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:
- simitr
a numeric, indicating the iteration of the simulation (a total of 1000 were performed and 812 converged)
- scores
an S4 Matrix, containing vectorised inference scores of applying the methods implemented in the package. These are precomputed predictions
- 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
- staticnet
an igraph object, storing the initial regulatory network (150 node network)
- infnet
an igraph object, representing the true differential network as determined using sensitivity analysis of the model
- netlayout
a matrix (150 x 2), storing the (x, y) positions of nodes for laying out the graph
- infdens
a numeric, network density of the true differential association network
- 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
- numbimodal
a numeric, the number of input genes that are knocked-down therefore have a bimodal distribution
- numtfs
a numeric, the number of genes in the network that regulate any other gene (are TFs)
- numcotargets
a numeric, the number of genes that are co-regulated, i.e. regulated by more than one TF
- 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
- 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
- sensmat
an S4 Matrix, sensitivities of genes to TFs based on perturbation analysis of the simulation model