812 simulated expression datasets for differential co-expression analysis

<h2>Simulated expression data with knock-outs</h2> <h3>Description</h3> <p>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. </p> <h3>Format</h3> <p>A named list with 14 elements: </p> <dl><dt>simitr</dt><dd><p>a numeric, indicating the iteration of the simulation (a total of 1000 were performed and 812 converged)</p> </dd><dt>scores</dt><dd><p>an S4 Matrix, containing vectorised inference scores of applying the methods implemented in the package. These are precomputed predictions</p> </dd><dt>inputmodels</dt><dd><p>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</p> </dd><dt>staticnet</dt><dd><p>an igraph object, storing the initial regulatory network (150 node network)</p> </dd><dt>infnet</dt><dd><p>an igraph object, representing the true differential network as determined using sensitivity analysis of the model</p> </dd><dt>netlayout</dt><dd><p>a matrix (150 x 2), storing the (x, y) positions of nodes for laying out the graph</p> </dd><dt>infdens</dt><dd><p>a numeric, network density of the true differential association network</p> </dd><dt>numinput</dt><dd><p>a numeric, the number of input genes in the regulatory network. These are genes that have no regulators therefore need to be pre-defined</p> </dd><dt>numbimodal</dt><dd><p>a numeric, the number of input genes that are knocked-down therefore have a bimodal distribution</p> </dd><dt>numtfs</dt><dd><p>a numeric, the number of genes in the network that regulate any other gene (are TFs)</p> </dd><dt>numcotargets</dt><dd><p>a numeric, the number of genes that are co-regulated, i.e. regulated by more than one TF</p> </dd><dt>data</dt><dd><p>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</p> </dd><dt>triplets</dt><dd><p>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</p> </dd><dt>sensmat</dt><dd><p>an S4 Matrix, sensitivities of genes to TFs based on perturbation analysis of the simulation model</p></dd></dl><h3>Load</h3><div>This dataset is in the form of an R RDS object. To load it, type the command below in an R console:</div><div><br></div><i>simdata = readRDS("sim812.rds")</i><dl><dt><br></dt></dl>