plotVolcano-methods: Volcano plot for each cell type

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

Volcano plot for each cell type

Description

Volcano plot for each cell type

Usage

plotVolcano(
  x,
  coef,
  nGenes = 5,
  size = 12,
  minp = 9.99999999999997e-311,
  cutoff = 0.05,
  ncol = 3,
  ...
)

## S4 method for signature 'list'
plotVolcano(
  x,
  coef,
  nGenes = 5,
  size = 12,
  minp = 9.99999999999997e-311,
  cutoff = 0.05,
  ncol = 3,
  assays = names(x),
  ...
)

## S4 method for signature 'MArrayLM'
plotVolcano(
  x,
  coef,
  nGenes = 5,
  size = 12,
  minp = 9.99999999999997e-311,
  cutoff = 0.05,
  ncol = 3,
  ...
)

## S4 method for signature 'dreamlet_mash_result'
plotVolcano(
  x,
  coef,
  nGenes = 5,
  size = 12,
  minp = 1e-16,
  cutoff = 0.05,
  ncol = 3,
  assays = colnames(x$logFC.original),
  ...
)

Arguments

x	result from dreamlet
coef	coefficient to test with topTable
nGenes	number of genes to highlight in each volcano plot
size	text size
minp	minimum p-value to show on the y-axis
cutoff	adj.P.Val cutoff to distinguish significant from non-significant genes
ncol	number of columns in the plot
...	arguments passed to facet_wrap(). Useful for specifying scales = "free_y"
assays	which assays to plot

Value

Volcano plot for each cell type

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)

# show coefficients estimated for each cell type
coefNames(res.dl)

# volcano plot for each cell type
plotVolcano(res.dl, coef = "group_idstim")

# volcano plot for first two cell types
plotVolcano(res.dl[1:2], coef = "group_idstim")

GabrielHoffman/dreamlet documentation built on May 20, 2024, 2:05 p.m.