Genomics Inform.  2019 Mar;17(1):e6. 10.5808/GI.2019.17.1.e6.

Statistical Analysis of Metagenomics Data

• Calle ML

• KMID: 2447796
• DOI: http://doi.org/10.5808/GI.2019.17.1.e6

Abstract

Understanding the role of the microbiome in human health and how it can be modulated is becoming increasingly relevant for preventive medicine and for the medical management of chronic diseases. The development of high-throughput sequencing technologies has boosted microbiome research through the study of microbial genomes and allowing a more precise quantification of microbiome abundances and function. Microbiome data analysis is challenging because it involves high-dimensional structured multivariate sparse data and because of its compositional nature. In this review we outline some of the procedures that are most commonly used for microbiome analysis and that are implemented in R packages. We place particular emphasis on the compositional structure of microbiome data. We describe the principles of compositional data analysis and distinguish between standard methods and those that fit into compositional data analysis.

Keyword

biomarkers; DNA sequence analysis; metagenome; microbiota; statistical models

MeSH Terms

Biomarkers
Chronic Disease
Genome, Microbial
Humans
Metagenome
Metagenomics*
Microbiota
Models, Statistical
Preventive Medicine
Sequence Analysis, DNA
Statistics as Topic
Biomarkers