Tumor clustering using nonnegative matrix factorization with gene selection

  • Authors:

  • Chun-Hou Zheng;De-Shuang Huang;Lei Zhang;Xiang-Zhen Kong

  • Affiliations:

  • College of Information and Communication Technology, Qufu Normal University, Rizhao and Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Chi ...;Intelligent Computing Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Anhui, China;Biometric Research Center, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, China

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
  • Year:
  • 2009

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Abstract

Tumor clustering is becoming a powerful method in cancer class discovery. Nonnegative matrix factorization (NMF) has shown advantages over other conventional clustering techniques. Nonetheless, there is still considerable room for improving the performance of NMF. To this end, in this paper, gene selection and explicitly enforcing sparseness are introduced into the factorization process. Particularly, independent component analysis is employed to select a subset of genes so that the effect of irrelevant or noisy genes can be reduced. The NMF and its extensions, sparse NMF and NMF with sparseness constraint, are then used for tumor clustering on the selected genes. A series of elaborate experiments are performed by varying the number of clusters and the number of selected genes to evaluate the cooperation between different gene selection settings and NMF-based clustering. Finally, the experiments on three representative gene expression datasets demonstrated that the proposed scheme can achieve better clustering results.