R/seuratFunctions.R
seuratComputeHeatmap.Rd
seuratComputeHeatmap Computes the heatmap plot object from the pca slot in the input sce object
seuratComputeHeatmap( 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 | Assay containing scaled counts to use in 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") if (FALSE) { sce <- seuratNormalizeData(sce, useAssay = "counts") sce <- seuratFindHVG(sce, useAssay = "counts") sce <- seuratScaleData(sce, useAssay = "counts") sce <- seuratPCA(sce, useAssay = "counts") heatmap <- seuratComputeHeatmap(sce, useAssay = "counts") seuratHeatmapPlot(heatmap) }