Addressing the batch effect issue for LC/MS metabolomics data in data preprocessing.

Area of Science:

• Metabolomics
• Analytical Chemistry
• Bioinformatics

Background:

• Large-scale metabolomics studies often require processing samples in multiple batches using Liquid Chromatography-Mass Spectrometry (LC/MS).
• Technical limitations and variations across batches can introduce batch effects, impacting data quality and analysis.
• Traditional preprocessing methods often fail to account for these batch effects at an early stage.

Purpose of the Study:

• To develop a novel preprocessing approach to mitigate batch effects during LC/MS data analysis.
• To improve peak detection, alignment, and quantification in multi-batch metabolomics datasets.
• To enhance the consistency and reliability of feature tables for downstream analyses.

Main Methods:

• A new batch effect addressing method was developed and integrated into the apLCMS platform workflow.
• The approach focuses on correcting batch effects during the preprocessing stage, prior to downstream analysis.
• The method was evaluated using standardized quality control plasma samples and real biological study data.

Main Results:

• The new method significantly improved the consistency of feature tables generated from multi-batch data.
• Enhanced peak detection, alignment, and quantification accuracy were observed.
• Downstream analysis results showed marked improvements when using the new preprocessing approach.

Conclusions:

• The developed preprocessing method effectively addresses batch effects in LC/MS data, offering better consistency and accuracy.
• This approach is a valuable addition to the analytical toolkit for large-scale metabolomics studies involving multiple batches.
• The method is available as part of the apLCMS package for wider accessibility.