MDiNE: a model to estimate differential co-occurrence networks in microbiome studies.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 03 2020
01 03 2020
Historique:
received:
07
02
2019
revised:
26
10
2019
accepted:
05
11
2019
pubmed:
8
11
2019
medline:
17
9
2020
entrez:
8
11
2019
Statut:
ppublish
Résumé
The human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa through precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates. We propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modeled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn's disease. MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 31697315
pii: 5614428
doi: 10.1093/bioinformatics/btz824
pmc: PMC7075537
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1840-1847Subventions
Organisme : CIHR
ID : MOP-130344
Pays : Canada
Informations de copyright
© The Author(s) 2019. Published by Oxford University Press.
Références
Theory Biosci. 2016 Jun;135(1-2):21-36
pubmed: 26762323
Genome Med. 2016 Apr 27;8(1):48
pubmed: 27124954
J R Stat Soc Series B Stat Methodol. 2014 Mar;76(2):373-397
pubmed: 24817823
Syst Biol. 2002 Oct;51(5):754-60
pubmed: 12396589
Front Microbiol. 2017 Nov 15;8:2224
pubmed: 29187837
PLoS Comput Biol. 2015 Mar 16;11(3):e1004075
pubmed: 25775355
Nat Rev Microbiol. 2010 Jan;8(1):15-25
pubmed: 19946288
Nature. 2009 Jan 22;457(7228):480-4
pubmed: 19043404
J R Stat Soc Series B Stat Methodol. 2013 Jun 1;75(3):427-450
pubmed: 23730197
Nature. 2007 Oct 18;449(7164):804-10
pubmed: 17943116
Proc Natl Acad Sci U S A. 2007 Aug 21;104(34):13780-5
pubmed: 17699621
Gut Pathog. 2018 Oct 10;10:44
pubmed: 30337963
Diabetologia. 2006 Sep;49(9):2105-8
pubmed: 16816951
Am J Physiol Gastrointest Liver Physiol. 2012 Sep 15;303(6):G675-85
pubmed: 22821944
PLoS Comput Biol. 2012;8(9):e1002687
pubmed: 23028285
PLoS One. 2012;7(2):e30126
pubmed: 22319561
PLoS One. 2013 Apr 22;8(4):e61217
pubmed: 23630581
Stat Interface. 2013 Apr 1;6(2):243-259
pubmed: 24551316
Biostatistics. 2008 Jul;9(3):432-41
pubmed: 18079126
Proc Natl Acad Sci U S A. 2013 Jul 30;110(31):12804-9
pubmed: 23858463
Biometrika. 2014 Jun;101(2):253-268
pubmed: 26023240
Microbiome. 2015 Jun 13;3:24
pubmed: 26106478
PLoS Comput Biol. 2015 May 07;11(5):e1004226
pubmed: 25950956
Nat Methods. 2019 May;16(5):381-386
pubmed: 30962620
Nat Med. 2017 Mar;23(3):314-326
pubmed: 28112736
J Comput Biol. 2016 Jun;23(6):526-35
pubmed: 27267776
Biometrics. 2013 Dec;69(4):1053-63
pubmed: 24128059
Nat Commun. 2017 Sep 11;8(1):518
pubmed: 28894149
Comput Stat Data Anal. 2014 Dec 1;80:117-128
pubmed: 25143662
Genome Biol. 2012 Apr 16;13(9):R79
pubmed: 23013615
Ann Appl Stat. 2013 Mar 1;7(1):
pubmed: 24312162
Bioinformatics. 2015 Oct 1;31(19):3172-80
pubmed: 26048598
Cell Host Microbe. 2014 Mar 12;15(3):382-392
pubmed: 24629344