Integrating Microarray Data by Consensus Clustering

Authors:
Vladimir Filkov;Steven Skiena
Affiliations:
-;-
Venue:
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Year:
2003

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Abstract

With the exploding volume of microarray experiments comes increasing interest in mining repositories of such data. Meaningfully combining results from varied experiments on an equal basis is a challenging task. Here we propose a general method for integrating heterogeneousdata sets based on the consensus clustering formalism. Our method analyzes source-specific clusterings and identifies a consensus set-partition which is as close as possible to all of them. We develop a general criterion to assess the potential benefit of integrating multiple heterogeneous data sets, i.e. whether the integrated data is more informative than the individual data sets. We apply our methods on two popular sets of microarray data yielding gene classifications of potentially greater interest than could be derived from the analysis of each individual data set.