plotScanpyDotPlot: plotScanpyDotPlot

In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

plotScanpyDotPlot

Description

plotScanpyDotPlot

Usage

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")
## Not run: 
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')

## End(Not run)

compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.