Literature DB >> 34255026

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

Jun Chen1, Xianyang Zhang2.   

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: D-MANOVA is implemented by the dmanova function in the CRAN package GUniFrac. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
© The Author(s) (2021). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Year:  2021        PMID: 34255026      PMCID: PMC8696110          DOI: 10.1093/bioinformatics/btab498

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.931


  5 in total

1.  Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

Authors:  Daniel B McArtor; Gitta H Lubke; C S Bergeman
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Review 2.  Microbiome at the Frontier of Personalized Medicine.

Authors:  Purna C Kashyap; Nicholas Chia; Heidi Nelson; Eran Segal; Eran Elinav
Journal:  Mayo Clin Proc       Date:  2017-12       Impact factor: 7.616

3.  Associating microbiome composition with environmental covariates using generalized UniFrac distances.

Authors:  Jun Chen; Kyle Bittinger; Emily S Charlson; Christian Hoffmann; James Lewis; Gary D Wu; Ronald G Collman; Frederic D Bushman; Hongzhe Li
Journal:  Bioinformatics       Date:  2012-06-17       Impact factor: 6.937

4.  American Gut: an Open Platform for Citizen Science Microbiome Research.

Authors:  Daniel McDonald; Embriette Hyde; Justine W Debelius; James T Morton; Antonio Gonzalez; Gail Ackermann; Alexander A Aksenov; Bahar Behsaz; Caitriona Brennan; Yingfeng Chen; Lindsay DeRight Goldasich; Pieter C Dorrestein; Robert R Dunn; Ashkaan K Fahimipour; James Gaffney; Jack A Gilbert; Grant Gogul; Jessica L Green; Philip Hugenholtz; Greg Humphrey; Curtis Huttenhower; Matthew A Jackson; Stefan Janssen; Dilip V Jeste; Lingjing Jiang; Scott T Kelley; Dan Knights; Tomasz Kosciolek; Joshua Ladau; Jeff Leach; Clarisse Marotz; Dmitry Meleshko; Alexey V Melnik; Jessica L Metcalf; Hosein Mohimani; Emmanuel Montassier; Jose Navas-Molina; Tanya T Nguyen; Shyamal Peddada; Pavel Pevzner; Katherine S Pollard; Gholamali Rahnavard; Adam Robbins-Pianka; Naseer Sangwan; Joshua Shorenstein; Larry Smarr; Se Jin Song; Timothy Spector; Austin D Swafford; Varykina G Thackray; Luke R Thompson; Anupriya Tripathi; Yoshiki Vázquez-Baeza; Alison Vrbanac; Paul Wischmeyer; Elaine Wolfe; Qiyun Zhu; Rob Knight
Journal:  mSystems       Date:  2018-05-15       Impact factor: 6.496

5.  Fewer permutations, more accurate P-values.

Authors:  Theo A Knijnenburg; Lodewyk F A Wessels; Marcel J T Reinders; Ilya Shmulevich
Journal:  Bioinformatics       Date:  2009-06-15       Impact factor: 6.937

  5 in total
  2 in total

1.  LinDA: linear models for differential abundance analysis of microbiome compositional data.

Authors:  Huijuan Zhou; Kejun He; Jun Chen; Xianyang Zhang
Journal:  Genome Biol       Date:  2022-04-14       Impact factor: 17.906

2.  MicrobiomeGWAS: A Tool for Identifying Host Genetic Variants Associated with Microbiome Composition.

Authors:  Xing Hua; Lei Song; Guoqin Yu; Emily Vogtmann; James J Goedert; Christian C Abnet; Maria Teresa Landi; Jianxin Shi
Journal:  Genes (Basel)       Date:  2022-07-09       Impact factor: 4.141

  2 in total

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