Discriminant analysis to evaluate clustering of gene expression data.
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.
Méndez
Marco A
MA
Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, Macul 5540, Santiago, Chile.
Hödar
Christian
C
Vulpe
Chris
C
González
Mauricio
M
Cambiazo
Verónica
V
D000818:Animals; D016000:Cluster Analysis; D016002:Discriminant Analysis; D004331:Drosophila melanogaster / Q000235:genetics*; D004440:Ecdysone / Q000494:pharmacology*; D015870:Gene Expression; D017344:Genes, Insect; D005810:Multigene Family; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods
D004440:Ecdysone