Title: Correlating Cellular Features with Gene Expression using CCA,
Abstract: To understand the biology of cancer, joint analysis of multiple data
modalities, including imaging and genomics, is crucial. The involved nature of
gene-microenvironment interactions necessitates the use of algorithms which
treat both data types equally. We propose the use of canonical correlation
analysis (CCA) and a sparse variant as a preliminary discovery tool for
identifying connections across modalities, specifically between gene expression
and features describing cell and nucleus shape, texture, and stain intensity in
histopathological images. Applied to 615 breast cancer samples from The Cancer
Genome Atlas, CCA revealed significant correlation of several image features
with expression of PAM50 genes, known to be linked to outcome, while Sparse CCA
revealed associations with enrichment of pathways implicated in cancer without
leveraging prior biological understanding. These findings affirm the utility of
CCA for joint phenotype-genotype analysis of cancer.