812 simulated expression datasets for differential co-expression analysis

Simulated expression data with knock-outs

Description

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:

simitra numeric, indicating the iteration of the simulation (a
total of 1000 were performed and 812 converged)

scoresan S4 Matrix, containing vectorised inference scores of
applying the methods implemented in the package. These are precomputed
predictions

inputmodelsa 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

staticnetan igraph object, storing the initial regulatory network
(150 node network)

infnetan igraph object, representing the true differential network
as determined using sensitivity analysis of the model

netlayouta matrix (150 x 2), storing the (x, y) positions
of nodes for laying out the graph

infdensa numeric, network density of the true differential
association network

numinputa numeric, the number of input genes in the regulatory
network. These are genes that have no regulators therefore need to be
pre-defined

numbimodala numeric, the number of input genes that are knocked-down
therefore have a bimodal distribution

numtfsa numeric, the number of genes in the network that regulate
any other gene (are TFs)

numcotargetsa numeric, the number of genes that are co-regulated,
i.e. regulated by more than one TF

dataan 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

tripletsa 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

sensmatan S4 Matrix, sensitivities of genes to TFs based on
perturbation analysis of the simulation model
LoadThis dataset is in the form of an R RDS object. To load it, type the command below in an R console:
simdata = readRDS("sim812.rds")