Machine learning approaches in microbiome research: challenges and best practices.

Area of Science:

• Microbiome research
• Machine learning applications
• Bioinformatics

Background:

• Microbiome data analysis using machine learning (ML) involves complex challenges in data preprocessing, feature selection, and model interpretation.
• Translational applications require robust methods for disease diagnosis and biomarker discovery from complex biological datasets.

Purpose of the Study:

• To provide recommendations for ML workflows in microbiome data analysis.
• To evaluate different ML approaches for colorectal cancer diagnosis and biomarker discovery using shotgun metagenomics data.

Main Methods:

• Comparison of preprocessing techniques, including compositional transformations and filtering.
• Evaluation of multivariate feature selection algorithms (e.g., Statistically Equivalent Signatures).
• Application of random forest and logistic regression models with Individual Conditional Expectation (ICE) plots for interpretation.

Main Results:

• Compositional transformations and filtering did not consistently enhance predictive performance.
• Multivariate feature selection, particularly the Statistically Equivalent Signatures algorithm, effectively reduced classification error.
• Random forest modeling combined with Statistically Equivalent Signatures achieved the most accurate performance estimates on a test dataset.

Conclusions:

• Specific feature selection methods are crucial for accurate microbiome-based disease prediction.
• Interpretable models like logistic regression with ICE plots can provide valuable biological insights for clinicians and researchers.
• The study offers practical guidance for developing effective ML pipelines for microbiome data analysis.