Merge method for shape-based clustering in time series microarray analysis

Authors:
Irene Barbero;Camelia Chira;Javier Sedano;Carlos Prieto;José R. Villar;Emilio Corchado
Affiliations:
Instituto Tecnológico de Castilla y León, Burgos, Spain;Instituto Tecnológico de Castilla y León, Burgos, Spain,Department of Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania;Instituto Tecnológico de Castilla y León, Burgos, Spain,Department of Electromechanical Engineering, University of Burgos, Spain;Instituto de Biotecnología de León, Spain;University of Oviedo, Gijón, Spain;University of Salamanca, Spain
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

A challenging task in time-course microarray data analysis is to combine the information provided by multiple time series in order to cluster genes meaningfully. This paper proposes a novel merge method to accomplish this goal obtaining clusters with highly correlated genes. The main idea of the proposed method is to generate a clustering, starting from clusterings created from different time series individually, that takes into account the number of times each clustering assemble two genes into the same group. Computational experiments are performed for real-world time series microarray with the purpose of finding co-expressed genes related to the production and growth of a certain bacteria. The results obtained by the introduced merge method are compared with clusterings generated by time series individually and averaged as well as interpreted biologically.