A Statistical Perspective on the Challenges in Molecular Microbial Biology

Area of Science:

• Microbiology
• Bioinformatics
• Statistical Science

Background:

• High throughput sequencing (HTS) revolutionized microbial ecology by enabling the study of non-culturable organisms.
• Microbial sequence data from diverse environments (human microbiome, soil, marine) offer profound biological insights.
• However, this data is prone to statistical challenges including contamination, batch effects, and unequal sampling.

Purpose of the Study:

• To introduce statistical tools for overcoming common challenges in analyzing high throughput sequencing microbial data.
• To demonstrate the application of these statistical methods on a practical example.
• To highlight the potential of integrating statistics with molecular microbial biology.

Main Methods:

• Application of standard statistical methods like hierarchical mixture and topic models.
• Review of nonparametric Bayesian approaches for visualization and uncertainty quantification.
• Demonstration of tools on an example dataset.

Main Results:

• Hierarchical mixture and topic models facilitate inferences on latent microbial communities.
• Nonparametric Bayesian methods aid in visualizing microbial data and quantifying uncertainty.
• The study illustrates how statistical tools can manage biases and improve differential abundance testing.

Conclusions:

• Statistical methods are crucial for robust analysis of high throughput sequencing microbial data.
• Integrating advanced statistical approaches enhances our understanding of microbial communities.
• Further statistical method development is needed to address remaining open problems in the field.