Plot heatmap
Usage
plotHeatmap(
x,
genes = rownames(x),
color = "darkblue",
assays = colnames(x),
useFillScale = TRUE
)
# S4 method for cellSpecificityValues
plotHeatmap(
x,
genes = rownames(x),
color = "darkblue",
assays = colnames(x),
useFillScale = TRUE
)
# S4 method for data.frame
plotHeatmap(
x,
genes = rownames(x),
color = "darkblue",
assays = colnames(x),
useFillScale = TRUE
)
# S4 method for matrix
plotHeatmap(
x,
genes = rownames(x),
color = "darkblue",
assays = colnames(x),
useFillScale = TRUE
)
Arguments
- x
fractions for each gene
- genes
name of genes to plot
- color
color of heatmap
- assays
array of assays to plot
- useFillScale
default TRUE. add scale_fill_gradient() to plot
Examples
library(muscat)
library(SingleCellExperiment)
data(example_sce)
# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
assay = "counts",
cluster_id = "cluster_id",
sample_id = "sample_id",
verbose = FALSE
)
# Compute cell type specificity of each gene
df <- cellTypeSpecificity(pb)
# For each cell type, get most specific gene
genes <- rownames(df)[apply(df, 2, which.max)]
# heatmap of 5 genes that are most cell type specific
dreamlet::plotHeatmap(df, genes = genes)