Systematic Comparisons for Composition Profiles, Taxonomic Levels, and Machine Learning Methods for Microbiome-Based...

Area of Science:

• Microbiome research
• Bioinformatics
• Computational biology

Background:

• Microbiome composition is crucial for predicting phenotypes, including complex diseases like diabetes and obesity.
• Traditional microbiome analysis uses Operational Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV) count matrices.
• The impact of different microbiome quantification and machine learning methods on prediction accuracy is not well-understood.

Purpose of the Study:

• To comprehensively evaluate over 1,000 combinations of microbiome data analysis methods for phenotype prediction.
• To compare the effectiveness of Operational Taxonomic Unit (OTU) counts, Amplicon Sequence Variant (ASV) counts, and k-mer counts as predictors.
• To assess the performance of various machine learning algorithms in conjunction with different microbiome data processing techniques.

Main Methods:

• Investigated over 1,000 combinations of microbiome quantification (OTU, ASV, k-mer counts) and machine learning methods.
• Explored various normalization, filtering, and taxonomic grouping strategies.
• Evaluated more than ten commonly used machine learning algorithms for phenotype prediction tasks.

Main Results:

• Short k-mer counts demonstrated effectiveness for microbiome-based prediction, offering computational and conceptual advantages.
• Tree-based machine learning methods consistently showed modest improvements in prediction accuracy compared to other approaches.
• The study identified advantages and disadvantages of various analysis pipeline combinations.

Conclusions:

• K-mer based microbiome profiling is a viable and efficient approach for phenotype prediction.
• Tree-based algorithms offer a reliable choice for machine learning in microbiome trait prediction.
• This research provides guidance for optimizing microbiome data analysis strategies for future trait-prediction studies.