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; p. e6
Main Author Calle, M Luz
Format Journal Article
LanguageEnglish
Published Korea (South) Korea Genome Organization 01.03.2019
한국유전체학회
Subjects
biomarkers
DNA sequence analysis
metagenome
microbiota
Review
statistical models
자연과학일반
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.
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https://genominfo.org/journal/view.php?number=549
ISSN:1598-866X
2234-0742
2234-0742
DOI:10.5808/GI.2019.17.1.e6