# Statistical Methods for Microbiome Analysis: A brief review

**Authors:** M. Bhattacharjee  
**arXiv:** [2303.16722](https://arxiv.org/abs/2303.16722)  
**Date:** 2023-03-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.16722v1/

## Key points

- Recent global outbreaks of various viruses have emphasized the need for extensive and robust analysis of microbiome data.
- Microbiome refers to the collective genomes of microorganisms, as well as the organisms themselves.
- Advanced statistical modelling techniques are required for effective analysis due to increasing complexity and size of microbiome datasets.
- Useful statistical methods in analysing microbiome data include parametric and non-parametric methods, post-hoc analysis, and model-based techniques.
- Exploring and developing analytical approaches is important to better understand complex microbiome data.
- Statistical methods are significant in advancing our understanding of microbiomes and their impact on human health and disease.
- Researchers can gain valuable insights into how microbial communities interact with their environment and how they influence human health outcomes by utilizing these methods effectively.

## Summary

Recent global outbreaks of various viruses have highlighted the need for extensive and robust analysis of microbiome data. Microbiome refers to the collective genomes of microorganisms, as well as the organisms themselves. With the increasing complexity and size of microbiome datasets, advanced statistical modelling techniques are required for effective analysis. This brief review provides an overview of statistical methods that are useful in analysing microbiome data, including parametric and non-parametric methods, post-hoc analysis, and model-based techniques. The review also emphasizes the importance of exploring and developing analytical approaches to better understand complex microbiome data. Overall, this review highlights the significance of statistical methods in advancing our understanding of microbiomes and their impact on human health and disease. By utilizing these methods to analyse microbiome data effectively, researchers can gain valuable insights into how microbial communities interact with their environment and how they influence human health outcomes.