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Sparse canonical correlation analysis applied to ‐omics studies for integrative analysis and biomarker discovery
Author(s) -
Cao DongSheng,
Liu Shao,
Zeng WenBin,
Liang YiZeng
Publication year - 2015
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2716
Subject(s) - canonical correlation , correlation , discriminative model , computer science , data mining , feature selection , curse of dimensionality , multivariate statistics , projection (relational algebra) , machine learning , mathematics , pattern recognition (psychology) , artificial intelligence , algorithm , geometry
With the rapid development of new ‐omics measurement methods, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods such as canonical correlation analysis (CCA) have been proposed to analyze the intrinsic correlation relationship by integrating two data sets. However, because of the high dimensionality of data and the relative scarcity of samples, the ordinary CCA is usually faced with variable selection problems and thereby fails to obtain a satisfactory relationship. Here, we explored the potential of sparse CCA (SCCA) to find the correlative components in two sparse views. SCCA aims at finding sparse projection directions to well extract the correlation between two data sets. We applied this method to one simulation data and one real ‐omics data to illustrate the performance of SCCA. The results from two studies show that SCCA could effectively find the correlated patterns between two data sets, which are of high importance for understanding the relationship between two underlying chemical or biological processes. The corresponding variable subsets selected by sparse weight vectors can assist in a better interpretation of the chemical or biological process. The integrative analysis from two views by SCCA helps in improving the discriminative ability of classification models for various ‐omics studies. Copyright © 2015 John Wiley & Sons, Ltd.