Molecular pattern discovery based on penalized matrix decomposition

Chun Hou Zheng, Lei Zhang, Vincent To Yee Ng, Chi Keung Simon Shiu, De Shuang Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

91 Citations (Scopus)


A reliable and precise identification of the type of tumors is crucial to the effective treatment of cancer. With the rapid development of microarray technologies, tumor clustering based on gene expression data is becoming a powerful approach to cancer class discovery. In this paper, we apply the penalized matrix decomposition (PMD) to gene expression data to extract metasamples for clustering. The extracted metasamples capture the inherent structures of samples belong to the same class. At the same time, the PMD factors of a sample over the metasamples can be used as its class indicator in return. Compared with the conventional methods such as hierarchical clustering (HC), self-organizing maps (SOM), affinity propagation (AP) and nonnegative matrix factorization (NMF), the proposed method can identify the samples with complex classes. Moreover, the factor of PMD can be used as an index to determine the cluster number. The proposed method provides a reasonable explanation of the inconsistent classifications made by the conventional methods. In addition, it is able to discover the modules in gene expression data of conterminous developmental stages. Experiments on two representative problems show that the proposed PMD-based method is very promising to discover biological phenotypes.
Original languageEnglish
Article number5765932
Pages (from-to)1592-1603
Number of pages12
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number6
Publication statusPublished - 22 Sept 2011


  • Developmental biology
  • gene expression data
  • metasample
  • penalized matrix decomposition
  • Tumor clustering

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics


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