Statistical Data Analysis Of Microbiomes And Metabolomics

Author : Yinglin Xia
Publisher : American Chemical Society
ISBN 13 : 0841299161
Total Pages : 229 pages
Book Rating : 4.60/5 ( download)

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Download or read book Statistical Data Analysis of Microbiomes and Metabolomics written by Yinglin Xia and published by American Chemical Society. This book was released on 2022-02-03 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Somnath Datta
Publisher : Springer Nature
ISBN 13 : 3030733513
Total Pages : 349 pages
Book Rating : 4.13/5 ( download)

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Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : American Chemical Society
ISBN 13 : 0841299544
Total Pages : 205 pages
Book Rating : 4.42/5 ( download)

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Download or read book An Integrated Analysis of Microbiomes and Metabolomics written by Yinglin Xia and published by American Chemical Society. This book was released on 2022-03-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Yinglin Xia
Publisher : CRC Press
ISBN 13 : 1040045669
Total Pages : 457 pages
Book Rating : 4.64/5 ( download)

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Download or read book Applied Microbiome Statistics written by Yinglin Xia and published by CRC Press. This book was released on 2024-07-22 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Himel Mallick
Publisher : Frontiers Media SA
ISBN 13 : 2889660915
Total Pages : 170 pages
Book Rating : 4.19/5 ( download)

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Download or read book Statistical and Computational Methods for Microbiome Multi-Omics Data written by Himel Mallick and published by Frontiers Media SA. This book was released on 2020-11-19 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

Author : Yinglin Xia
Publisher : Springer Nature
ISBN 13 : 3031213912
Total Pages : 717 pages
Book Rating : 4.15/5 ( download)

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Download or read book Bioinformatic and Statistical Analysis of Microbiome Data written by Yinglin Xia and published by Springer Nature. This book was released on 2023-06-16 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Springer
ISBN 13 : 9811315345
Total Pages : 505 pages
Book Rating : 4.43/5 ( download)

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Download or read book Statistical Analysis of Microbiome Data with R written by Yinglin Xia and published by Springer. This book was released on 2018-10-06 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : Gibraan Rahman
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.49/5 ( download)

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Download or read book Computational and Statistical Methods for Extracting Biological Signal from High-Dimensional Microbiome Data written by Gibraan Rahman and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next-generation sequencing (NGS) has effected an explosion of research into the relationship between genetic information and a variety of biological conditions. One of the most exciting areas of study is how the trillions of microbial species that we share this Earth with affect our health. However, the process of extracting useful biological insights from this breadth of data is far from trivial. There are numerous statistical and computational considerations in addition to the already complex and messy biological problems. In this thesis, I describe my work on developing and implementing software to tackle the complex world of statistical microbiome analysis. In the first part of this thesis, we review the applications and challenges of performing dimensionality reduction on microbiome data comprising thousands of microbial taxa. When dealing with this high dimensionality, it is imperative to be able to get an overview of the community structure in a lower dimensional space that can be both visualized and interpreted. We review the statistical considerations for dimensionality reduction and the existing tools and algorithms that can and cannot address them. This includes discussions about sparsity, compositionality, and phylogenetic signal. We also make recommendations about tools and algorithms to consider for different use-cases. In the second part of this thesis, we present a new software, Evident, designed to assist researchers with statistical analysis of microbiome effect sizes and power analysis. Effect sizes of statistical tests are not widely reported in microbiome datasets, limiting the interpretability of community differences such as alpha and beta diversity. As more large microbiome studies are produced, researchers have the opportunity to mine existing datasets to get a sense of the effect size for different biological conditions. These, in turn, can be used to perform power analysis prior to designing an experiment, allowing researchers to better allocate resources. We show how Evident is scalable to dozens of datasets and provides easy calculation and exploration of effect sizes and power analysis from existing data. In the third part of this thesis, we describe a novel investigation into the joint microbiome and metabolome axis in colorectal cancer. In most cases of sporadic colorectal cancers (CRC), tumorigenesis is a multistep process driven by genomic alterations in concert with dietary influences. In addition, mounting evidence has implicated the gut microbiome as an effector in the development and progression of CRC. While large meta-analyses have provided mechanistic insight into disease progression in CRC patients, study heterogeneity has limited causal associations. To address this limitation, multi-omics studies on genetically controlled cohorts of mice were performed to distinguish genetic and dietary influences. Diet was identified as the major driver of microbial and metabolomic differences, with reductions in alpha diversity and widespread changes in cecal metabolites seen in HFD-fed mice. Similarly, the levels of non-classic amino acid conjugated forms of the bile acid cholic acid (AA-CAs) increased with HFD. We show that these AA-CAs signal through the nuclear receptor FXR and membrane receptor TGR5 to functionally impact intestinal stem cell growth. In addition, the poor intestinal permeability of these AA-CAs supports their localization in the gut. Moreover, two cryptic microbial strains, Ileibacterium valens and Ruminococcus gnavus, were shown to have the capacity to synthesize these AA-CAs. This multi-omics dataset from CRC mouse models supports diet-induced shifts in the microbiome and metabolome in disease progression with potential utility in directing future diagnostic and therapeutic developments. In the fourth chapter, we demonstrate a new framework for performing differential abundance analysis using customized statistical modeling. As we learn more and more about the relationship between the microbiome and biological conditions, experimental protocols are becoming more and more complex. For example, meta-analyses, interventions, longitudinal studies, etc. are being used to better understand the dynamic nature of the microbiome. However, statistical methods to analyze these relationships are lacking--especially in the field of differential abundance. Finding biomarkers associated with conditions of interest must be performed with statistical care when dealing with these kinds of experimental designs. We present BIRDMAn, a software package integrating probabilistic programming with Stan to build custom models for analyzing microbiome data. We show that, on both simulated and real datasets, BIRDMAn is able to extract novel biological signals that are missed by existing methods. These chapters, taken together, advance our knowledge of statistical analysis of microbiome data and provide tools and references for researchers looking to perform analysis on their own data.

Author : Angela Zhang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.47/5 ( download)

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Download or read book Statistical Tools for the Multi-omics Analysis of Microbiome Data written by Angela Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The human microbiome consists of trillions of bacteria, archaea, and viruses that exist on virtually every organ in the body. The microbiome plays a fundamental role in human health and has been implicated in several different diseases and conditions such as cardiovascular disease and certain cancers. Understanding the functional role of the microbiome can lead to increased understanding of these complex diseases and result in the development of more effective treatments. Although advances in technology have allowed for the inexpensive processing and analysis of high-throughput data, several statistical challenges exist in the analysis of microbiome data. In my dissertation, I will present three projects that address the statistical challenges of high-dimensionality, multi-omics data integration, batch effects/other covariate adjustment, and the visualization of microbiome data. In Project 1, We address the issues of high-dimensionality and data integration by proposing a new procedure for testing the cumulative metabolic effect of the microbiome using a weighted variance component test framework. In this setup, we focus on metabolic pathways and recognize that metabolism can be represented by metagenomics (metabolic potential) and metabolomics (metabolic output). In Project 2, we address the issue of batch effects and high-dimensionality by outlining a two-step adjustment of the principal coordinates (PCs) of the microbial taxa data. In the first step, we project the mean effect of the unwanted covariates out of the PCs. In the second step, we adjust out the second moment of the same covariates from the PCs by assuming a linear relationship between the covariates and the variance of the PCs. Finally, in Project 3, we propose an effect modification testing procedure for evaluating interactions between microbial taxa and environmental factors on an outcome of interest. We address concerns of data integration and high-dimensionality by using a variance component test framework with LASSO-selected variables to assess the effect modification of the microbiome on environmental variables.

Author : Rachael E. Antwis
Publisher :
ISBN 13 : 9781108654418
Total Pages : pages
Book Rating : 4.1X/5 ( download)

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Download or read book Microbiomes of Soils, Plants and Animals written by Rachael E. Antwis and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A comparative, holistic synthesis of microbiome research, spanning soil, plant, animal and human hosts.