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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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