INTEGRATING MICROARRAY DATA BY CONSENSUS CLUSTERING

作     者：VLADIMIR FILKOV STEVEN SKIENA 

作者机构：Computer Science Dept. University of California One Shields Ave. Davis CA 95616 USA Computer Science Dept. SUNY Stony Brook Stony Brook NY 11794 USA 

出 版 物：《International Journal on Artificial Intelligence Tools》 (国际人工智能工具杂志)

年 卷 期：2004年第13卷第4期

页      面：863-880页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Microarray data integration consensus clustering median partition heuristics 

摘      要：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 heterogeneous data 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.