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.
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Part I Preprocessing and Bioinformatics Pipelines |
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Denoising Methods for Inferring Microbiome Community Content and Abundance |
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3 | (24) |
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Statistical and Computational Methods for Analysis of Shotgun Metagenomics Sequencing Data |
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27 | (18) |
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Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics |
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45 | (36) |
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Part II Exploratory Analyses of Microbial Communities |
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Statistical Methods for Pairwise Comparison of Metagenomic Samples |
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81 | (20) |
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Beta Diversity and Distance-Based Analysis of Microbiome Data |
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101 | (30) |
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Part III Statistical Models and Inference |
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Joint Models for Repeatedly Measured Compositional and Normally Distributed Outcomes |
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131 | (44) |
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Jeanine Houwing-Duistermaat |
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Statistical Methods for Feature Identification in Microbiome Studies |
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175 | (18) |
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Statistical Methods for Analyzing Tree-Structured Microbiome Data |
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193 | (28) |
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A Log-Linear Model for Inference on Bias in Microbiome Studies |
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221 | (28) |
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Dirichlet-Multinomial Regression Models with Bayesian Variable Selection for Microbiome Data |
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249 | (22) |
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A Bayesian Approach to Restoring the Duality Between Principal Components of a Distance Matrix and Operational Taxonomic Units in Microbiome Analyses |
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271 | (24) |
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Tree Variable Selection for Paired Case-Control Studies with Application to Microbiome Data |
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295 | (16) |
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Networks for Compositional Data |
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311 | (26) |
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| Index |
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337 | |
Somnath Datta is Professor of Biostatistics and a preeminence hire in Genomic Medicine at the University of Florida. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics.
Subharup Guha is Associate Professor of Biostatistics at the University of Florida. His current research areas of interest are Bayesian nonparametric methods, clustering, classification, Markov chain Monte Carlo algorithms, causal inferences, and high-dimensional data analysis. The applications have included cancer genomics, image processing, microbiomics, and connectomics.
