plotVoom-methods: Plot voom curves from each cell type

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

Plot voom curves from each cell type

Description

Plot voom curves from each cell type

Usage

plotVoom(x, ncol = 3, alpha = 0.5, ...)

## S4 method for signature 'dreamletProcessedData'
plotVoom(x, ncol = 3, alpha = 0.5, assays = names(x))

## S4 method for signature 'EList'
plotVoom(x, ncol = 3, alpha = 0.5)

Arguments

x	dreamletProcessedData
ncol	number of columns in the plot
alpha	transparency of points
...	other arguments
assays	which assays to plot

Value

Plot of mean-variance trend

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)

# Show mean-variance trend from voom
plotVoom(res.proc)

# plot for first two cell types
plotVoom(res.proc[1:2])

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