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An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample...) belongs to one of these previously identified clusters or to a new group.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>is a user-friendly package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1471-2105-13-30","type":"journal-article","created":{"date-parts":[[2012,2,13]],"date-time":"2012-02-13T13:14:21Z","timestamp":1329138861000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ICGE: an R package for detecting relevant clusters and atypical units in gene expression"],"prefix":"10.1186","volume":"13","author":[{"given":"Itziar","family":"Irigoien","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Basilio","family":"Sierra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Concepcion","family":"Arenas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2012,2,13]]},"reference":[{"key":"5183_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610928308827180","volume":"3","author":"R Calinski","year":"1974","unstructured":"Calinski R, Harabasz J: A Dendrite Method for Cluster Analysis. 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