Plot sRACIPE data

Plots heatmap, pca, umap of the data simulated using sRACIPE

sracipePlotData(
  .object,
  plotToFile = FALSE,
  nClusters = 2,
  heatmapPlot = TRUE,
  pcaPlot = TRUE,
  umapPlot = TRUE,
  networkPlot = TRUE,
  clustMethod = "ward.D2",
  col = col,
  distType = "euclidean",
  assignedClusters = NULL,
  corMethod = "spearman",
  ...
)

# S4 method for RacipeSE
sracipePlotData(
  .object,
  plotToFile = FALSE,
  nClusters = 2,
  heatmapPlot = TRUE,
  pcaPlot = TRUE,
  umapPlot = TRUE,
  networkPlot = TRUE,
  clustMethod = "ward.D2",
  col = col,
  distType = "euclidean",
  assignedClusters = NULL,
  corMethod = "spearman",
  ...
)

Arguments

.object	List A list returned by sracipeSimulate function
plotToFile	(optional) logical. Default FALSE. Whether to save plots to a file.
nClusters	(optional) Integer. Default 2. Expected number of clusters in the simulated data. Hierarchical clustering will be used to cluster the data and the the models will be colored in UMAP and PCA plots according to these clustering results. The clusters can be also supplied using assignedClusters.
heatmapPlot	(optional) logical. Default TRUE. Whether to plot hierarchichal clustering.
pcaPlot	(optional) logical. Default TRUE. Whether to plot PCA embedding.
umapPlot	(optional) logical. Default TRUE. Whether to plot UMAP embedding
networkPlot	(optional) logical. Default TRUE. Whether to plot the network.
clustMethod	(optional) character. Default "ward.D2". Clustering method for heatmap. See heatmap.2
col	(optional) Color palette
distType	(optional) Distance type. Used only if specified explicitly. Otherwise, 1-cor is used. See dist, hclust
assignedClusters	vector integer or character. Default NULL. Cluster assignment of models.
corMethod	(optional) character. Default "spearman". Correlation method for distance function.
...	Other arguments

Value

RacipeSE object

Related Functions

sracipeSimulate, sracipeKnockDown, sracipeOverExp, sracipePlotData,

Examples

data("demoCircuit")
if (FALSE) {
rSet <- sRACIPE::sracipeSimulate(circuit = demoCircuit, numModels = 20,
integrateStepSize = 0.1, simulationTime = 30)
rSet <- sracipePlotData(rSet)
}