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D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies.


ABSTRACT:

Summary

PERMANOVA (permutational multivariate analysis of variance based on distances) has been widely used for testing the association between the microbiome and a covariate of interest. Statistical significance is established by permutation, which is computationally intensive for large sample sizes. As large-scale microbiome studies, such as American Gut Project (AGP), become increasingly popular, a computationally efficient version of PERMANOVA is much needed. To achieve this end, we derive the asymptotic distribution of the PERMANOVA pseudo-F statistic and provide analytical P-value calculation based on chi-square approximation. We show that the asymptotic P-value is close to the PERMANOVA P-value even under a moderate sample size. Moreover, it is more accurate and an order-of-magnitude faster than the permutation-free method MDMR. We demonstrated the use of our procedure D-MANOVA on the AGP dataset.

Availability and implementation

D-MANOVA is implemented by the dmanova function in the CRAN package GUniFrac.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Chen J 

PROVIDER: S-EPMC8696110 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies.

Chen Jun J   Zhang Xianyang X  

Bioinformatics (Oxford, England) 20211201 1


<h4>Summary</h4>PERMANOVA (permutational multivariate analysis of variance based on distances) has been widely used for testing the association between the microbiome and a covariate of interest. Statistical significance is established by permutation, which is computationally intensive for large sample sizes. As large-scale microbiome studies, such as American Gut Project (AGP), become increasingly popular, a computationally efficient version of PERMANOVA is much needed. To achieve this end, we  ...[more]

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