ICGE: an R package for detecting relevant clusters and atypical units in gene expression

BMC Bioinformatics

Software

Itziar Irigoien¹  Basilio Sierra¹  Concepcion Arenas² 

[1] Department of Computation Science and Artificial Intelligence, University of the Basque Country, Donostia, Spain;Department of Statistics, University of Barcelona, Barcelona, Spain;

关键词: Acute Myeloid Leukemia;    Acute Lymphoblastic Leukemia;    Cluster Structure;    Mahalanobis Distance;    Mixed Variable;   

DOI  :  10.1186/1471-2105-13-30

 received in 2011-06-02, accepted in 2012-02-13,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundGene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. 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.ResultsICGE is a user-friendly R 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.ConclusionsWe 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.

【 授权许可】

Unknown   
© Irigoien et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.