Statistical analysis of metagenomics data

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 st...

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Bibliographic Details
Published inGenomics & informatics Vol. 17; no. 1; pp. 6.1 - 6.9
Main Author Calle, M. Luz
Format Journal Article
LanguageKorean
Published 2019
Subjects
microbiota
DNA sequence analysis
metagenome
biomarkers
statistical models
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Summary: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.
Bibliography:KISTI1.1003/JNL.JAKO201924155912569
ISSN:1598-866X
2234-0742