Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.

ABSTRACT:

Background

Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes.

Methods

In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are a

SUBMITTER: Dutta D 

PROVIDER: S-EPMC9803132 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature