This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
SC³: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles
Nov.-Dec. 2012 (vol. 9 no. 6)
pp. 1751-1765
Zhiwen Yu, Higher Educ. Megacenter, South China Univ. of Technol., Guangzhou, China
Le Li, Higher Educ. Megacenter, South China Univ. of Technol., Guangzhou, China
J. You, Dept. of Comput., Hong Kong Polytech. Univ., Kowloon, China
Hau-San Wong, Dept. of Comput. Sci., City Univ. of Hong Kong, Kowloon, China
Guoqiang Han, Higher Educ. Megacenter, South China Univ. of Technol., Guangzhou, China
In order to perform successful diagnosis and treatment of cancer, discovering, and classifying cancer types correctly is essential. One of the challenging properties of class discovery from cancer data sets is that cancer gene expression profiles not only include a large number of genes, but also contains a lot of noisy genes. In order to reduce the effect of noisy genes in cancer gene expression profiles, we propose two new consensus clustering frameworks, named as triple spectral clustering-based consensus clustering (SC3) and double spectral clustering-based consensus clustering (SC2 Ncut) in this paper, for cancer discovery from gene expression profiles. SC3 integrates the spectral clustering (SC) algorithm multiple times into the ensemble framework to process gene expression profiles. Specifically, spectral clustering is applied to perform clustering on the gene dimension and the cancer sample dimension, and also used as the consensus function to partition the consensus matrix constructed from multiple clustering solutions. Compared with SC3, SC2 Ncut adopts the normalized cut algorithm, instead of spectral clustering, as the consensus function. Experiments on both synthetic data sets and real cancer gene expression profiles illustrate that the proposed approaches not only achieve good performance on gene expression profiles, but also outperforms most of the existing approaches in the process of class discovery from these profiles.
Index Terms:
pattern classification,cancer,gene therapy,genetic algorithms,genomics,medical diagnostic computing,patient diagnosis,normalized cut algorithm,SC<sup>3</sup>,triple spectral clustering-based consensus clustering framework,class discovery,cancer gene expression profiles,cancer diagnosis,cancer treatment,cancer type classification,cancer data sets,noisy genes,double spectral clustering-based consensus clustering,spectral clustering algorithm multiple times,gene dimension,cancer sample dimension,consensus matrix construction,multiple clustering solutions,Cancer,Gene expression,Clustering algorithms,Partitioning algorithms,Bioinformatics,Noise measurement,cancer gene expression profiles,Cluster ensemble,spectral clustering
Citation:
Zhiwen Yu, Le Li, J. You, Hau-San Wong, Guoqiang Han, "SC³: Triple Spectral Clustering-Based Consensus Clustering Framework for Class Discovery from Cancer Gene Expression Profiles," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 6, pp. 1751-1765, Nov.-Dec. 2012, doi:10.1109/TCBB.2012.108
Usage of this product signifies your acceptance of the Terms of Use.