CIT: identification of differentially expressed clusters of genes from microarray data | Zendy

Daniel R. Rhodes | Zendy; Jeremy C. Miller | Zendy; Brian B. Haab | Zendy; Kyle A. Furge | Zendy

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CIT: identification of differentially expressed clusters of genes from microarray data

Author(s) -

Daniel R. Rhodes,

Jeremy C. Miller,

Brian B. Haab,

Kyle A. Furge

Publication year - 2002

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/18.1.205

Subject(s) - microarray analysis techniques , microarray databases , identification (biology) , permutation (music) , microarray , computational biology , metric (unit) , cluster (spacecraft) , gene , gene chip analysis , biology , computer science , genetics , data mining , gene expression , engineering , botany , physics , operations management , acoustics , programming language

Cluster Identification Tool (CIT) is a microarray analysis program that identifies differentially expressed genes. Following division of experimental samples based on a parameter of interest, CIT uses a statistical discrimination metric and permutation analysis to identify clusters of genes or individual genes that best differentiate between the experimental groups. CIT integrates with the freely available CLUSTER and TREEVIEW programs to form a more complete microarray analysis package.

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