{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:53:51Z","timestamp":1775105631559,"version":"3.50.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,1,15]]},"abstract":"<jats:p>Motivation: Over the last decade, a large variety of clustering algorithms have been developed to detect coregulatory relationships among genes from microarray gene expression data. Model-based clustering approaches have emerged as statistically well-grounded methods, but the properties of these algorithms when applied to large-scale data sets are not always well understood. An in-depth analysis can reveal important insights about the performance of the algorithm, the expected quality of the output clusters, and the possibilities for extracting more relevant information out of a particular data set.<\/jats:p>\n               <jats:p>Results: We have extended an existing algorithm for model-based clustering of genes to simultaneously cluster genes and conditions, and used three large compendia of gene expression data for Saccharomyces cerevisiae to analyze its properties. The algorithm uses a Bayesian approach and a Gibbs sampling procedure to iteratively update the cluster assignment of each gene and condition. For large-scale data sets, the posterior distribution is strongly peaked on a limited number of equiprobable clusterings. A GO annotation analysis shows that these local maxima are all biologically equally significant, and that simultaneously clustering genes and conditions performs better than only clustering genes and assuming independent conditions. A collection of distinct equivalent clusterings can be summarized as a weighted graph on the set of genes, from which we extract fuzzy, overlapping clusters using a graph spectral method. The cores of these fuzzy clusters contain tight sets of strongly coexpressed genes, while the overlaps exhibit relations between genes showing only partial coexpression.<\/jats:p>\n               <jats:p>Availability: GaneSh, a Java package for coclustering, is available under the terms of the GNU General Public License from our website at http:\/\/bioinformatics.psb.ugent.be\/software<\/jats:p>\n               <jats:p>Contact: \u00a0yves.vandepeer@psb.ugent.be<\/jats:p>\n               <jats:p>Supplementary information: Supplementary data are available on our website at http:\/\/bioinformatics.psb.ugent.be\/supplementary_data\/anjos\/gibbs<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm562","type":"journal-article","created":{"date-parts":[[2007,11,23]],"date-time":"2007-11-23T01:33:56Z","timestamp":1195781636000},"page":"176-183","source":"Crossref","is-referenced-by-count":53,"title":["Analysis of a Gibbs sampler method for model-based clustering of gene expression data"],"prefix":"10.1093","volume":"24","author":[{"given":"Anagha","family":"Joshi","sequence":"first","affiliation":[{"name":"1 \u00a01Department of Plant Systems Biology, VIB and 2Department of Molecular Genetics, UGent, Technologiepark 927, 9052 Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yves","family":"Van de Peer","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Plant Systems Biology, VIB and 2Department of Molecular Genetics, UGent, Technologiepark 927, 9052 Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom","family":"Michoel","sequence":"additional","affiliation":[{"name":"1 \u00a01Department of Plant Systems Biology, VIB and 2Department of Molecular Genetics, UGent, Technologiepark 927, 9052 Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2007,11,22]]},"reference":[{"key":"2023020209485103600_B1","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/75556","article-title":"Gene ontology: tool for the unification of biology. 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