2018/12/06 by Hao-Ting Wang, Jonathan Smallwood, Wang, Hao-Ting +13
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Applications (stat.AP) #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #cs.LG #stat.AP #stat.ML
paper · pdf · doi:10.48550/arxiv.1812.02598
arxiv created 2018/12/06 · openalex publication_date 2018/12/06 · arxiv updated 2018/12/10 · openalex created_date 2022/08/01 · openalex updated_date 2026/07/28
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.