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Sparse Canonical Correlation Analysis
Data Integration for regular and high dimensional studies
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Sparse Canonical Correlation Analysis (SCCA) §performs data integration by simultaneous analysis §of 2 data types to find the relationships between §them. It is applicable to all studies including §large studies with limited sample size. SCCA §provides sparse solutions that include only a small §subset of variables. Sparse results aid in §biological interpretability and can be used for §hypothesis generation. This monograph presents §methodology for SCCA and evaluation of its §properties using simulated data. The practical use §of SCCA is illustrated by applying it to the study §of natural variation in human gene expression. Two §extensions of SCCA - adaptive SCCA and modified §adaptive SCCA are also presented. Their performance §is evaluated and compared using simulated data and §adaptive SCCA is applied to the study of natural §variation in human gene expression.
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