plotForest-methods: Forest plot

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

Forest plot

Description

Forest plot

Usage

plotForest(x, gene, coef, ...)

## S4 method for signature 'dreamletResult'
plotForest(x, gene, coef, assays = names(x), ylim = NULL)

## S4 method for signature 'dreamlet_mash_result'
plotForest(x, gene, coef, assays = colnames(x$logFC.original), ylim = NULL)

Arguments

x	result from dreamlet
gene	gene to show results for
coef	coefficient to test with topTable
...	other arguments
assays	array of assays to plot
ylim	limits for the y axis

Value

Plot showing effect sizes

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)

# Show estimated log fold change with in each cell type
plotForest(res.dl, gene = "ISG20", coef = "group_idstim")

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