Bioinformatic and Statistical Analysis of Microbiome Data

Dr. Yinglin Xia is a Research Professor in the Department of Medicine at the University of Illinois Chicago (UIC). He was a Research Assistant Professor in the Department of Biostatistics and Computational Biology at the University of Rochester (Rochester, NY) and Clinical Statistician in AbbVie (North Chicago, IL) before joining UIC as a Research Associate Professor in 2015. Dr. Xia has published more than 140 statistical methodology and research papers in peer-reviewed journals. He serves on the editorial board for several scientific journals including as an Associate Editor of Gut Microbes and has served as a reviewer for over 100 scientific journals. He  is the lead authors of Statistical Analysis of Microbiome Data with R (Springer Nature, 2018), which was the first statistics book in microbiome study,  Statistical Data Analysis of Microbiomes and Metabolomics(American Chemical Society, 2022) and An Integrated Analysis of Microbiomes and Metabolomics (American Chemical Society, 2022).   Dr. Jun Sun is a tenured Professor of Medicine at the University of Illinois Chicago. She is an elected fellow of the American Gastroenterological Association (AGA) and American Physiological Society (APS). She chairs the AGA Microbiome and Microbial Therapy section. 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 is the first to discover chronic effects and molecular mechanisms of Salmonella infection and development of colon cancer. Dr. Sun has published over 210 scientific articles in peer-reviewed journals and 8 books on microbiome. She is on the editorial boards of more than 10 peer-reviewed international scientific journals, including a Deputy Editor for American Journal of Physiology-GIL, an Associate Editor for Gut Microbes. She serves on the study sections for the national and international research foundations. 

Chapter 1. Introduction to Linux and Unix.- Chapter 2. Introduction to R, Rstudio.- Chapter 3. Bioinformatic Analysis of Next-Generation Sequencing.- Chapter 4. Bioinformatic Analysis of Metagenomics.- Chapter 5. Alpha Diversity.- Chapter 6. Beta Diversity.- Chapter 7. Differential Abundance Analysis.- Chapter 8. Analyzing Zero-Inflated Microbiome Data.- Chapter 9. Compositional Analysis of Microbiome Data.- Chapter 10. Longitudinal Data Analysis of Microbiome.- Chapter 11. Meta-analysis of Microbiome Data (optional).