Statistical Data Analysis of Microbiomes and Metabolomics

Abstract

Compared with other research fields, both microbiome and metabolomics data are complicated and have some unique characteristics, respectively. Thus, choosing an appropriate statistical test or method is a very important step in the analysis of microbiome and metabolomics data. However, this is still a difficult task for those biomedical researchers without a statistical background and for those biostatisticians who do not have research experiences in these fields. Statistical Data Analysis of Microbiomes and Metabolomics focuses on data analysis, statistical methods, and models. The general goal of this primer is to provide our readers with: The challenges of analyzing microbiome and metabolomics data using the standard models and methods. The new specifically designed methods and models developed to target the unique characteristics of microbiome data. The strengths and weaknesses of the newly developed methods and models. A comparison of the same categories of methods, based on their nature and capabilities, including whether the methods fit different types of data. Explanations for whether the tested methods and used models with their assumptions and attributes are amenable to the tested data. References to real studies to illustrate each of the important methods and models. Graduate students studying microbiome and metabolomics; statisticians, working on microbiome and metabolomics projects, either for their own research, or for their collaborative research for experimental design, grant application, and data analysis; and researchers who investigate biomedical and biochemical projects with the microbiome, metabolome, and multi-omics data analysis will benefit from reading this work.