TY - JOUR
T1 - A Survey of Statistical Methods for Microbiome Data Analysis
AU - Lutz, Kevin C.
AU - Jiang, Shuang
AU - Neugent, Michael L.
AU - De Nisco, Nicole J.
AU - Zhan, Xiaowei
AU - Li, Qiwei
N1 - Publisher Copyright:
Copyright © 2022 Lutz, Jiang, Neugent, De Nisco, Zhan and Li.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.
AB - In the last decade, numerous statistical methods have been developed for analyzing microbiome data generated from high-throughput next-generation sequencing technology. Microbiome data are typically characterized by zero inflation, overdispersion, high dimensionality, and sample heterogeneity. Three popular areas of interest in microbiome research requiring statistical methods that can account for the characterizations of microbiome data include detecting differentially abundant taxa across phenotype groups, identifying associations between the microbiome and covariates, and constructing microbiome networks to characterize ecological associations of microbes. These three areas are referred to as differential abundance analysis, integrative analysis, and network analysis, respectively. In this review, we highlight available statistical methods for differential abundance analysis, integrative analysis, and network analysis that have greatly advanced microbiome research. In addition, we discuss each method's motivation, modeling framework, and application.
KW - differential abundance analysis
KW - integrative analysis
KW - metagenomics data
KW - microbiome data
KW - network analysis
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U2 - 10.3389/fams.2022.884810
DO - 10.3389/fams.2022.884810
M3 - Review article
AN - SCOPUS:85133541881
SN - 2297-4687
VL - 8
JO - Frontiers in Applied Mathematics and Statistics
JF - Frontiers in Applied Mathematics and Statistics
M1 - 884810
ER -