title: Block principal component analysis with application to gene microarray data classification.
abstract: 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.
authors.affiliation: Biostatistics Unit, Lombardi Cancer Center, Georgetown University Medical Center, 3800 Reservoir Road, NW, Washington, DC 20007, USA. Liua1@georgetown.edu
authors.forename: Aiyi
authors.initials: A
authors.lastname: Liu
authors.forename: Ying
authors.initials: Y
authors.lastname: Zhang
authors.forename: Edmund
authors.initials: E
authors.lastname: Gehan
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
mesh_terms: D004273:DNA, Neoplasm / Q000145:classification*; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*; D014407:Tumor Cells, Cultured
chemical_list: D004273:DNA, Neoplasm