Bioinformatic and Statistical Analysis of Microbiome Data

From Raw Sequences to Advanced Modeling with QIIME 2 and R

AvYinglin Xia,Jun Sun

Häftad, Engelska, 2024

2 117 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-05-16
Mått:155 x 235 x 40 mm
Vikt:1 089 g
Format:Häftad
Språk:Engelska
Antal sidor:703
Förlag:Springer International Publishing AG
ISBN:9783031213939

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik
Biovetenskap inom Naturvetenskap och teknik
Affärsapplikationer inom Data och IT

Mer om författaren

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.

Innehållsförteckning

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).