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

Load

This 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")