Issue No. 03 - May/June (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.34
Banu Dost , University of California, San Diego, La Jolla
Chunlei Wu , The Genomics Institute of the Novartis Research Foundation, La Jolla
Andrew Su , The Genomics Institute of the Novartis Research Foundation, La Jolla
Vineet Bafna , University of California, San Diego, La Jolla
Genes with a common function are often hypothesized to have correlated expression levels in mRNA expression data, motivating the development of clustering algorithms for gene expression data sets. We observe that existing approaches do not scale well for large data sets, and indeed did not converge for the data set considered here. We present a novel clustering method TCLUST that exploits coconnectedness to efficiently cluster large, sparse expression data. We compare our approach with two existing clustering methods CAST and K-means which have been previously applied to clustering of gene-expression data with good performance results. Using a number of metrics, TCLUST is shown to be superior to or at least competitive with the other methods, while being much faster. We have applied this clustering algorithm to a genome-scale gene-expression data set and used gene set enrichment analysis to discover highly significant biological clusters. (Source code for TCLUST is downloadable at http://www.cse.ucsd.edu/~bdost/tclust.)
Microarray expression, clustering, graph algorithms, coconnectedness.
V. Bafna, C. Wu, B. Dost and A. Su, "TCLUST: A Fast Method for Clustering Genome-Scale Expression Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 808-818, 2010.