Discriminant analysis to evaluate clustering of gene expression data
Discriminant analysis to evaluate clustering of gene expression data
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DOI：
10.1016/s0014-5793(02)02873-9
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发表时间：
2002-07-03
期刊：
影响因子：
3.5
通讯作者：
Cambiazo, V
中科院分区：
文献类型：
--
作者：
Méndez, MA;Hödar, C;Cambiazo, V
在这项工作中，我们提出了一个程序，结合经典的统计方法来评估表达数据的层次聚类确定的基因簇的信心。这种方法应用于公开发布的果蝇变态数据集[白色等人，Science 286（1999）2179-2184]。我们已经能够产生可靠的分类基因组和基因组内的应用无监督（聚类分析），降维（主成分分析）和监督的方法（线性判别分析）的顺序形式。该程序提供了一种从微阵列数据中选择相关信息的方法，减少了需要进一步生物学分析的基因和簇的数量。(C)2002年欧洲生物化学学会联合会。由Elsevier Science B. V.出版，版权所有。
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. (C) 2002 Federation of European Biochemical Societies. Published by Elsevier Science B.V. All rights reserved.