Statistical Visual And Functional Analysis Of Microbiome Data
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Statistical Visual And Functional Analysis Of Microbiome Data
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Author : Achal Dhariwal
language : en
Publisher:
Release Date : 2018
Statistical Visual And Functional Analysis Of Microbiome Data written by Achal Dhariwal and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"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. " --
Statistical Analysis Of Microbiome Data
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Author : Somnath Datta
language : en
Publisher: Springer Nature
Release Date : 2021-10-27
Statistical Analysis Of Microbiome Data written by Somnath Datta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-27 with Medical categories.
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.
Applied Microbiome Statistics
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Author : Yinglin Xia
language : en
Publisher: CRC Press
Release Date : 2024-07-22
Applied Microbiome Statistics written by Yinglin Xia and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Mathematics categories.
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.
Statistical Methods For Longitudinal Data Analysis And Reproducible Feature Selection In Human Microbiome Studies
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Author : Lingjing Jiang
language : en
Publisher:
Release Date : 2020
Statistical Methods For Longitudinal Data Analysis And Reproducible Feature Selection In Human Microbiome Studies written by Lingjing Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
The microbiome is inherently dynamic, driven by interactions among microbes, with the host, and with the environment. At any point in life, human microbiome can be dramatically altered, either transiently or long term, by diseases, medical interventions or even daily routines. Since the human microbiome is highly dynamic and personalized, longitudinal microbiome studies that sample human-associated microbial communities repeatedly over time provide valuable information for researchers to observe both inter- and intra-individual variability, or to measure changes in response to an intervention in real time. Despite this increasing need in longitudinal data analysis, statistical methods for analyzing sparse longitudinal microbiome data and longitudinal multi-omics data still lag behind. In this dissertation, we describe our efforts in developing two novel statistical methods, Bayesian functional principal components analysis (SFPCA) for sparse longitudinal data analysis, and multivariate sparse functional principal components analysis (mSFPCA) for longitudinal microbiome multi-omics data analysis. Beyond longitudinal data analysis, we are also interested in utilizing statistical techniques for addressing the "reproducibility crisis" in microbiome research, especially in the indispensable task of feature selection. Instead of developing "the best" feature selection method, we focus on discovering a reproducible criterion called Stability for evaluating feature selection methods in order to yield reproducible results in microbiome analysis. To set an appropriate motivation and context for our work, Chapter 1 reviews the importance of longitudinal studies in human microbiome research, and presents the crucial need of developing novel statistical methods to meet the new challenges in longitudinal microbiome data analysis, and of producing reproducible results in microbiome feature selection. Chapter 2 introduces Bayesian SFPCA, a flexible Bayesian approach to SFPCA that enables efficient model selection and graphical model diagnostics for valid longitudinal microbiome applications. Chapter 3 presents mSFPCA, an extension of Bayesian SFPCA from modeling a univariate temporal outcome to simultaneously characterizing multiple temporal measurements, and inferring their temporal associations based on mutual information estimation. Chapter 4 proposes to use reproducibility criterion such as Stability instead of popular model prediction metric such as mean squared error (MSE) to quantify the reproducibility of identified microbial features.
Statistical And Computational Methods For Microbiome Multi Omics Data
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Author : Himel Mallick
language : en
Publisher: Frontiers Media SA
Release Date : 2020-11-19
Statistical And Computational Methods For Microbiome Multi Omics Data written by Himel Mallick and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-19 with Science categories.
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.
Bioinformatic And Statistical Analysis Of Microbiome Data
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Author : Yinglin Xia
language : en
Publisher: Springer Nature
Release Date : 2023-06-16
Bioinformatic And Statistical Analysis Of Microbiome Data written by Yinglin Xia and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-16 with Science categories.
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.
Computational Methods For Microbiome Analysis
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Author : Joao Carlos Setubal
language : en
Publisher: Frontiers Media SA
Release Date : 2021-02-02
Computational Methods For Microbiome Analysis written by Joao Carlos Setubal and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-02 with Science categories.
Statistical Analysis Of Microbiome Data With R
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Author : Yinglin Xia
language : en
Publisher: Springer
Release Date : 2018-10-06
Statistical Analysis Of Microbiome Data With R written by Yinglin Xia and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-06 with Computers categories.
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.
Statistical Tools For The Multi Omics Analysis Of Microbiome Data
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Author : Angela Zhang
language : en
Publisher:
Release Date : 2022
Statistical Tools For The Multi Omics Analysis Of Microbiome Data written by Angela Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
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.
Statistical Methods For The Analysis Of Microbiome Data
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Author : Anna M. Plantinga
language : en
Publisher:
Release Date : 2018
Statistical Methods For The Analysis Of Microbiome Data written by Anna M. Plantinga and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.