Clustering cancer gene expression data: a comparative study

By M. D. Souto, Ivan G. Costa, D. A. D. Araujo, Teresa Bernarda Ludermir, Alexander Schliep. BMC Bioinformatics, 2008

447 citations recorded by the source corpus.

BackgroundThe 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 large-scale evaluation of different clustering methods in this context.Results/ConclusionWe 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 indicated by our validation criteria. Furthermore, hierarchical methods, which have been widely used by the medical community, exhibited a poorer recovery performance than that of the other methods evaluated. Moreover, as a stable basis for the assessment and comparison of different clustering methods for cancer gene expression data, this study provides a common group of data sets (benchmark data sets) to be shared among researchers and used for comparisons with new methods. The data sets analyzed in this study are available at http://algorithmics.molgen.mpg.de/Supplements/CompCancer/.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/95f1d69e4e7b7065cbc4dcb28b775b1258260749

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Citations in the corpus

Clustering algorithms: A comparative approach (2016, methodology): “A comparative analysis using real world dataset is presented in several works [20, 21, 24, 25, 43, 44].”
Robust continuous clustering (2017, methodology): “We conducted an additional comprehensive evaluation on a large-scale benchmark that consists of more than 30 cancer gene expression datasets, collected for the purpose of evaluating clustering algorithms (53).”