plotScanpyDotPlot

plotScanpyDotPlot

plotScanpyDotPlot(
  inSCE,
  useAssay = NULL,
  features,
  groupBy,
  standardScale = NULL,
  title = "",
  vmin = NULL,
  vmax = NULL,
  colorBarTitle = "Mean expression in group"
)

Arguments

inSCE

Input SingleCellExperiment object.

useAssay

Assay to use for plotting. By default it will use counts assay.

features

Genes to plot. Sometimes is useful to pass a specific list of var names (e.g. genes). The var_names could be a dictionary or a list.

groupBy

The key of the observation grouping to consider.

standardScale

Whether or not to standardize the given dimension between 0 and 1, meaning for each variable or group, subtract the minimum and divide each by its maximum. Default NULL means that it doesn't perform any scaling.

title

Provide title for the figure.

vmin

The value representing the lower limit of the color scale. Values smaller than vmin are plotted with the same color as vmin. Default NULL

vmax

The value representing the upper limit of the color scale. Values larger than vmax are plotted with the same color as vmax. Default NULL

colorBarTitle

Title for the color bar.

Value

plot object

Examples

data(scExample, package = "singleCellTK")
if (FALSE) {
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")
sce <- runScanpyPCA(sce, useAssay = "scanpyScaledData")
sce <- runScanpyFindClusters(sce, useReducedDim = "scanpyPCA")
sce <- runScanpyUMAP(sce, useReducedDim = "scanpyPCA")
markers <- c("MALAT1" ,"RPS27" ,"CST3")
plotScanpyDotPlot(sce, features = markers, groupBy = 'Scanpy_louvain_1')
}