{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:26:19Z","timestamp":1776281179401,"version":"3.50.1"},"reference-count":11,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Summary: Hierarchical clustering is a widely used method for detecting clusters in genomic data. Clusters are defined by cutting branches off the dendrogram. A common but inflexible method uses a constant height cutoff value; this method exhibits suboptimal performance on complicated dendrograms. We present the Dynamic Tree Cut R package that implements novel dynamic branch cutting methods for detecting clusters in a dendrogram depending on their shape. Compared to the constant height cutoff method, our techniques offer the following advantages: (1) they are capable of identifying nested clusters; (2) they are flexible\u2014cluster shape parameters can be tuned to suit the application at hand; (3) they are suitable for automation; and (4) they can optionally combine the advantages of hierarchical clustering and partitioning around medoids, giving better detection of outliers. We illustrate the use of these methods by applying them to protein\u2013protein interaction network data and to a simulated gene expression data set.<\/jats:p><jats:p>Availability: The Dynamic Tree Cut method is implemented in an R package available at http:\/\/www.genetics.ucla.edu\/labs\/horvath\/CoexpressionNetwork\/BranchCutting<\/jats:p><jats:p>Contact: \u00a0stevitihit@yahoo.com<\/jats:p><jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm563","type":"journal-article","created":{"date-parts":[[2007,11,17]],"date-time":"2007-11-17T01:25:29Z","timestamp":1195262729000},"page":"719-720","source":"Crossref","is-referenced-by-count":1663,"title":["Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R"],"prefix":"10.1093","volume":"24","author":[{"given":"Peter","family":"Langfelder","sequence":"first","affiliation":[{"name":"1 Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and 2Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"1 Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and 2Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA"}]},{"given":"Steve","family":"Horvath","sequence":"additional","affiliation":[{"name":"1 Department of Human Genetics, University of California at Los Angeles, CA 90095-7088 and 2Rosetta Inpharmatics-Merck Research Laboratories, Seattle, WA, USA"}]}],"member":"286","published-online":{"date-parts":[[2007,11,16]]},"reference":[{"key":"2023020210114692500_B1","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2164-7-40","article-title":"Gene connectivity, function, and sequence conservation: predictions from modularv yeast co-expression networks","volume":"7","author":"Carlson","year":"2006","journal-title":"BMC Genomics"},{"key":"2023020210114692500_B2","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1093\/bioinformatics\/btg119","article-title":"Fuzzy C-means method for clustering microarray data","volume":"19","author":"Dembele","year":"2003","journal-title":"Bioinformatics"},{"key":"2023020210114692500_B3","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/1752-0509-1-24","article-title":"Understanding network concepts in modules","volume":"1","author":"Dong","year":"2007","journal-title":"BMC Syst. 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