Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15942
Title: Molecular pattern discovery based on penalized matrix decomposition
Authors: Zheng, CH
Zhang, L 
Ng, VTY 
Shiu, SCK 
Huang, DS
Keywords: Developmental biology
gene expression data
metasample
penalized matrix decomposition
Tumor clustering
Issue Date: 2011
Publisher: ACM Special Interest Group
Source: IEEE/ACM transactions on computational biology and bioinformatics, 2011, v. 8, no. 6, 5765932, p. 1592-1603 How to cite?
Journal: IEEE/ACM transactions on computational biology and bioinformatics 
Abstract: 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.
URI: http://hdl.handle.net/10397/15942
ISSN: 1545-5963
EISSN: 1557-9964
DOI: 10.1109/TCBB.2011.79
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