Assisted clustering of gene expression data using regulatory data from partially overlapping sets of individuals.

ABSTRACT:

Background

As omics measurements profiled on different molecular layers are interconnected, integrative approaches that incorporate the regulatory effect from multi-level omics data are needed. When the multi-level omics data are from the same individuals, gene expression (GE) clusters can be identified using information from regulators like genetic variants and DNA methylation. When the multi-level omics data are from different individuals, the choice of integration approaches is limited.

Methods

We developed an approach to improve GE clustering from microarray data by integrating regulatory data from different but partially overlapping sets of individuals. We achieve this through (1) decomposing gene expression into the regulated component and the other component that is n

SUBMITTER: Jiang W 

PROVIDER: S-EPMC9734806 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature