Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.

Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate t...

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Main Authors: Leyla Kodalci, Olivier Thas
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS ONE
Online Access:https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292055&type=printable
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author Leyla Kodalci
Olivier Thas
author_facet Leyla Kodalci
Olivier Thas
author_sort Leyla Kodalci
collection DOAJ
description Microbiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package 'signtrans' and can be installed from Github (https://github.com/lucp9827/signtrans).
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spelling doaj.art-71097304c8604d4188129d0942160f522023-10-04T05:31:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01189e029205510.1371/journal.pone.0292055Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.Leyla KodalciOlivier ThasMicrobiome data obtained with amplicon sequencing are considered as compositional data. It has been argued that these data can be analysed after appropriate transformation to log-ratios, but ratios and logarithms cause problems with the many zeroes in typical microbiome experiments. We demonstrate that some well chosen sign and rank transformations also allow for valid inference with compositional data, and we show how logistic regression and probabilistic index models can be used for testing for differential abundance, while inheriting the flexibility of a statistical modelling framework. The results of a simulation study demonstrate that the new methods perform better than most other methods, and that it is comparable with ANCOM-BC. These methods are implemented in an R-package 'signtrans' and can be installed from Github (https://github.com/lucp9827/signtrans).https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292055&type=printable
spellingShingle Leyla Kodalci
Olivier Thas
Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
PLoS ONE
title Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
title_full Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
title_fullStr Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
title_full_unstemmed Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
title_short Simple and flexible sign and rank-based methods for testing for differential abundance in microbiome studies.
title_sort simple and flexible sign and rank based methods for testing for differential abundance in microbiome studies
url https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0292055&type=printable
work_keys_str_mv AT leylakodalci simpleandflexiblesignandrankbasedmethodsfortestingfordifferentialabundanceinmicrobiomestudies
AT olivierthas simpleandflexiblesignandrankbasedmethodsfortestingfordifferentialabundanceinmicrobiomestudies