Sparse canonical correlation analysis applied to ‐omics studies for integrative analysis and biomarker discovery. (19th April 2015)

• Record Type: Journal Article
• Authors: Cao, Dong‐Sheng; Liu, Shao; Zeng, Wen‐Bin; Liang, Yi‐Zeng
• Is Part Of: Journal of chemometrics. Volume 29:Number 6(2015:Jun.)
• Page Start: 371
• Page End: 378
• Publication Date: 2015-04-19
• Subjects: Chemistry -- Mathematics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; 542.85
• DOI: 10.1002/cem.2716
• Languages: English
• ISSNs: 0886-9383

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>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.</p> </abstract>