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Fuzzy soft subspace clustering method for gene co-expression network analysis

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Abstract

Gene expression clustering methods for building gene co-expression networks suffer greatly from the biological complexity of cells. This paper proposes a fuzzy soft subspace clustering method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific cluster subspaces and interactions among different gene clusters can be extracted by this method, providing useful information for gene co-expression networks analysis. Experiments on the yeast cell cycle benchmark microarray data have shown that this method is effective in extracting underlying biological relationships between genes, and enhancing gene co-expression network inference.

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Acknowledgments

This work is supported by China Postdoctoral Science Foundation (2015M572361) and National Natural Science Foundations of China (61503252 and 61170040).

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Authors and Affiliations

  1. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Qiang Wang

  2. HPC Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Guoliang Chen

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  1. Qiang Wang
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  2. Guoliang Chen
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Correspondence to Qiang Wang.

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Wang, Q., Chen, G. Fuzzy soft subspace clustering method for gene co-expression network analysis. Int. J. Mach. Learn. & Cyber. 8, 1157–1165 (2017). https://doi.org/10.1007/s13042-015-0486-7

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  • DOI: https://doi.org/10.1007/s13042-015-0486-7

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