Congratulations to Dr. Celia Greenwood (Platform 5 Lead), Kevin McGregor et al.
MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine
MDiNE: A model to estimate differential co-occurrence networks in microbiome studies – Bioinformatics. 2019 Nov 7. PMID: 31697315 DOI: 10.1093/bioinformatics/btz824
Abstract
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 modelled 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.
