A Statistical Perspective on the Challenges in Molecular Microbial Biology

TL;DR

This paper discusses the statistical challenges in analyzing high throughput sequencing data for microbial communities and reviews tools and methods to address issues like contamination, batch effects, and heteroscedasticity.

Contribution

It introduces statistical tools such as hierarchical mixture and topic models, and reviews nonparametric Bayesian approaches for microbial data analysis.

Findings

Standard methods can facilitate inference on microbial communities.

Bayesian approaches help visualize and quantify uncertainty.

Addressing statistical challenges improves microbial data interpretation.

Abstract

High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.