Applied Microbiome Statistics Correlation, Association, Interaction and Composition

Preface

Acknowledgement
About the Authors

1. Introduction to Microbiome Statistics

2. Classical Parametric Correlation

3. Classical Nonparametric Correlation

4. Composition Barplots

5. Composition Heatmaps

6. Correlation Heatmaps and plots

7. Model Selection for Correlation and Association Analysis

8. Alpha Diversity-Based Association Analysis

9. Beta Diversity-Based Association Analysis

10. Multiple Comparisons and Multiple Hypothesis Testing

11. Multiple Comparisons and Multiple Hypothesis Testing in Microbiome Research

12. Linear Discriminant Analysis Effect Size (LEfSe)

13. Sparse and Compositional Methods for Inferencing Microbial Interactions

14. Network Construction and Comparison for Microbiome Data

15. Microbial Networks in Semi-Parametric Rank-Based Correlation and Partial Correlation Estimation

References

Biography

Yinglin Xia is a clinical professor in the Department of Medicine at the University of Illinois Chicago (UIC). He has published four books on statistical analysis of microbiome and metabolomics data and more than 160 statistical methodology and research papers in peer-reviewed journals. He serves on the editorial boards of several scientific journals, including as an associate editor of Gut Microbes, and has served as a reviewer for over 100 scientific journals.

Jun Sun is a tenured professor of medicine at the University of Illinois Chicago (UIC). She is an internationally recognized expert on microbiome and human diseases, such as vitamin D receptor in inflammation, dysbiosis, and intestinal dysfunction in amyotrophic lateral sclerosis (ALS). Her lab was the first to discover the chronic effects and molecular mechanisms of Salmonella infection and development of colon cancer. Dr. Sun has published over 220 scientific articles in peer-reviewed journals and nine books on the microbiome.

"We highly recommend this book for graduate students, bioinformaticians, statisticians, and biomedical researchers as an essential reference in comprehensive exploration, modeling, and inference of microbiome data. With systematic discussions and illustrations of implementation in R, this book provides in-depth insights to understand and apply statistical methods in microbiome data analysis."

-Oktaviyani Daswati and Yunia Hasnataeni, Technometrics, Vol. 67, 2025

"Applied Microbiome Statistics: Correlation, Association, Interaction and Composition offers a unified and rigorous introduction to statistical approaches for analyzing microbiome data. Drawing from their complementary biostatistics and clinical backgrounds, Professors Yinglin Xia and Jun Sun introduce the reader to the essential challenges of microbiome science and the statistical methods that can be used to address them. Chapters are organized around scientific questions, like assessing diversity and identifying microbial interactions. Each provides a self-contained description of the relevant methods, a comparison of their merits,and computational examples in R applying the methods to realmicrobiome data."

-Kris Sankaran, Biometrics, ujaf136, 10 October 2025