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Plot heatmap

Source: R/plotHeatmap.R
plotHeatmap-methods.Rd

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

Value

heatmap

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)