Efficiently mining time-delayed gene expression patterns

Authors:
Guoren Wang;Linjun Yin;Yuhai Zhao;Keming Mao
Affiliations:
School of Information Science and Engineering, and Key Laboratory of Medical Image Computing, Northeastern University, Shenyang, China;Department of Business Intelligence, Yahoo! China, Hangzhou, China and School of Information Science and Engineering, Northeastern University, Shenyang, China;School of Information Science and Engineering, and Key Laboratory of Medical Image Computing, Northeastern University, Shenyang, China;School of Information Science and Engineering, and Key Laboratory of Medical Image Computing, Northeastern University, Shenyang, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

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Abstract

Unlike pattern-based biclustering methods that focus on grouping objects in the same subset of dimensions, in this paper, we propose a novel model of coherent clustering for time-series gene expression data, i.e., time-delayed cluster (td-cluster). Under this model, objects can be coherent in different subsets of dimensions if these objects follow a certain time-delayed relationship. Such a cluster can discover the cycle time of gene expression, which is essential in revealing gene regulatory networks. This paper is the first attempt to mine time-delayed gene expression patterns from microarray data. A novel algorithm is also presented and implemented to mine all significant td-clusters. Our experimental results show following two results: 1) the td-cluster algorithm can detect a significant amount of clusters that were missed by previous models, and these clusters are potentially of high biological significance and 2) the td-cluster model and algorithm can easily be extended to 3-D gene × sample × time data sets to identify 3-D td-clusters.