New approaches to clustering microarray time-series data using multiple expression profile alignment

An important process in functional genomic studies is clustering microarray time-series data, where genes with similar expression profiles are expected to be functionally related. Clustering microarray time-series data via pairwise alignment of piecewise linear profiles has been recently introduced. In this paper, we propose a clustering approach based on a multiple profile alignment of natural cubic spline and piecewise linear representations of gene expression profiles. We combine these multiple alignment approaches with k-means. We ran our methods on a well-known data set of pre-clustered Saccharomyces cerevisiae gene expression profiles and a data set of 3315 Pseudomonas aeruginosa expression profiles. We assessed the validity of the resulting clusters and applied a c-nearest neighbor classifier for evaluating the performance of our approaches, obtaining accuracies of 89.51% and 86.12% respectively, on Saccharomyces cerevisiae data, and 90.90% and 93.71% accuracies for cubic spline and piecewise linear respectively on Pseudomonas aeruginosa data.