Microarray Time-Series Data Clustering via Multiple Alignment of Gene Expression Profiles

Genes with similar expression profiles are expected to be functionally related or co-regulated. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a k-means clustering approach based on a multiple alignment of natural cubic spline representations of gene expression profiles. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a tune-interval, defined oil a, set of profiles. Preliminary experiments on a well-known data set of 221 pre-clustered Saccharomyces cerevisiae gene expression profiles yields excellent results with 79.64% accuracy.