Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

Основні поняття

The author proposes a novel method using multi-view variational autoencoder to impute missing metabolites in untargeted metabolomics data, leveraging genetic information from whole-genome sequencing data.

Анотація

The study introduces a method that combines whole-genome sequencing data with metabolomics data to impute missing values. By utilizing a multi-view variational autoencoder, the approach effectively imputes missing metabolomics values based on genomic information. The integration of WGS data enhances downstream analyses and provides insights into metabolic pathways and disease associations.

Статистика

Using 35 template metabolites derived burden scores, PGS, and LD-pruned SNPs, the proposed methods achieved 𝑅𝑅2-scores > 0.01 for 71.55% of metabolites.

Цитати

Ключові висновки, отримані з

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics

by Chen Zhao,Ku... о arxiv.org 03-13-2024

https://arxiv.org/pdf/2310.07990.pdf

Глибші Запити

How can the integration of WGS data improve precision medicine research beyond metabolomics

The integration of Whole Genome Sequencing (WGS) data in precision medicine research goes beyond metabolomics by providing a comprehensive understanding of the genetic underpinnings of diseases and treatment responses. WGS data offers insights into an individual's complete genetic makeup, including rare variants, structural variations, and non-coding regions that can influence disease susceptibility and drug metabolism. By combining WGS with metabolomics data, researchers can identify novel biomarkers, pathways, and therapeutic targets for personalized medicine approaches. This integration enables a more holistic view of the molecular mechanisms underlying health and disease states.

What are potential limitations or biases introduced by using cross-omics based imputation methods

While cross-omics based imputation methods offer significant advantages in integrating diverse datasets for missing value imputation in metabolomics studies, they also come with potential limitations and biases. One limitation is the assumption of linear relationships between different omics layers, which may oversimplify complex biological interactions. Biases can arise from batch effects, platform-specific variations, or confounding factors not accounted for during integration. Additionally, imputing missing values based on correlated features across omics layers may introduce noise or propagate errors if the correlations are spurious or context-dependent.

How might advancements in multi-modal data integration impact future studies in metabolomics and genomics

Advancements in multi-modal data integration have the potential to revolutionize future studies in metabolomics and genomics by enabling a more comprehensive understanding of biological systems. By leveraging multiple omics datasets simultaneously, researchers can uncover intricate relationships between genes, proteins, metabolites, and environmental factors that contribute to health outcomes. This integrated approach allows for a deeper exploration of complex diseases such as cancer or metabolic disorders by capturing the interplay between genetic predisposition and environmental influences at various molecular levels. Furthermore,
multi-modal data integration facilitates systems biology analyses that consider the dynamic interactions within biological networks rather than isolated components—a crucial step towards unraveling the complexity of living organisms.