Perform in-silico knockdown analysis — sracipeKnockDown • sRACIPE
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.

# Perform in-silico knockdown analysis

Source: `R/AllGenerics.R`, `R/methods.R`
`sracipeKnockDown.Rd`

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.

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

    # S4 method for 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")
    if (FALSE) {
    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)
    }