OBC: Optimized Batch Correction with Dual-level Quality Control for Scalable Proteomics and Metabolomics.

Area of Science:

• Biochemistry
• Bioinformatics
• Computational Biology

Background:

• Large-scale proteomics and metabolomics studies generate vast datasets.
• Technical variations, known as batch effects, are introduced during multi-instrument, multi-period sample analysis.
• These batch effects can obscure true biological signals and complicate data interpretation.

Purpose of the Study:

• To develop and validate a computational pipeline for mitigating batch effects in omics data.
• To integrate optimized data preprocessing and a dual-tier quality control system for both proteomic and metabolomic datasets.
• To provide a user-friendly tool for researchers to enhance the reliability of their omics data.

Main Methods:

• Development of the "omics batch correct (OBC)" pipeline.
• Integration of normalization, missing value imputation, and batch correction algorithms.
• Implementation of a two-tier quality control system using PCA, t-SNE, UMAP, RSD, Pearson correlation, PVCA, and differential expression analysis.
• Validation using clinical proteomic and metabolomic datasets through cross-validation.

Main Results:

• The OBC pipeline effectively reduces batch effects in proteomic and metabolomic data.
• The pipeline preserves biologically significant variations while removing technical noise.
• Cross-validation demonstrated superior performance compared to existing methods.
• The OBC pipeline is accessible via a web interface.

Conclusions:

• The OBC pipeline offers a robust solution for addressing batch effects in large-scale omics studies.
• It improves the accuracy and reliability of data analysis in proteomics and metabolomics.
• The tool facilitates the discovery of genuine biological insights from complex datasets.