Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.

The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists,...

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Autores principales:	Maryia Khomich, Ingrid Måge, Ida Rud, Ingunn Berget
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ca38bf3a4bb4d5fa16f77e298bc1388
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spelling	oai:doaj.org-article:7ca38bf3a4bb4d5fa16f77e298bc13882021-12-02T20:12:46ZAnalysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.1932-620310.1371/journal.pone.0259973https://doaj.org/article/7ca38bf3a4bb4d5fa16f77e298bc13882021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259973https://doaj.org/toc/1932-6203The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.Maryia KhomichIngrid MågeIda RudIngunn BergetPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259973 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maryia Khomich Ingrid Måge Ida Rud Ingunn Berget Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
description	The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.
format	article
author	Maryia Khomich Ingrid Måge Ida Rud Ingunn Berget
author_facet	Maryia Khomich Ingrid Måge Ida Rud Ingunn Berget
author_sort	Maryia Khomich
title	Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
title_short	Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
title_full	Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
title_fullStr	Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
title_full_unstemmed	Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
title_sort	analysing microbiome intervention design studies: comparison of alternative multivariate statistical methods.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7ca38bf3a4bb4d5fa16f77e298bc1388
work_keys_str_mv	AT maryiakhomich analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods AT ingridmage analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods AT idarud analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods AT ingunnberget analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods
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Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.

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