Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study.

ABSTRACT:

Motivation

Recent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. Several approaches have been proposed for the integration of heterogeneous and high-dimensional (p≫n) data, such as OMICS. The sparse variant of canonical correlation analys

SUBMITTER: Rodosthenous T 

PROVIDER: S-EPMC7750936 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature