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- Title
What is principal component analysis?
- Authors
Ringnér, Markus
- Abstract
The article offers information on principal component analysis (PCA) and how it can be used to explore high-dimensional data. According to the author, PCA is a mathematical algorithm that reduces the dimensionality of the data, while retaining most of the variation in the data set. He added that new variables and the principal components which are linear combinations of the original variables are being identified by PCA. Moreover, PCA applications include identifying patterns that correlate with experimental artifacts and filtering them out, estimating missing data and associating genes and expression patterns with activities of regulators.
- Subjects
PRINCIPAL components analysis; GENETIC algorithms; BIOTECHNOLOGY; GENETIC engineering; HUMAN genome; GENETICS; GENES; GENOMES; STATISTICAL correlation
- Publication
Nature Biotechnology, 2008, Vol 26, Issue 3, p303
- ISSN
1087-0156
- Publication type
Article
- DOI
10.1038/nbt0308-303