Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
This is an incremental tutorial aimed at researchers in biology and medicine to help them apply existing CCA methods to complex data.
The paper addresses the challenge of analyzing high-dimensional, multimodal datasets in biomedicine, such as those with thousands of subjects and hundreds of variables, by presenting a tutorial on canonical correlation analysis (CCA) as a method to uncover hidden associations between different data modalities.
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. The complexity of such big data repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. This doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. Our primer discusses the rationale, promises, and pitfalls of CCA in biomedicine.