This page introduces Canonical Correlation Analysis (CCA) in mixOmics, an unsupervised method used to explore relationships between two datasets by finding linear combinations (canonical variates) that maximise their correlation. Classical CCA is best for cases where the total number of variables is less than the number of samples, while Regularised CCA (rCCA) applies methods like ridge and shrinkage to handle high-dimensional or collinear data.
Typical (r)CCA-type questions:
– Does the information from both datasets agree and reflect any biological condition of interest?
– What is the overall correlation between them?
Data used on this page:nutrimouse
Key functions used on this page:rcc()plotIndiv()plotVar()
Related case studies:
Case Study: rCCA Nutrimouse
References:
1. Vinod, H.D., 1976. Canonical ridge and econometrics of joint production. Journal of econometrics, 4(2), pp.147-166.
2. Leurgans, S.E., Moyeed, R.A. and Silverman, B.W., 1993. Canonical correlation analysis when the data are curves. Journal of the Royal Statistical Society. Series B (Methodological), pp.725-740.