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Discriminant analysis to evaluate clustering of gene expression data
Author(s) -
Méndez Marco A,
Hödar Christian,
Vulpe Chris,
González Mauricio,
Cambiazo Verónica
Publication year - 2002
Publication title -
febs letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.593
H-Index - 257
eISSN - 1873-3468
pISSN - 0014-5793
DOI - 10.1016/s0014-5793(02)02873-9
Subject(s) - linear discriminant analysis , principal component analysis , cluster analysis , hierarchical clustering , dimensionality reduction , data mining , data set , dimension (graph theory) , computer science , discriminant , clustering high dimensional data , artificial intelligence , computational biology , microarray analysis techniques , pattern recognition (psychology) , gene , biology , gene expression , mathematics , genetics , pure mathematics
In this work we present a procedure that combines classical statistical methods to assess the confidence of gene clusters identified by hierarchical clustering of expression data. This approach was applied to a publicly released Drosophila metamorphosis data set [White et al., Science 286 (1999) 2179–2184]. We have been able to produce reliable classifications of gene groups and genes within the groups by applying unsupervised (cluster analysis), dimension reduction (principal component analysis) and supervised methods (linear discriminant analysis) in a sequential form. This procedure provides a means to select relevant information from microarray data, reducing the number of genes and clusters that require further biological analysis.