Bayesian Inference of Multiple Gaussian Graphical Models • multiGGM

The multiGGM package implements the Bayesian method of Peterson, Stingo & Vannucci (2015, JASA) for joint inference of multiple Gaussian graphical models (GGMs). Given data from $K$ groups, the method simultaneously estimates group-specific precision matrices $\Omega_k$ and their associated conditional independence graphs $G_k$ , while borrowing strength across groups through a Markov random field (MRF) prior. It also implements two follow-up versions of the original algorithm, a faster version using continuous spike-and-slab priors called scale-multiGGM (Shaddox, Peterson, Vanucci, 2018, Statistics in Biosciences), and multi-platform multiGGM (Shaddox et al. 2020) which allows you to incorporate multi-modal data and jointly estimate networks.

Installation

You can install the development version of multiGGM from GitHub with:

# install.packages("pak")
pak::pak("mljaniczek/multiGGMr")

Example

This is a basic example simulating data from 2 groups and then running the scale-multiGGM. For more detailed examples see vignettes.

library(multiGGM)

# first simulate some data
sim <- simulate_multiggm(
  K = 2, p = 20, n = 100,
  graph_type = "hub",
  perturb_prob = 0.1,
  seed = 42
)

# then run multiGGM, specifying which method

fit <- multiggm_mcmc(
  data_list = sim$data_list,
  method    = "ssvs", # this is for the scalable version
  burnin    = 200,
  nsave     = 100,
  seed      = 123
)

References

Peterson, C.B., Stingo, F.C. & Vannucci, M. (2015). Bayesian inference of multiple Gaussian graphical models. Journal of the American Statistical Association, 110(509), 159–174.

Shaddox, E., Stingo, F.C., Peterson, C.B., et al. (2018). A Bayesian approach for learning gene networks underlying disease severity in COPD. Statistics in Biosciences, 10(1), 59–85.

Shaddox, E., Peterson, C.B., Stingo, F.C., et al. (2020). Bayesian inference of networks across multiple sample groups and data types. Biostatistics, 21 (3) 561–-576.

Wang, H. (2015). Scaling it up: stochastic search structure learning in graphical models. Bayesian Analysis, 10(2), 351–377.

Wang, H. & Li, S.Z. (2012). Efficient Gaussian graphical model determination under G-Wishart prior distributions. Electronic Journal of Statistics, 6, 168–198.