A Changing Window Approach to Exploring Gene Expression Patterns

This paper presents a changing window approach to exploring gene expression patterns in "snapshot windows". A snapshot window is a sub-matrix of co-expressed microarray data representing certain expression pattern. In this approach, we use a feature weighting k-means subspace clustering algorithm to generate a set of clusters and each cluster defines a set of "snapshot windows" which are characterized by different sets of ordered sample weights that were assigned by the clustering algorithm. We define an accumulated weighting threshold (AWT) as the sum of weights of samples in the "snapshot window". Given a cluster, different "snapshot windows" can be obtained by changing AWT to explore all possible local expression patterns in the cluster. Experiment results have shown our approach is effective and flexible in exploring various expression patterns and identifying novel ones.