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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.
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.
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.
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.
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.
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.
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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.
The Food Forum convened a public workshop on February 22-23, 2012, to explore current and emerging knowledge of the human microbiome, its role in human health, its interaction with the diet, and the translation of new research findings into tools and products that improve the nutritional quality of the food supply. The Human Microbiome, Diet, and Health: Workshop Summary summarizes the presentations and discussions that took place during the workshop. Over the two day workshop, several themes covered included: The microbiome is integral to human physiology, health, and disease. The microbiome is arguably the most intimate connection that humans have with their external environment, mostly through diet. Given the emerging nature of research on the microbiome, some important methodology issues might still have to be resolved with respect to undersampling and a lack of causal and mechanistic studies. Dietary interventions intended to have an impact on host biology via their impact on the microbiome are being developed, and the market for these products is seeing tremendous success. However, the current regulatory framework poses challenges to industry interest and investment.