This unique book officially defines Microbiome Statistics as a specific new field of statistics, and addresses the statistical analysis of correlation, association, interaction and composition in microbiome research. It also defines microbiome as a hypothesis-driven experimental science, describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigates challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.
- Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction and composition adopted from classical statistics, ecology, and specifically designed for microbiome research
- Performs step-by-step statistical analysis of correlation, association, interaction and composition in microbiome data
- Discusses the issues of statistical analysis of microbiome data: high-dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity
- Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data
- Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot.
- Employs Kruskal-Wallis rank-sum test to perform model selection for further multi-omics data integration
- Offers R codes, and the data sets from the authors real microbiome research and publicly available data for the analysis used
- Remarks on the advantages and disadvantages of each of the methods used
This unique book officially defines Microbiome Statistics as a specific new field of statistics, and addresses the statistical analysis of correlation, association, interaction and composition in microbiome research. This book is useful for researchers of biostatistics and data analysts.
Preface Acknowledgement About the Authors
1. Introduction to Microbiome Statistics
2. Classical Parametric Correlation
3. Classical Nonparametric Correlation
4. Composition Barplot
5. Composition Heatmap
6. Correlation Heatmap and plot
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
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.
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