navigm is an R package that implements variable-guided network
inference using Bayesian graphical spike-and-slab modelling. Alongside
the primary node measurements, our framework encodes node-level
auxiliary variables that may be informative on the network structure.
For instance, gene network inference may be informed by the use of
publicly available summary statistics on the regulation of genes by
genetic variants.
Our approach relies on a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. Inference is carried out using an efficient variational expectation conditional maximisation algorithm that scales to hundreds of samples, nodes and auxiliary variables, and approximates full posterior distributions for parameters of interest.
Reference: Xiaoyue Xi, Hélène Ruffieux, 2023. A modelling framework for detecting and leveraging node-level information in Bayesian network inference.
The package can be installed in R using the following command:
if(!require(remotes)) install.packages("remotes")
remotes::install_github("XiaoyueXI/navigm")For further instructions, please check the tutorial.