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Issue No. 06 - November/December (2011 vol. 8)
ISSN: 1545-5963
pp: 1592-1603
Simon Chi-Keung Shiu , The Hong Kong Polytechnic University, Hong Kong
Vincent To-Yee Ng , The Hong Kong Polytechnic University, Hong Kong
Lei Zhang , The Hong Kong Polytechnic University, Hong Kong
Chun-Hou Zheng , The Hong Kong Polytechnic University, Hong Kong and Qufu Normal University, Rizhao, Shandong
De-Shuang Huang , Tongji University, Shanghai
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.
INDEX TERMS
Tumor clustering, penalized matrix decomposition, metasample, gene expression data, developmental biology.
CITATION
Simon Chi-Keung Shiu, Vincent To-Yee Ng, Lei Zhang, Chun-Hou Zheng, De-Shuang Huang, "Molecular Pattern Discovery Based on Penalized Matrix Decomposition", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1592-1603, November/December 2011, doi:10.1109/TCBB.2011.79
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