A novel gene-centric clustering algorithm for standardization of time series expression data

This paper proposes a novel data transformation method aiming at multi-purpose data standardization and inspired by gene-centric clustering approaches. The idea is to perform data standardization via template matching of each expression profile with the rest of the expression profiles employing dynamic time warping (DTW) alignment algorithm to measure the similarity between the expression profiles. This algorithm facilitates the identification of a cluster of genes whose expression profiles are related, possibly with a nonlinear time shift, to the profile of the gene supplied as a template. Consequently, for each gene profile a varying number (based on the degree of similarity) of neighboring gene profiles is identified to be used in the subsequent standardization phase. The latter uses a recursive aggregation algorithm in order to reduce the set of neighboring expression profiles into a singe profile representing the standardized version of the profile in question. The proposed data transformation method is evaluated and demonstrated on gene expression time series data coming from a study examining the global cell-cycle control of gene expression in fission yeast Schizosaccharomyces pombe.