plotScanpyMarkerGenesDotPlot

plotScanpyMarkerGenesDotPlot

plotScanpyMarkerGenesDotPlot(
  inSCE,
  groups = NULL,
  nGenes = 10,
  groupBy,
  log2fcThreshold = NULL,
  parameters = "logfoldchanges",
  standardScale = NULL,
  features = NULL,
  title = "",
  vmin = NULL,
  vmax = NULL,
  colorBarTitle = "log fold change"
)

Arguments

inSCE

Input SingleCellExperiment object.

groups

The groups for which to show the gene ranking. Default NULL means that all groups will be considered.

nGenes

Number of genes to show. Default 10

groupBy

The key of the observation grouping to consider. By default, the groupby is chosen from the rank genes groups parameter.

log2fcThreshold

Only output DEGs with the absolute values of log2FC larger than this value. Default NULL.

parameters

The options for marker genes results to plot are: ‘scores’, ‘logfoldchanges’, ‘pvals’, ‘pvals_adj’, ‘log10_pvals’, ‘log10_pvals_adj’. If NULL provided then it uses mean gene value to plot.

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.

features

Genes to plot. Sometimes is useful to pass a specific list of var names (e.g. genes) to check their fold changes or p-values, instead of the top/bottom genes. The gene names could be a dictionary or a list. Default NULL

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 <- runScanpyFindMarkers(sce, colDataName = "Scanpy_louvain_1" )
plotScanpyMarkerGenesDotPlot(sce, groupBy = 'Scanpy_louvain_1')
}