Local Sample Cohesion Normalization: Preserving Inherent Biological Heterogeneity in Metabolomics Data.

Area of Science:

• Metabolomics
• Bioinformatics
• Data Science

Background:

• Metabolomics data often suffers from dilution effects, confounding downstream analyses.
• Current normalization methods (CSN, L2N, PQN, QT) use global references, failing to preserve biological heterogeneity.
• This artificial uniformity distorts biological data structures and impacts analytical outcomes.

Purpose of the Study:

• To develop a novel normalization method, Local Sample Cohesion Normalization (LSCN), that corrects dilution effects while preserving biological heterogeneity.
• To provide a robust, biologically faithful preprocessing framework for metabolomics data.

Main Methods:

• LSCN constructs sample-specific neighbor sets based on pairwise similarity in reduced dimensions.
• Normalization is performed locally within these identified neighborhoods, mitigating technical bias.
• Validated against conventional methods (CSN, L2N, PQN, QT) using simulated and real-world metabolomics datasets.

Main Results:

• LSCN demonstrated superior performance in preserving heterogeneity and biological signals compared to conventional methods.
• Enhanced identification of differential metabolites, correlation networks, pathway enrichment, and classification accuracy.
• Effective correction of dilution effects, yielding more accurate normalization factors and reduced within-group variance.

Conclusions:

• LSCN offers a robust and biologically faithful approach to metabolomics data preprocessing.
• This method improves the reliability of downstream analyses by accurately correcting dilution effects without losing biological variation.
• LSCN is a valuable tool for advancing metabolomics research across various sample types.