Block principal component analysis with application to gene microarray data classification

Aiyi Liu, Ying Zhang, Edmund Gehan, Robert Clarke

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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 languageEnglish (US)
Pages (from-to)3465-3474
Number of pages10
JournalStatistics in Medicine
Issue number22
StatePublished - Nov 30 2002
Externally publishedYes


  • Gene expression
  • Grouping of variables
  • Microarray data analysis
  • Principal component analysis
  • Similarity


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