Tumor clustering using nonnegative matrix factorization with gene selection

Chun Hou Zheng, De Shuang Huang, Lei Zhang, Xiang Zhen Kong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

162 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)599-607
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number4
Publication statusPublished - 3 Aug 2009


  • Clustering
  • Gene expression data
  • Independent component analysis (ICA)
  • Nonnegative matrix factorization (NMF)
  • Tumor

ASJC Scopus subject areas

  • Biotechnology
  • Medicine(all)
  • Computer Science Applications
  • Electrical and Electronic Engineering

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