A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome...

Area of Science:

• Microbiology
• Computational Biology
• Statistical Modeling

Background:

• Microbiome studies use next-generation sequencing, generating high-dimensional data with complex correlations.
• Analyzing this data requires statistical models capable of handling high dimensionality and modest sample sizes.
• Ensemble models, popular in other fields, have been under-explored for microbiome data analysis.

Purpose of the Study:

• To evaluate the performance of different statistical modeling ensembles for microbiome data analysis.
• To develop a simulation framework for generating realistic microbiome data to test analytical strategies.
• To identify optimal analytical methods for high-dimensional microbiome datasets.

Main Methods:

• Developed a simulation framework to mimic experimental microbiome data constraints.
• Systematically evaluated frequentist (stability selection with elastic net) and Bayesian (spike-and-slab Bayesian model averaging) regression modeling ensembles.
• Compared ensemble performance based on model space exploration and data characteristics.

Main Results:

• Bayesian model averaging (BMA) ensembles, exploring larger model spaces, showed superior performance and lower variability.
• Stability selection ensembles matched BMA performance in low sparsity scenarios with large regression coefficients.
• The study identified microbiome signatures associated with Candida albicans colonization during antibiotic exposure.

Conclusions:

• A methodology for simulating microbiome data was presented to aid analytical strategy evaluation.
• Larger ensembles demonstrate the strongest performance for high-dimensional microbiome data with modest sample sizes.
• The findings support the use of advanced ensemble methods for translational microbiome research and decision-making.