Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

Here, the authors use simulated quantitative gut microbial communities to benchmark the performance of 13 common data transformations in determining diversity as well as microbe-microbe and microbe-metadata associations, finding that quantitative approaches incorporating microbial load variation out...

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Autores principales:	Verónica Lloréns-Rico, Sara Vieira-Silva, Pedro J. Gonçalves, Gwen Falony, Jeroen Raes
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e544f190337b4f3d9572dcc00f19fd3d
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spelling	oai:doaj.org-article:e544f190337b4f3d9572dcc00f19fd3d2021-12-02T14:59:27ZBenchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases10.1038/s41467-021-23821-62041-1723https://doaj.org/article/e544f190337b4f3d9572dcc00f19fd3d2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23821-6https://doaj.org/toc/2041-1723Here, the authors use simulated quantitative gut microbial communities to benchmark the performance of 13 common data transformations in determining diversity as well as microbe-microbe and microbe-metadata associations, finding that quantitative approaches incorporating microbial load variation outperform computational strategies in downstream analyses, urging for a widespread adoption of quantitative approaches, or recommending specific computational transformations whenever determination of microbial load of samples is not feasible.Verónica Lloréns-RicoSara Vieira-SilvaPedro J. GonçalvesGwen FalonyJeroen RaesNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Verónica Lloréns-Rico Sara Vieira-Silva Pedro J. Gonçalves Gwen Falony Jeroen Raes Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
description	Here, the authors use simulated quantitative gut microbial communities to benchmark the performance of 13 common data transformations in determining diversity as well as microbe-microbe and microbe-metadata associations, finding that quantitative approaches incorporating microbial load variation outperform computational strategies in downstream analyses, urging for a widespread adoption of quantitative approaches, or recommending specific computational transformations whenever determination of microbial load of samples is not feasible.
format	article
author	Verónica Lloréns-Rico Sara Vieira-Silva Pedro J. Gonçalves Gwen Falony Jeroen Raes
author_facet	Verónica Lloréns-Rico Sara Vieira-Silva Pedro J. Gonçalves Gwen Falony Jeroen Raes
author_sort	Verónica Lloréns-Rico
title	Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
title_short	Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
title_full	Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
title_fullStr	Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
title_full_unstemmed	Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
title_sort	benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e544f190337b4f3d9572dcc00f19fd3d
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Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

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