Statistical analysis of microbiome data / Somnath Datta, Subharup Guha, editors.
Datta, Somnath, 1962- editor.; Guha, Subharup, editor.
2021
QR60 .S73 2021
Available Online

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Statistical analysis of microbiome data / Somnath Datta, Subharup Guha, editors.
ISBN
9783030733513 (electronic bk.)
3030733513 (electronic bk.)
9783030733506
3030733505
DOI
https://doi.org/10.1007/978-3-030-73351-3
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (some color)
Item Number
10.1007/978-3-030-73351-3 doi
Call Number
QR60 .S73 2021
Dewey Decimal Classification
579.01/5
Summary
Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
Note
Includes index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed November 12, 2021).
Added Author
Datta, Somnath, 1962- editor.
Guha, Subharup, editor.
Series
Frontiers in probability and the statistical sciences. 2624-9995
Available in Other Form
Print version: 9783030733506
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
1. Tree-guided regression and multivariate analysis of microbiome data
Hongu Zhao and Tao Wang
2. Computational methods for metagenomic assemblies and strain identification
Hongzhe Li
3. Graphical models for microbiome data
Ali Shojaie
4. Bayesian models for understanding the modulating factors of microbiome data
Francesco Denti, Matthew D. Koslovsky, Michele Guindani, Marina Vannucci, and Katrine L. Whiteson
5. Use of variable importance in microbiome studies
Hemant Ishwaran
6. Log-linear models for microbiome data
Glen Satten
7. Quantification of amplicon sequences in microbiome samples using statistical methods
Karin Dorman
8. TBD
Jeanine Houwing Duistermaat
9. Analyzing microbiome data by employing the power of abundance ratios
Zhigang Li
10. Beta diversity analysis
Michael Wu
11. MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations
Fengzhu Sun
12. Statistical methods for feature selection in microbiome studies
Peng Liu
13. A Bayesian restoration of the duality between principal components of a distance matrix and operational taxonomic units in microbiome analyses
Somnath Datta and Subharup Guha.

Browse Subjects

Show more subjects...

Statistics

from
to
Export