Statistical modelling of an outcome variable with integrated multi-omics.

ABSTRACT:

Background

In studies that aim to model the relationship between an outcome variable and multiple omics datasets, it is often desirable to reduce the dimensionality of these datasets or to represent one omics dataset in terms of another. Several approaches exist for this purpose, including univariate methods such as polygenic scores, and multivariate methods. Multivariate approaches offer advantages by producing lower-dimensional integrative scores, capturing joint structures across datasets, and filtering out dataset-specific noise. In this paper, we describe one univariate and two multivariate methods, and evaluate their performance through simulations involving two correlated multivariate normally distributed omics datasets, as well as a combination of one multivariate normal an

SUBMITTER: Li H 

PROVIDER: S-EPMC12859906 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature