Gene selection in time-series gene expression data

Authors:
Prem Raj Adhikari;Bimal Babu Upadhyaya;Chen Meng;Jaakko Hollmén
Affiliations:
Aalto University School of Science, Department of Information and Computer Science, Espoo, Finland;Aalto University School of Science, Department of Information and Computer Science, Espoo, Finland;Royal Institute of Technology, School of Computer Science and Communication, Department of Computational Biology, Stockholm, Sweden;Aalto University School of Science, Department of Information and Computer Science, Espoo, Finland
Venue:
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Year:
2011

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Abstract

The dimensionality of biological data is often very high. Feature selection can be used to tackle the problem of high dimensionality. However, majority of the work in feature selection consists of supervised feature selection methods which require class labels. The problem further escalates when the data is time-series gene expression measurements that measure the effect of external stimuli on biological system. In this paper we propose an unsupervised method for gene selection from time-series gene expression data founded on statistical significance testing and swap randomization. We perform experiments with a publicly available mouse gene expression dataset and also a human gene expression dataset describing the exposure to asbestos. The results in both datasets show a considerable decrease in number of genes.