plotHeatmap-methods: Plot heatmap

In GabrielHoffman/dreamlet: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Plot heatmap

Description

Plot heatmap

Usage

plotHeatmap(
  x,
  genes = rownames(x),
  color = "darkblue",
  assays = colnames(x),
  useFillScale = TRUE
)

## S4 method for signature 'cellSpecificityValues'
plotHeatmap(
  x,
  genes = rownames(x),
  color = "darkblue",
  assays = colnames(x),
  useFillScale = TRUE
)

## S4 method for signature 'data.frame'
plotHeatmap(
  x,
  genes = rownames(x),
  color = "darkblue",
  assays = colnames(x),
  useFillScale = TRUE
)

## S4 method for signature '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)

GabrielHoffman/dreamlet documentation built on Oct. 29, 2024, 4:04 a.m.