Applied Microbiome Statistics: Correlation, Association, Interaction and Composition
Preface Acknowledgements 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
Machine Learning for Microbiome Statistics
Preface Acknowledgements About the Authors Chapter 1 Introduction to Machine Learning Chapter 2 Overview of Machine Learning in Microbiome Research Chapter 3 Accessing Model Accuracy and Goodness of Fit Tests for Normality Chapter 4 Overfitting and Underfitting Chapter 5 Assessing Model Accuracy Using Cross-Validation Chapter 6 Feature Engineering and Model Selection Chapter 7 Logistic Regression Chapter 8 Support Vector Machines Chapter 9 Classification Trees Chapter 10 Random Forest Chapter 11 The Evolution of Tree-Based Algorithms Chapter 12 Extreme Gradient Boosting (XGBoost) Chapter 13 Artificial Neural Networks and Deep Learning Chapter 14 Machine Learning Microbiome with SIAMCAT Chapter 15 Basic Performance Metrics for Machine Learning Models Chapter 16 Matthews Correlation Coefficient Chapter 17 Area Under the Receiver Operating Characteristic Curve (AUC-ROC) Chapter 18 Area Under the Precision-Recall Curve (AUC-PR) Chapter 19 Comparisons of Machine Learning Classification Models with Tidymodels
Biography
Dr. Yinglin Xia is a Clinical Professor in the Department of Medicine at the University of Illinois Chicago. He has published six books on statistical analysis of microbiome and metabolomics data and more than 180 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.
Dr. Jun Sun is a tenured Professor of Medicine at the University of Illinois Chicago and an internationally recognized expert on microbiome and human diseases, e.g., vitamin D receptor in inflammation, dysbiosis and intestinal dysfunction in amyotrophic lateral sclerosis (ALS). Her lab is the first to discover that chronic effects and molecular mechanisms of Salmonella infection and risk of colon cancer. Dr. Sun has published over 260 scientific articles in peer-reviewed journals and 10 books on microbiome.
