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A new clustering method of gene expression data based on multivariate Gaussian mixture models

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Abstract

Clustering gene expression data are an important problem in bioinformatics because understanding which genes behave similarly can lead to the discovery of important biological information. Many clustering methods have been used in the field of gene clustering. This paper proposed a new method for gene expression data clustering based on an improved expectation maximization(EM) method of multivariate Gaussian mixture models. To solve the problem of over-reliance on the initialization, we propose a remove and add initialization for the classical EM, and make a random perturbation on the solution before continuing EM iterations. The number of clusters is estimated with the Quasi Akaike’s information criterion in this paper. The improved EM method is tested and compared with some other clustering methods; the performance of our clustering algorithm has been extensively compared over several simulated and real gene expression data sets. Our results indicated that improved EM clustering method is superior than other clustering algorithms and can be widely used for gene clustering.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61402204), the Jiangsu Province Natural Science Foundation (Nos. BK20130529, BK2012209), the Research Fund for the Doctoral Program of Higher Education of China (No. 20113227110010), the research foundation for talented scholars, Jiangsu University (No. 14JDG141), and the science and technology program of Zhenjiang city (SH20140110).

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Authors and Affiliations

  1. School of Computer Science and Telecommunication, Jiangsu University, Room 522, Zhenjiang, Jiangsu Province, People’s Republic of China

    Zhe Liu, Yu-qing Song & Zheng Tang

  2. School of Computer Science, Jilin Nomal University, Sipin, Jilin Province, People’s Republic of China

    Zhe Liu

  3. School of Computer Science and Engineering, Changshu Institute of Technology, Suzhou, Jiangsu Province, People’s Republic of China

    Cong-hua Xie

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  1. Zhe Liu
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  2. Yu-qing Song
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  4. Zheng Tang
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Correspondence to Zhe Liu.

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Liu, Z., Song, Yq., Xie, Ch. et al. A new clustering method of gene expression data based on multivariate Gaussian mixture models. SIViP 10, 359–368 (2016). https://doi.org/10.1007/s11760-015-0749-5

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  • DOI: https://doi.org/10.1007/s11760-015-0749-5

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