Clustering cancer gene expression data: a comparative study

Background The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There have been no studies thus far performing a largescale evaluation of different clustering methods in this context. Results/Conclusion We present the first large-scale analysis of seven different clustering methods and four proximity measures for the analysis of 35 cancer gene expression data sets. Our results reveal that the finite mixture of Gaussians, followed closely by k-means, exhibited the best performance in terms of recovering the true structure of the data sets. These methods also exhibited, on average, the smallest difference between the actual number of classes in the data sets and the best number of clusters as in...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Marcílio Carlos Pereira de Souto, Ivan G. Costa, Daniel S. A. de Araujo, Teresa Bernarda Ludermir, Alexander Schliep
• Keywords: BMCBI 2008, Clustering Methods, Data Sets, Gene Expression Data