Titlebook: Bioinformatic and Statistical Analysis of Microbiome Data; From Raw Sequences t Yinglin Xia,Jun Sun Book 2023 Springer Nature Switzerland A

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Clustering Sequences into OTUs,minary procedures of clustering sequences into OTUs. Then it describes VSEARCH and q2-vsearch. Next three sections introduce and illustrate three approaches of clustering sequences into OTUs using q2-vsearch: closed-reference clustering, .?clustering, and open-reference clustering.

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OTU Methods in Numerical Taxonomy,ntroduces the principles and aims of numerical taxonomy. Third, it briefly describes the philosophy of numerical taxonomy. Fourth, it introduces the formation and characteristics of commonly clustering-based OTU methods, which have large impact on microbiome study in both concept and methodology. Fi

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Moving Beyond OTU Methods,clustering-based OTU methods and the purposes of using OTUs and definitions of species and species-level analysis in microbiome studies. Then, it introduces the OTU-based methods that move toward single-nucleotide resolution. Third, it describes moving beyond the OTU methods. Finally, it discusses t

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Alpha Diversity,y indicate that an environment of human body has undergone a change (e.g., from a healthy to a disease condition), or the environment is disrupted by some factors (e.g., antibiotic use or a change of immune response). Thus, one task of microbiome community analysis is to analyze diversity of the gut

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Beta Diversity Metrics and Ordination,iversity measures the difference between two samples or communities. Beta diversity analysis requires a distance or dissimilarity measure matrix as input. This chapter first introduces abundance-based and phylogenetic beta diversity metrics, respectively; then introduces ordination methods and ordin

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Linear Mixed-Effects Models for Longitudinal Microbiome Data, Univariate analysis analyzes the change of one taxon or alpha diversity over time. Multivariate analysis directly analyzes the change of multiple taxa simultaneously or distance/dissimilarity (beta diversities) over time. This chapter introduces using the classical univariate linear mixed-effects m

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Introduction to Generalized Linear Mixed Models, an extension of linear mixed models to allow response variables from different distributions, such as binary responses. First, it reviews the brief history of generalized linear models (GLMs) and generalized nonlinear models (GNLMs). Then it describes the generalized linear mixed models (GLMMs). Ne