Identifying Optimal Machine Learning Approaches for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics...

Area of Science:

• Microbiome research
• Computational biology
• Bioinformatics

Background:

• Microbiome research faces challenges due to methodological inconsistencies and limited functional insights from DNA-based profiling.
• Multi-omics integration offers a promising avenue to connect microbiome composition with function, but lacks standardized methods and consistent machine learning strategies, hindering reproducibility.
• The stability of feature selection in machine learning models for multi-omics microbiome data is crucial for experimental validation and biomarker discovery but remains underexplored.

Purpose of the Study:

• To systematically compare the performance and feature selection stability of different machine learning algorithms and multi-omics integration strategies for microbiome data.
• To evaluate the impact of feature reduction, data dimensionality, and response type on predictive performance and feature selection stability in multi-omics microbiome modeling.

Main Methods:

• Systematic comparison of Elastic Net, Random Forest, and XGBoost algorithms.
• Evaluation of five multi-omics integration strategies: Concatenation, Averaged Stacking, Weighted Non-negative Least Squares (NNLS), Lasso Stacking, and Partial Least Squares (PLS).
• Performance assessment across 588 binary and 735 continuous models using microbiome-derived metabolomics and taxonomic data, including analysis of feature reduction impact.

Main Results:

• Random Forest combined with Weighted Non-negative Least Squares (NNLS) demonstrated the highest overall predictive performance across diverse datasets.
• Tree-based methods, including Random Forest, exhibited consistent feature selection stability across different data types and dimensionalities.
• Integration strategies, algorithm choice, data dimensionality, and response type significantly influence both predictive accuracy and the reliability of selected features in multi-omics microbiome models.

Conclusions:

• The combination of Random Forest and NNLS provides a robust approach for multi-omics microbiome data integration and analysis.
• Consistent feature selection from tree-based methods supports their utility in identifying key microbial and metabolic features for downstream validation.
• Standardizing machine learning methodologies and integration strategies is essential for advancing reproducible and reliable microbiome multi-omics research.