between views. However, the information provided by different views may not always be easily reconciled, due to the incomplete nature of the data, or the fact that some patterns will be present in one view but not in another. To address this problem, we present the Parallel Integration Clustering Algorithm (PICA), a novel cluster analysis approach which supports the simultaneous integration of information from two or more sources. The resulting model preserves patterns that are unique to individual views, as well as those common to all views. We demonstrate the effectiveness of PICA in identifying significant patterns corresponding to functional groupings, when applied to three genome-wide datasets.