Gene-set integrative analysis of multi-omics data using tensor-based association test.

ABSTRACT:

Motivation

Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference.

Results

We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling eff

SUBMITTER: Chang SM 

PROVIDER: S-EPMC8388036 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature