
Applied Microbiome Statistics
Description
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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.
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Persons
Jun Sun is a tenured professor of medicine at the University of Illinois Chicago (UIC). She is an internationally recognized expert on microbiome and human diseases, such as vitamin D receptor in inflammation, dysbiosis, and intestinal dysfunction in amyotrophic lateral sclerosis (ALS). Her lab was the first to discover the chronic effects and molecular mechanisms of Salmonella infection and development of colon cancer. Dr. Sun has published over 220 scientific articles in peer-reviewed journals and nine books on the microbiome.
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