Author: Andrea Rau
This package is used to infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.
Posterior distributions (mean and variance) of network parameters are
estimated using time-course data based on a linear feedback state space
model that allows for a set of hidden states to be in- corporated. The
algorithm is composed of three principal parts: choice of hidden state
dimension (see hankel), estimation of hidden states via the
Kalman filter and smoother, and calculation of posterior distributions
based on the empirical Bayes estimation of hyperparameters in a
hierarchical Bayesian framework (see ebdbn).
Plot functionalities are provided via the igraph
package.
A. Rau, F. Jaffrezic, J.-L. Foulley, R. W. Doerge (2010). An empirical Bayesian method for estimating biological networks from temporal microarray data. Statistical Applications in Genetics and Molecular Biology, vol. 9, iss. 1, article 9.