Open AccessD-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies
Author(s) -
Jun Chen,
Xianyang Zhang
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab498
Subject(s) - multivariate analysis of variance , multivariate statistics , permutation (music) , statistics , microbiome , statistic , covariate , sample size determination , scale (ratio) , variance (accounting) , sample (material) , multivariate analysis , mathematics , computer science , biology , bioinformatics , geography , cartography , physics , chemistry , accounting , chromatography , acoustics , business
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.