association network analysis

#' Simulated expression data with knock-outs
#'
#' A dataset containing simulated expression dataset. Data is simulated using a
#' dynamical systems model from a network sampled from the S. Cerevisiae regulatory
#' network. The dataset is a list containing the results from the simulation,
#' and other information generated subsequently.
#'
#' @format A named list with 14 elements:
#' \describe{
#'   \item{simitr}{a numeric, indicating the iteration of the simulation (a
#'   total of 1000 were performed and 812 converged)}
#'   \item{scores}{an S4 Matrix, containing vectorised inference scores of
#'   applying the methods implemented in the package. These are precomputed
#'   predictions}
#'   \item{inputmodels}{a named list, storing the parameters used to sample the
#'   initial values of input genes. Proportions, means and variances of each
#'   gene is stored for each gene}
#'   \item{staticnet}{an igraph object, storing the initial regulatory network
#'   (150 node network)}
#'   \item{infnet}{an igraph object, representing the true differential network
#'   as determined using sensitivity analysis of the model}
#'   \item{netlayout}{a matrix (150 x 2), storing the (x, y) positions
#'   of nodes for laying out the graph}
#'   \item{infdens}{a numeric, network density of the true differential
#'   association network}
#'   \item{numinput}{a numeric, the number of input genes in the regulatory
#'   network. These are genes that have no regulators therefore need to be
#'   pre-defined}
#'   \item{numbimodal}{a numeric, the number of input genes that are knocked-down
#'   therefore have a bimodal distribution}
#'   \item{numtfs}{a numeric, the number of genes in the network that regulate
#'   any other gene (are TFs)}
#'   \item{numcotargets}{a numeric, the number of genes that are co-regulated,
#'   i.e. regulated by more than one TF}
#'   \item{data}{an S4 Matrix, the expression data with samples along the columns
#'   and genes along the rows. Condition classification (KD vs WT) are stored as
#'   attributes of this object}
#'   \item{triplets}{a data frame, consisting of gene triplets representing TF-
#'   Target associations conditioned on the gene knocked-down. Triplets are
#'   annotated for being in either the direct, influence and association networks}
#'   \item{sensmat}{an S4 Matrix, sensitivities of genes to TFs based on
#'   perturbation analysis of the simulation model}
#' }
#' @source LINK TO PAPERRRR
"sim102"