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.
Original language | English (US) |
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Pages (from-to) | 3465-3474 |
Number of pages | 10 |
Journal | Statistics in Medicine |
Volume | 21 |
Issue number | 22 |
DOIs | |
State | Published - Nov 30 2002 |
Externally published | Yes |
Keywords
- Gene expression
- Grouping of variables
- Microarray data analysis
- Principal component analysis
- Similarity