Differential Canonical Correlation Analysis (dCCA)

Thomas M. Menino Convention & Exhibition Center 

Canonical Correlation Analysis (CCA) involves identifying the linear combinations of two sets of measurements from the same set of observations that have the highest correlation. This method has been used in genomics data to explore associations between different data modalities, such as DNA copy number and single nucleotide polymorphisms (SNPs). In this study, we focus on differential CCA (dCCA), an extension of CCA that maximizes the absolute difference between canonical correlations from two distinct groups. This identifies linear combinations that maximally contrast the between-measurement associations in one group versus the other. We propose algorithms to solve both the sparse and dense linear combinations of variables. In addition, we propose a generalization of dCCA for multiple groups. This method finds linear combinations such that the resulting canonical correlation from each group exhibits the strongest possible association with a continuous outcome variable (e.g., a risk score) assigned to that group. Finally, we explore applications of dCCA in studying cell-cell interactions as well as axial patterns in spatial transcriptomics data.

spatial transcriptomics

canonical correlation analysis 

Section on Statistics in Genomics and Genetics