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Over the last decade, a large variety of clustering algorithms have beendeveloped to detect coregulatory relationships among genes from microarray geneexpression data. Model based clustering approaches have emerged asstatistically well grounded methods, but the properties of these algorithmswhen applied to large-scale data sets are not always well understood. Anin-depth analysis can reveal important insights about the performance of thealgorithm, the expected quality of the output clusters, and the possibilitiesfor extracting more relevant information out of a particular data set. We haveextended an existing algorithm for model based clustering of genes tosimultaneously cluster genes and conditions, and used three large compendia ofgene expression data for S. cerevisiae to analyze its properties. The algorithmuses a Bayesian approach and a Gibbs sampling procedure to iteratively updatethe cluster assignment of each gene and condition. For large-scale data sets,the posterior distribution is strongly peaked on a limited number ofequiprobable clusterings. A GO annotation analysis shows that these localmaxima are all biologically equally significant, and that simultaneouslyclustering genes and conditions performs better than only clustering genes andassuming independent conditions. A collection of distinct equivalentclusterings can be summarized as a weighted graph on the set of genes, fromwhich we extract fuzzy, overlapping clusters using a graph spectral method. Thecores of these fuzzy clusters contain tight sets of strongly coexpressed genes,while the overlaps exhibit relations between genes showing only partialcoexpression.

Analysis of a Gibbs sampler method for model based clustering of gene expression data