Discriminant analysis to evaluate clustering of gene expression data

FEBS Letters

Hödar, Christian¹  Méndez, Marco A¹  González, Mauricio¹  Vulpe, Chris²  Cambiazo, Verónica¹ 

[1] Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, Macul 5540, Macul, Santiago, Chile;Nutrition and Toxicology, 119 Morgan Hall, University of California, Berkeley, CA, USA

关键词: Microarray;    Gene expression data;    Cluster analysis;    Principal component analysis;    Discriminant analysis;    Drosophila;   

DOI  :  10.1016/S0014-5793(02)02873-9

学科分类：生物化学/生物物理

来源: John Wiley & Sons Ltd.

【 摘 要 】

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.

【 授权许可】

Unknown