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An effective non-parametric method for globally clustering genes from expression profiles

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Abstract

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm.

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Acknowledgments

This research is partially supported by the Starting Grant of Faculty of Science and Technology, Deakin University, Australia. We thank Dr Yang Xiang for his contribution to some evaluations. The authors also thank the anonymous reviewers for their valuable comments.

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Authors and Affiliations

  1. School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

    Jingyu Hou, Gang Li & Wanlei Zhou

  2. The Walter and Eliza Hall Institute of Medical Research (WEHI), Parkville, VIC, 3050, Australia

    Wei Shi

Authors
  1. Jingyu Hou
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  2. Wei Shi
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  3. Gang Li
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  4. Wanlei Zhou
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Correspondence to Jingyu Hou.

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Hou, J., Shi, W., Li, G. et al. An effective non-parametric method for globally clustering genes from expression profiles. Med Bio Eng Comput 45, 1175–1185 (2007). https://doi.org/10.1007/s11517-007-0271-1

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  • DOI: https://doi.org/10.1007/s11517-007-0271-1

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