Premium
Block principal component analysis with application to gene microarray data classification
Author(s) -
Liu Aiyi,
Zhang Ying,
Gehan Edmund,
Clarke Robert
Publication year - 2002
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1263
Subject(s) - principal component analysis , block (permutation group theory) , computer science , microarray analysis techniques , component (thermodynamics) , microarray databases , data mining , computational biology , artificial intelligence , gene , mathematics , biology , genetics , gene expression , geometry , physics , thermodynamics
We propose a block principal component analysis method for extracting information from a database with a large number of variables and a relatively small number of subjects, such as a microarray gene expression database. This new procedure has the advantage of computational simplicity, and theory and numerical results demonstrate it to be as efficient as the ordinary principal component analysis when used for dimension reduction, variable selection and data visualization and classification. The method is illustrated with the well‐known National Cancer Institute database of 60 human cancer cell lines data (NCI60) of gene microarray expressions, in the context of classification of cancer cell lines. Copyright © 2002 John Wiley & Sons, Ltd.