Multiple gene expression profile alignment for microarray time-series data clustering

Abstract

Motivation: Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints. Traditional clustering methods based on conventional similarity measures are not always suitable for clustering time-series data. A few methods have been proposed recently for clustering microarray time-series, which take the temporal dimension of the data into account. The inherent principle behind these methods is to either dene a similaritymeasure appropriate for temporal expression data, or pre-process the data in such a way that the temporal relationships between and within the time-series are considered during the subsequent clustering phase. Results: We introduce pairwise gene expression prole alignment, which vertically shifts two proles in such a way that the area between their corresponding curves is minimal. Based on the pairwise alignment operation, we dene a new distance function that is appropriate for time-series proles. We also introduce a new clustering method that involves multiple expression prole alignment, which generalizes pairwise alignment to a set of proles. Extensive experiments on well-known datasets yield encouraging results of at least 80% classication accuracy. Show more