Overview
- Advances in microbiome research methods
- Statistical modeling of microbiome data
- Workflow for microbiome data analysis
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Table of contents (19 chapters)
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Front Matter
Keywords
- Microbiome
- Bioinformatics
- Biostatistics
- Data analysis microbiome
- microbiome bioinformatics
- microbiome biostatistics
- Bioinformatics Linux
- Bioinformatics Unix
- RStudio bioinformatics
- RStudio biostatistics
- r bioinformatics
- r biostatistics
- Bioinformatic Next-Generation Sequencing
- Bioinformatic Analysis Metagenomics
- Alpha Diversity biostatistics
- Beta Diversity biostatistics
- Differential Abundance Analysis microbiome
- Compositional Analysis Microbiome Data
- Longitudinal Data Analysis Microbiome
- Meta-analysis Microbiome Data
About this book
This unique book addresses the bioinformatic and statistical modelling and also the analysis of microbiome data using cutting-edge QIIME 2 and R software. It covers core analysis topics in both bioinformatics and statistics, which provides a complete workflow for microbiome data analysis: from raw sequencing reads to community analysis and statistical hypothesis testing. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of QIIME 2 and R for data analysis step-by-step. The data as well as QIIME 2 and R computer programs are publicly available, allowing readers to replicate the model development and data analysis presented in each chapter so that these new methods can be readily applied in their own research.
Bioinformatic and Statistical Analysis of Microbiome Data is an ideal book for advanced graduate students and researchers in the clinical, biomedical, agricultural, and environmental fields, as well as those studying bioinformatics, statistics, and big data analysis.
Authors and Affiliations
About the authors
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 AssociateEditor for Gut Microbes. She serves on the study sections for the national and international research foundations.
Bibliographic Information
Book Title: Bioinformatic and Statistical Analysis of Microbiome Data
Book Subtitle: From Raw Sequences to Advanced Modeling with QIIME 2 and R
Authors: Yinglin Xia, Jun Sun
DOI: https://doi.org/10.1007/978-3-031-21391-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-21390-8Published: 16 May 2023
Softcover ISBN: 978-3-031-21393-9Due: 09 October 2023
eBook ISBN: 978-3-031-21391-5Published: 15 May 2023
Edition Number: 1
Number of Pages: XXVI, 703
Number of Illustrations: 16 b/w illustrations, 59 illustrations in colour
Topics: Biostatistics, Statistics for Life Sciences, Medicine, Health Sciences, Big Data, Statistics and Computing/Statistics Programs, Biomedical Engineering/Biotechnology, Biomedical Engineering and Bioengineering