NPM: latent batch effects correction of omics data by nearest-pair matching.

Area of Science:

• Omics data analysis
• Biomedical research
• Bioinformatics

Background:

• Batch effects (BEs) are a major source of noise in omics data, often obscuring true biological signals.
• Existing datasets frequently contain BEs, and current correction methods may be inadequate due to complex models or specific assumptions.
• Inadequate BE correction can hinder downstream analyses and reduce the power of scientific discovery.

Purpose of the Study:

• To develop a novel method for adjusting batch effects in omics data.
• To provide a robust and widely applicable solution for batch effect correction.
• To improve the accuracy and reliability of omics data analysis for enhanced biological discovery.

Main Methods:

• Developed NPM, a nearest-neighbor matching-based method for batch effect adjustment.
• Assessed NPM's performance using distinct metrics and graphical readouts.
• Compared NPM against commonly used batch effect correction methods.

Main Results:

• NPM effectively corrects for batch effects while preserving genuine biological differences.
• NPM demonstrates superior performance compared to other methods across multiple evaluation metrics.
• The method shows promise in maximizing discovery potential in biomedical and clinical research.

Conclusions:

• NPM is a valuable approach for batch effect correction in omics data.
• The method is particularly applicable in clinical research where latent batch effects are prevalent.
• NPM offers improved discovery power for biomedical research by accurately adjusting for batch effects.