UC San Diego

Large-scale microbiome datasets from 16S amplicon sequencing provide opportunities for building predictive models with supervised machine learning to answer questions of biological significance. Prior regression analyses have used supervised learning to predict variables of the sampled microbial environment, such as pH, host age, or other host phenotypes and disease states, however little justification has been made for the use of specific algorithms on microbiome data. We performed a large-scale comprehensive benchmark for 11 regression algorithms across an exhaustive grid search for tuning algorithm hyperparameters, in three large human datasets: The National FINRISK Study, Study of Latinos, and International Multiple Sclerosis Microbiome Study. We found that ensemble-based algorithms consistently performed the best, confirming prior analyses’ use of ensemble algorithms such as Random Forests. For the most accurate ensemble algorithms, we analyzed the best hyperparameters from our grid search to produce a set of hyperparameters that we recommend to be fixed at specific values. With those recommended hyperparameter settings, we observed no loss in accuracy and significant reductions in the runtime and computational expense of hyperparameter tuning. Our results suggest the feasibility of further streamlining the process of producing robust machine learning models specific to microbiome data. These results may generalize to compositional data obtained from other preparations, such as taxonomic profiles from shotgun metagenomic analyses, and an expansion of this work to include metagenomics profiles as well as other machine learning tasks presents an exciting opportunity.