View source: R/seuratFunctions.R
runSeuratHeatmap | R Documentation |
runSeuratHeatmap Computes the heatmap plot object from the pca slot in the input sce object
runSeuratHeatmap(
inSCE,
useAssay,
useReduction = c("pca", "ica"),
dims = NULL,
nfeatures = 30,
cells = NULL,
ncol = NULL,
balanced = TRUE,
fast = TRUE,
combine = TRUE,
raster = TRUE,
externalReduction = NULL
)
inSCE |
(sce) object from which to compute heatmap (pca should be computed) |
useAssay |
Specify name of the assay that will be scaled by this function. The output scaled assay will be used for computation of the heatmap. |
useReduction |
Reduction method to use for computing clusters. One of
"pca" or "ica". Default |
dims |
Number of components to generate heatmap plot objects. If
|
nfeatures |
Number of features to include in the heatmap. Default
|
cells |
Numeric value indicating the number of top cells to plot.
Default is |
ncol |
Numeric value indicating the number of columns to use for plot.
Default is |
balanced |
Plot equal number of genes with positive and negative scores.
Default is |
fast |
See DimHeatmap for more information. Default
|
combine |
See DimHeatmap for more information. Default
|
raster |
See DimHeatmap for more information. Default
|
externalReduction |
Pass DimReduc object if PCA/ICA computed through
other libraries. Default |
plot object
data(scExample, package = "singleCellTK")
## Not run:
sce <- runSeuratNormalizeData(sce, useAssay = "counts")
sce <- runSeuratFindHVG(sce, useAssay = "counts")
sce <- runSeuratScaleData(sce, useAssay = "counts")
sce <- runSeuratPCA(sce, useAssay = "counts")
heatmap <- runSeuratHeatmap(sce, useAssay = "counts")
plotSeuratHeatmap(heatmap)
## End(Not run)
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