1.

Record Nr.

UNISA996547956503316

Autore

Xia Yinglin

Titolo

Bioinformatic and Statistical Analysis of Microbiome Data [[electronic resource] ] : From Raw Sequences to Advanced Modeling with QIIME 2 and R / / by Yinglin Xia, Jun Sun

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023

ISBN

9783031213915

9783031213908

Edizione

[1st ed. 2023.]

Descrizione fisica

1 online resource (716 pages)

Disciplina

576

Soggetti

Bioinformatics

Biometry

Big data

Mathematical statistics - Data processing

Biotechnology

Biomedical engineering

Biostatistics

Big Data

Statistics and Computing

Biomedical Engineering and Bioengineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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).

Sommario/riassunto

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.