10.26188/5cb8049f593a6
Dharmesh Dinesh Bhuva
Melissa J. Davis
Joseph Cursons
812 simulated expression datasets for differential co-expression analysis
2019
University of Melbourne
differential networks
differential co-expression
Gene regulatory networks
dcanr
Simulated datasets
dynamical systems modelling
2019-05-01 23:58:02
article
https://melbourne.figshare.com/articles/812_simulated_expression_datasets_for_differential_co-expression_analysis/8010176
<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>