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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| Format: | Article |
| Language: | English |
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BioMed Central
2019-03-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2019-17-1-e6.pdf |
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| _version_ | 1832557977271271424 |
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| author | M. Luz Calle |
| author_facet | M. Luz Calle |
| author_sort | M. Luz Calle |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-93ce434b61ce4f089a5003bfded3b2c5 |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-93ce434b61ce4f089a5003bfded3b2c52025-02-03T01:34:00ZengBioMed CentralGenomics & Informatics2234-07422019-03-0117110.5808/GI.2019.17.1.e6549Statistical Analysis of Metagenomics DataM. Luz CalleUnderstanding 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.http://genominfo.org/upload/pdf/gi-2019-17-1-e6.pdfbiomarkersDNA sequence analysismetagenomemicrobiotastatistical models |
| spellingShingle | M. Luz Calle Statistical Analysis of Metagenomics Data Genomics & Informatics biomarkers DNA sequence analysis metagenome microbiota statistical models |
| title | Statistical Analysis of Metagenomics Data |
| title_full | Statistical Analysis of Metagenomics Data |
| title_fullStr | Statistical Analysis of Metagenomics Data |
| title_full_unstemmed | Statistical Analysis of Metagenomics Data |
| title_short | Statistical Analysis of Metagenomics Data |
| title_sort | statistical analysis of metagenomics data |
| topic | biomarkers DNA sequence analysis metagenome microbiota statistical models |
| url | http://genominfo.org/upload/pdf/gi-2019-17-1-e6.pdf |
| work_keys_str_mv | AT mluzcalle statisticalanalysisofmetagenomicsdata |