MetaHD: A multivariate meta-analysis model for metabolomics data.

ABSTRACT:

Motivation

Meta-analysis methods widely-used for combining metabolomics data do not account for correlation between metabolites or missing values. Within- and between-study variability are also often overlooked. These can give results with inferior statistical properties, leading to misidentification of biomarkers.

Results

We propose a multivariate meta-analysis model for high-dimensional metabolomics data (MetaHD), which accommodates the correlation between metabolites, within- and between-study variances, and missing values. MetaHD can be used for integrating and collectively analysing individual-level metabolomics data generated from multiple studies as well as for combining summary estimates. We show that MetaHD leads to lower root mean square error compared to the exist

SUBMITTER: Liyanage JC 

PROVIDER: S-EPMC11520400 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature