View source: R/scanpyFunctions.R
plotScanpyMatrixPlot | R Documentation |
plotScanpyMatrixPlot
plotScanpyMatrixPlot(
inSCE,
useAssay = NULL,
features,
groupBy,
standardScale = NULL,
title = "",
vmin = NULL,
vmax = NULL,
colorBarTitle = "Mean expression in group"
)
inSCE |
Input |
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 |
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 |
vmax |
The value representing the upper limit of the color scale.
Values larger than vmax are plotted with the same color as vmax.
Default |
colorBarTitle |
Title for the color bar. |
plot object
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")
plotScanpyMatrixPlot(sce, features = markers, groupBy = 'Scanpy_louvain_1')
## End(Not run)
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