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Updated: Jul 17, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

Ying Yu¹, Naixin Zhang¹, Yuanbang Mai¹

¹State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.

|September 6, 2023

Summary

This summary is machine-generated.

Batch effects in multiomics data can mislead results. A ratio-based scaling method effectively corrects these variations, offering a broadly applicable solution for accurate data integration and analysis in large-scale studies.

Keywords:

Batch effect Data integration Differentially expressed Metrology Multiomics Phenomics Prediction Quartet family Ratio Reference materials

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Area of Science:

Bioinformatics
Genomics
Data Science

Background:

Multiomics data frequently contain batch effects, which are technical variations that can distort results if not properly managed.
Existing batch-effect correction algorithms lack comprehensive assessment regarding their performance across different omics types, metrics, and applications.

Purpose of the Study:

To comprehensively assess and compare the performance of seven batch-effect correction algorithms for multiomics data integration.
To identify the most effective and broadly applicable method for correcting batch effects, particularly when confounded with biological signals.

Main Methods:

Evaluation of seven batch-effect correction algorithms using metrics relevant to clinical applications, including differential expression accuracy, predictive model robustness, and sample clustering.
Implementation and assessment of a ratio-based scaling method using concurrently profiled reference materials.

Main Results:

The ratio-based method demonstrated superior effectiveness and broader applicability compared to other tested algorithms.
This method proved particularly advantageous when batch effects were confounded with biological factors of interest.
Practical guidelines for applying the ratio-based approach in large-scale multiomics studies were developed.

Conclusions:

Multiomics data are susceptible to batch effects, but ratio-based scaling offers an effective correction strategy.
This study establishes a foundational approach for eliminating batch effects at a ratio scale in multiomics profiling.