plotGeneHeatmap-methods: Heatmap of genes and assays

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

Heatmap of genes and assays

Description

Heatmap of genes and assays

Usage

plotGeneHeatmap(
  x,
  coef,
  genes,
  assays = assayNames(x),
  zmax = NULL,
  transpose = FALSE
)

## S4 method for signature 'dreamletResult'
plotGeneHeatmap(
  x,
  coef,
  genes,
  assays = assayNames(x),
  zmax = NULL,
  transpose = FALSE
)

Arguments

x	A dreamletResult object
coef	column number or column name specifying which coefficient or contrast of the linear model is of interest.
genes	array of genes to include in plot
assays	array of assay names to include in analysis. Defaults to assayNames(x)
zmax	maximum z.std value
transpose	(default: FALSE) Use 'coord_flip()' to flip axies

Value

Heatmap plot for specified genes and assays

Heatmap plot for specified genes and assays

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
)

# voom-style normalization
res.proc <- processAssays(pb, ~group_id)

# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(res.proc, ~group_id)

# Heatmap for specified subset of genes
plotGeneHeatmap(res.dl, coef = "group_idstim", genes = rownames(pb)[1:15])

GabrielHoffman/dreamlet documentation built on Nov. 8, 2024, 2:45 a.m.