[PDF] Statistical Analysis Of Microbiome Data With R eBook

Author : Yinglin Xia
Publisher : Springer
Page : 518 pages
File Size : 30,99 MB
Release : 2018-10-06
Category : Computers
ISBN : 9811315345

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This unique book addresses the statistical modelling and analysis of microbiome data using cutting-edge R software. It includes real-world data from the authors’ research and from the public domain, and discusses the implementation of R for data analysis step by step. The data 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. The book also discusses recent developments in statistical modelling and data analysis in microbiome research, as well as the latest advances in next-generation sequencing and big data in methodological development and applications. This timely book will greatly benefit all readers involved in microbiome, ecology and microarray data analyses, as well as other fields of research.

Author : Somnath Datta
Publisher : Springer Nature
Page : 349 pages
File Size : 47,21 MB
Release : 2021-10-27
Category : Medical
ISBN : 3030733513

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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.

Author : Yinglin Xia
Publisher : Springer Nature
Page : 717 pages
File Size : 28,11 MB
Release : 2023-06-16
Category : Science
ISBN : 3031213912

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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.

Author : Yinglin Xia
Publisher : American Chemical Society
Page : 229 pages
File Size : 22,81 MB
Release : 2022-02-03
Category : Science
ISBN : 0841299161

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Compared with other research fields, both microbiome and metabolomics data are complicated and have some unique characteristics, respectively. Thus, choosing an appropriate statistical test or method is a very important step in the analysis of microbiome and metabolomics data. However, this is still a difficult task for those biomedical researchers without a statistical background and for those biostatisticians who do not have research experiences in these fields. Graduate students studying microbiome and metabolomics; statisticians, working on microbiome and metabolomics projects, either for their own research, or for their collaborative research for experimental design, grant application, and data analysis; and researchers who investigate biomedical and biochemical projects with the microbiome, metabolome, and multi-omics data analysis will benefit from reading this work.

Author : Yinglin Xia
Publisher : CRC Press
Page : 457 pages
File Size : 18,52 MB
Release : 2024-07-22
Category : Mathematics
ISBN : 1040045669

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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. Presents a thorough overview of statistical methods in microbiome statistics of parametric and nonparametric correlation, association, interaction, and composition adopted from classical statistics and ecology and specifically designed for microbiome research. Performs step-by-step statistical analysis of correlation, association, interaction, and composition in microbiome data. Discusses the issues of statistical analysis of microbiome data: high dimensionality, compositionality, sparsity, overdispersion, zero-inflation, and heterogeneity. Investigates statistical methods on multiple comparisons and multiple hypothesis testing and applications to microbiome data. Introduces a series of exploratory tools to visualize composition and correlation of microbial taxa by barplot, heatmap, and correlation plot. Employs the Kruskal–Wallis rank-sum test to perform model selection for further multi-omics data integration. Offers R code and the datasets from the authors’ real microbiome research and publicly available data for the analysis used. Remarks on the advantages and disadvantages of each of the methods used.

Author : Mohsen Nady
Publisher :
Page : 0 pages
File Size : 46,74 MB
Release : 2023-12
Category :
ISBN : 9781774699027

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This book covers the necessary analysis steps for dealing with microbiome data. The microbial samples are sequenced into raw reads or fastq files. These raw reads should be quality checked to assure their quality per base or per read. Then, after removing the errors, we can infer their exact amplicon sequence variants (ASVs). After that, we taxonomically classify these sequences to represent the different taxonomy levels of species, genus, family, order, class, and phylum and generate a phylogenetic tree. Finally, the sequence data, the taxonomy table, phylogenetic tree, and sample data are combined in a single phyloseq object for ease of plotting, manipulation, and analysis of the different components of microbiome data. The final phyloseq object can also be cleaned for certain low prevalent sequences. All these steps are explained in R codes using a freely available microbiome data of 360 fecal samples.

Author : Weijia Fu
Publisher :
Page : 39 pages
File Size : 25,59 MB
Release : 2019
Category :
ISBN :

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Progress in high throughput sequencing has facilitated the conduct of large scale microbiome profiling studies which have already begun to elucidate the role of microbes in many disorders and clinical outcomes. Despite the many successes, statistical analysis of data from these studies continues to pose a challenge. In the thesis, we proposed methods to study two specific challenges: batch effects and integrative analysis of microbiome and other omics data. Both issues are increasingly relevant problems. As studies get larger, batching becomes inevitable and integrative analysis is imperative for gaining clues as to the mechanisms underlying discovered associations. The thesis is composed of two projects. In the first project, we compared six existing batch correction methods for microarray data when applied to microbiome data. Two real microbiome data sets were used to evaluate the performance using data visualization and several evaluation metrics. Our results suggest that an empirical bayes approach (ComBat), when applied appropriately, can outperform other methods. In the second project, we proposed a robust microbiome regression-based kernel association test (MiRKAT-R) to screen a large number of genomic markers for association with microbiome profiles. This approach utilizes a recently developed robust kernel machine test. We further propose to incorporate an omnibus test that simultaneously considers different models so as to allow for different relationships between the individual markers and microbiome composition. Systematic simulations and applications to real data show that the MiRKAT-R improves both type I error control and power.

Author : Yinglin Xia
Publisher : American Chemical Society
Page : 205 pages
File Size : 28,73 MB
Release : 2022-03-25
Category : Science
ISBN : 0841299544

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Because the microbial community is dynamic, an individual’s microbiota at a given time is varied, and many factors, including age, host genetics, diet, and the local environment, significantly change the microbiota. Thus, microbiome researchers have naturally expanded their research to look for insights into the interaction of the microbiome with other “omics”. Metabolites (small molecules) are the intermediate or end products of metabolism. Metabolites have various functions. The microbial-derived metabolites play an important role in the function of the microbiome. Thus, the advancement in microbiome studies is becoming particularly critical for the integration of microbial DNA sequencing data with other omics data, especially microbiome-metabolomics integration.

Author : Achal Dhariwal
Publisher :
Page : pages
File Size : 48,59 MB
Release : 2018
Category :
ISBN :

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"The advancements in next-generation sequencing technologies have revolutionized microbiome research by allowing culture-independent high-throughput profiling of the genetic contents of microbial communities. Nowadays, 16S rRNA based marker gene sequencing is widely used to characterize the taxonomic composition and phylogenetic diversity of complex microbial communities. However, statistical, visual and functional analysis of such data possess great challenges. In addition, many aspects of the current approaches can be improved to get a better understanding of communities. The proper analysis of the resulting large and complicated datasets remains a key bottleneck in current microbiome studies. Over the last decade, powerful computational pipelines and standard protocols have been developed to support efficient raw data processing and annotation of microbiome data. The focus has now shifted towards downstream statistical analysis and functional interpretation. To address this bottleneck, we have developed MicrobiomeAnalyst, a user-friendly web-based tool that incorporates recent progresses in statistics and interactive visualization techniques, coupled with novel knowledge bases, to facilitate comprehensive analysis of common data sets generated from microbiome studies. MicrobiomeAnalyst contains four major components, including i) a module for community diversity profiling, comparative analysis and functional prediction of 16S rRNA marker gene data; ii) a module for exploratory data analysis, functional profiling and metabolic network visualization for shotgun metagenomics or metatranscriptomics data; iii) a module to help users to interpret their taxa of interest via enrichment analysis against ~300 taxon sets manually collected from recent literature and public databases; and iv) a module to allow users to visually explore their data sets within the context of compatible public data (meta-analysis) for pattern discovery and biological insights. The tool is freely accessible at http://www.microbiomeanalyst.ca. " --