Efficiently Mining Time-Delayed Gene Expression Patterns

Guoren Wang, Linjun Yin, Yuhai Zhao, Keming Mao

Abstract: para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> 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 <formula formulatype="inline"><tex Notation="TeX">$\times$</tex></formula> sample <formula formulatype="inline"><tex Notation="TeX">$\times$</tex> </formula> time data sets to identify 3-D td-clusters. </para>