sracipeKnockDown: Perform in-silico knockdown analysis

In vivekkohar/sRACIPE: Systems biology tool to simulate gene regulatory circuits

Description

Calculate the fraction of models in different clusters with full parameter range and on a subset of models with low production rate of a specific gene representing the knockdown of the specific gene.

Usage

sracipeKnockDown(
  .object,
  reduceProduction = 10,
  nClusters = 2,
  clusterOfInterest = 2,
  plotFilename = NULL,
  plotHeatmap = TRUE,
  plotBarPlot = TRUE,
  clusterCut = NULL,
  plotToFile = FALSE
)

## S4 method for signature 'RacipeSE'
sracipeKnockDown(
  .object,
  reduceProduction = 10,
  nClusters = 2,
  clusterOfInterest = 2,
  plotFilename = NULL,
  plotHeatmap = TRUE,
  plotBarPlot = TRUE,
  clusterCut = NULL,
  plotToFile = FALSE
)

Arguments

.object	RacipeSE object generated by sracipeSimulate function.
reduceProduction	(optional) Percentage to which production rate decreases on knockdown. Uses a default value of 10 percent.
nClusters	(optional) Number of clusters in the data. Uses a default value of 2.
clusterOfInterest	(optional) cluster number (integer) to be used for arranging the transcription factors
plotFilename	(optional) Name of the output file.
plotHeatmap	logical. Default TRUE. Whether to plot the heatmap or not.
plotBarPlot	logical. Default TRUE. Whether to plot the barplot.
clusterCut	integer or character. The cluster assignments.
plotToFile	logical. Default FALSE.

Value

List containing fraction of models in different clusters in the original simulations and after knowcking down different genes. Additionaly, it generates two pdf files in the results folder. First is barplot showing the percentage of different clusters in the original simulations and after knocking down each gene. The second pdf contains the heatmap of clusters after marking the models with cluster assignments.

Related Functions

sracipeSimulate, sracipeKnockDown, sracipeOverExp, sracipePlotData

Examples

data("demoCircuit")
## Not run: 
rSet <- sRACIPE::sracipeSimulate(circuit = demoCircuit, numModels = 100,
plots=FALSE, plotToFile = FALSE)
rSet <- sRACIPE::sracipeNormalize(rSet)
rSet <- sRACIPE::sracipeKnockDown(rSet, plotToFile = FALSE,
plotBarPlot = TRUE, plotHeatmap = FALSE, reduceProduction = 50)

## End(Not run)

vivekkohar/sRACIPE documentation built on March 21, 2021, 8:19 p.m.