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Author: Sven Kleine Bardenhorst
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Sven Kleine Bardenhorst
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author: Somnath Datta
Publisher: Springer Nature
ISBN: 3030733513
Category : Medical
Languages : en
Pages : 349
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Book Description
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: 0841299161
Category : Science
Languages : en
Pages : 229
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Book Description
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: Yuanpei Cao
Publisher:
ISBN:
Category :
Languages : en
Pages : 202
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Next generation sequencing (NGS) technologies make the studies of microbiomes in very large-scale possible without cultivation in vitro. One approach to sequencing-based microbiome studies is to sequence specific genes (often the 16S rRNA gene) to produce a profile of diversity of bacterial taxa. Alternatively, the NGS-based sequencing strategy, also called shotgun metagenomics, provides further insights at the molecular level, such as species/strain quantification, gene function analysis and association studies. Such studies generate large-scale high-dimensional count and compositional data, which are the focus of this dissertation.
Author: Neal Steven Grantham
Publisher:
ISBN:
Category :
Languages : en
Pages : 90
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Author: Yinglin Xia
Publisher: CRC Press
ISBN: 1040045669
Category : Mathematics
Languages : en
Pages : 457
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Book Description
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: Gibraan Rahman
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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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: Anna M. Plantinga
Publisher:
ISBN:
Category :
Languages : en
Pages : 128
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Book Description
The human microbiome plays a vital role in maintaining health, and imbalances in the microbiome are associated with a wide variety of diseases. Understanding whether and how the microbiome is associated with particular health conditions is a focus of many modern microbiome studies, with the hope that a deeper understanding of these associations may lead to more effective prevention and treatment regimens. However, how best to analyze data from microbiome profiling studies remains unclear. The high dimensionality, compositional nature, intrinsic biological structure, and limited availability of samples pose substantial statistical challenges. To face these challenges, we propose novel analytic approaches based on sparse penalized regression strategies and distance-based global association analysis. Most distance-based methods for global microbiome association analysis are restricted to simple dichotomous or quantitative outcomes, but more complex outcomes are increasingly common in microbiome studies. In the first part of this dissertation, we introduce two distance-based methods for the analysis of entire microbial communities in modern microbiome studies. We develop a kernel machine regression-based score test for association between the microbiome and censored time-to-event outcomes. We then propose a novel longitudinal measure of dissimilarity that summarizes changes in the microbiome across time and compares these changes between subjects. Since this dissimilarity may be incorporated into any distance-based analysis framework, it is a highly flexible tool for applying a wide variety of distance-based analyses in longitudinal studies. Identification of associated taxa and detection of predictive microbial signatures are key to translation of microbiome studies. In the second part of this dissertation, we present two penalized regression methods for estimation and prediction with high-dimensional compositional data. Because phylogenetic similarity between bacteria often corresponds to shared functions, our first contribution is to incorporate phylogenetic structure into a penalized regression model for constrained data. We then propose a model that exploits phylogenetic structure to use partial information in the setting of differing feature sets between model-building and prediction datasets. We evaluate the performance of these methods through extensive simulation studies and apply them to studies investigating the association of graft-versus-host disease or body mass index with the gut microbiome.
Author: Jessica Galloway-Peña
Publisher: Frontiers Media SA
ISBN: 2889456536
Category :
Languages : en
Pages : 186
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High-throughput sequencing technologies are widely used to study microbial ecology across species and habitats in order to understand the impacts of microbial communities on host health, metabolism, and the environment. Due to the dynamic nature of microbial communities, longitudinal microbiome analyses play an essential role in these types of investigations. Key questions in microbiome studies aim at identifying specific microbial taxa, enterotypes, genes, or metabolites associated with specific outcomes, as well as potential factors that influence microbial communities. However, the characteristics of microbiome data, such as sparsity and skewedness, combined with the nature of data collection, reflected often as uneven sampling or missing data, make commonly employed statistical approaches to handle repeated measures in longitudinal studies inadequate. Therefore, many researchers have begun to investigate methods that could improve incorporating these features when studying clinical, host, metabolic, or environmental associations with longitudinal microbiome data. In addition to the inferential aspect, it is also becoming apparent that visualization of high dimensional data in a way which is both intelligible and comprehensive is another difficult challenge that microbiome researchers face. Visualization is crucial in both the analysis and understanding of metagenomic data. Researchers must create clear graphic representations that give biological insight without being overly complicated. Thus, this Research Topic seeks to both review and provide novels approaches that are being developed to integrate microbiome data and complex metadata into meaningful mathematical, statistical and computational models. We believe this topic is fundamental to understanding the importance of microbial communities and provides a useful reference for other investigators approaching the field.
Author: Himel Mallick
Publisher: Frontiers Media SA
ISBN: 2889660915
Category : Science
Languages : en
Pages : 170
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Book Description
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.
