  Integrative analyses of different high dimensional data types are becoming
increasingly popular. Similarly, incorporating prior functional relationships
among variables in data analysis has been a topic of increasing interest as it
helps elucidate underlying mechanisms among complex diseases. In this paper,
the goal is to assess association between transcriptomic and metabolomic data
from a Predictive Health Institute (PHI) study including healthy adults at high
risk of developing cardiovascular diseases. To this end, we develop statistical
methods for identifying sparse structure in canonical correlation analysis
(CCA) with incorporation of biological/structural information. Our proposed
methods use prior network structural information among genes and among
metabolites to guide selection of relevant genes and metabolites in sparse CCA,
providing insight on the molecular underpinning of cardiovascular disease. Our
simulations demonstrate that the structured sparse CCA methods outperform
several existing sparse CCA methods in selecting relevant genes and metabolites
when structural information is informative and are robust to mis-specified
structural information. Our analysis of the PHI study reveals that a number of
genes and metabolic pathways including some known to be associated with
cardiovascular diseases are enriched in the subset of genes and metabolites
selected by our proposed approach.
