Discriminant analysis to evaluate clustering of gene expression data

Mendez, MA; Hodar, C; Vulpe, C; Gonzalez, M.; Cambiazo, V

Abstract

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. © 2002 Federation of European Biochemical Societies. Published by Elsevier Science B.V. All rights reserved.

Más información

Título según WOS: Discriminant analysis to evaluate clustering of gene expression data
Título según SCOPUS: Discriminant analysis to evaluate clustering of gene expression data
Título de la Revista: FEBS LETTERS
Volumen: 522
Número: 01-mar
Editorial: Wiley
Fecha de publicación: 2002
Página de inicio: 24
Página final: 28
Idioma: English
URL: http://linkinghub.elsevier.com/retrieve/pii/S0014579302028739
DOI:

10.1016/S0014-5793(02)02873-9

Notas: ISI, SCOPUS