Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 12, Issue 4, Pages 887-901Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2014.2359433
Keywords
Tumor clustering; gene expression profiles; cancer discovery; fuzzy model; cluster ensemble; consensus clustering; optimization; microarray; feature selection
Categories
Funding
- Hong Kong Scholars Program [XJ2012015]
- National Natural Science Foundation of China (NSFC) [61273363, 61379033, 61472145]
- NSFC-Guangdong Joint Fund [U1035004]
- New Century Excellent Talents in University [NCET-11-0165]
- Guangdong Natural Science Funds for Distinguished Young Scholar [S2013050014677]
- Science and Technology Planning Project of Guangzhou [11A11080267]
- China Postdoctoral Science Foundation [2013M540655]
- Foundation of Guangdong Educational Committee [2012KJCX0011]
- Fundamental Research Funds for the Central Universities [2014G0007]
- Key Enterprises and Innovation Organizations in Nanshan District in Shenzhen [KC2013ZDZJ0007A]
- Natural Science Foundation of Guangdong Province, China [S2012010009961]
- Doctoral Program of Higher Education [20110172120027]
- Cooperation Project in Industry, Education and Academy of Guangdong Province and Ministry of Education of China [2011B090400032]
- key lab of cloud computing and big data in Guangzhou [SITGZ[2013] 268-6]
- Hong Kong Baptist University [RGC/HKBU211212]
- City University of Hong Kong [7004047]
- Hong Kong Polytechnic University [G-YK53, G-YK77]
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Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.
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