plotScanpyHeatmap
plotScanpyHeatmap(
inSCE,
useAssay = NULL,
features,
groupBy,
standardScale = "var",
vmin = NULL,
vmax = NULL
)
Input SingleCellExperiment
object.
Assay to use for plotting. By default it will use counts assay.
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.
The key of the observation grouping to consider.
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.
The value representing the lower limit of the color scale.
Values smaller than vmin are plotted with the same color as vmin.
Default NULL
The value representing the upper limit of the color scale.
Values larger than vmax are plotted with the same color as vmax.
Default NULL
plot object
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")
plotScanpyHeatmap(sce, features = markers, groupBy = 'Scanpy_louvain_1')
}