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

Authors:
Numanul Subhani;Alioune Ngom;Luis Rueda;Conrad Burden
Affiliations:
School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;School of Computer Science, 5115 Lambton Tower, University of Windsor, Windsor, Canada N9B 3P4;Centre for Bioinformation Science, Mathematical Sciences Institute and John Curtin School of Medical Research, The Australian National University, Canberra, Australia 0200
Venue:
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Year:
2009

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Abstract

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 time-interval, defined on 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.