Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.

ABSTRACT:

Background

Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.

Method

In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both om

SUBMITTER: Zhao C 

PROVIDER: S-EPMC11324385 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature