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 the study of the microbiome as a hypothesis-driven experimental science and describes two microbiome research themes and six unique characteristics of microbiome data, as well as investigating challenges for statistical analysis of microbiome data using the standard statistical methods. This book is useful for researchers of biostatistics, ecology, and data analysts.
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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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