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
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/7bdc6dcd369c4f79874adcf7c0bcb9d8
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spelling oai:doaj.org-article:7bdc6dcd369c4f79874adcf7c0bcb9d82021-11-25T06:19:36ZAnalysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods1932-6203https://doaj.org/article/7bdc6dcd369c4f79874adcf7c0bcb9d82021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601541/?tool=EBIhttps://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 (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/7bdc6dcd369c4f79874adcf7c0bcb9d8
work_keys_str_mv AT maryiakhomich analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods
AT ingridmage analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods
AT idarud analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods
AT ingunnberget analysingmicrobiomeinterventiondesignstudiescomparisonofalternativemultivariatestatisticalmethods
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