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Microarray Data Analysis Using Fuzzy Clustering Algorithms

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Fuzzy Systems in Bioinformatics and Computational Biology

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 242))

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Summary

Microarray technology is used for studying gene regulation at the genome and transcriptome level. In the most common application, the expression level of thousands of genes is monitored simultaneously leading to a huge dataset having high dimensionality. It is assumed that genes with similar function or regulatory elements will display a common expression profile over a variety of biological conditions. For some cases, it may be desirable to study simultaneously many drugs in different experimental conditions (e.g. concentration or time point) on biological models, leading to the generation of 3-way data. Cluster analysis is used for identifying biologically relevant groups of genes. In this chapter, fuzzy cluster analysis is used for this purpose. After a brief formulation of the problem, we outline motivations for our choice of the clustering algorithm. Then, the fuzzy clustering algorithms are presented and the main tuning parameters are discussed in the context of 2-way and 3-way microarray data. We propose a transformation allowing more contrast in distances between all pairs of samples in a dataset. This increases the likelihood of detection of a group structure, if any, in a high dimensional dataset. Results showing the performance of the fuzzy C-Means algorithm are carried out using real datasets. These results are finally validated through functional enrichment of genes.

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Author information

Authors and Affiliations

  1. IGBMC, CNRS-INSERM-ULP, 1 rue Laurent Fries, BP 10142, 67404, Illkirch Cedex, France

    Doulaye Dembélé

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  1. Doulaye Dembélé
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Editor information

Editors and Affiliations

  1. Honda Research Institute Honda Research Institute Europe , Offenbach, Germany

    Yaochu Jin

  2. Nanyang Technological University, Singapore

    Lipo Wang

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Dembélé, D. (2009). Microarray Data Analysis Using Fuzzy Clustering Algorithms. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_5

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  • DOI: https://doi.org/10.1007/978-3-540-89968-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89967-9

  • Online ISBN: 978-3-540-89968-6

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