SCCA (Sparse Canonical Correlation Analysis) examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsets of variables of different types while performing variable selection. We also present an extension of SCCA – adaptive SCCA. We evaluate their properties using simulated data and illustrate practical use by applying both methods to the study of natural variation in human gene expression.
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Daniela M Witten and Robert J. Tibshirani
Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Stat Appl Genet Mol Biol. 2009 January 1; 8(1): Article 28.