Abstract
Meaningfully integrating massive multi-experimental genomic data sets is becoming critical for the understanding of gene function. We have recently proposed methodologies for integrating large numbers of microarray data sets based on consensus clustering. Our methods combine gene clusters into a unified representation, or a consensus, that is insensitive to mis-classifications in the individual experiments. Here we extend their utility to heterogeneous data sets and focus on their refinement and improvement. First of all we compare our best heuristic to the popular majority rule consensus clustering heuristic, and show that the former yields tighter consensuses. We propose a refinement to our consensus algorithm by clustering of the source-specific clusterings as a step before finding the consensus between them, thereby improving our original results and increasing their biological relevance. We demonstrate our methodology on three data sets of yeast with biologically interesting results. Finally, we show that our methodology can deal successfully with missing experimental values.
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Filkov, V., Skiena, S. (2004). Heterogeneous Data Integration with the Consensus Clustering Formalism. In: Rahm, E. (eds) Data Integration in the Life Sciences. DILS 2004. Lecture Notes in Computer Science(), vol 2994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24745-6_8
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DOI: https://doi.org/10.1007/978-3-540-24745-6_8
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