stackAssays: Stack assays from pseudobulk

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

Stack assays from pseudobulk

Description

Stack assays from pseudobulk to perform analysis across cell types

Usage

stackAssays(pb, assays = assayNames(pb))

Arguments

pb	pseudobulk SingleCellExperiment from aggregateToPseudoBulk()
assays	array of assay names to include in analysis. Defaults to assayNames(pb)

Value

pseudobulk SingleCellExperiment cbind'ing expression values and rbind'ing colData. The column stackedAssay in colData() stores the assay information of the stacked data.

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
)

# Stack assays for joint analysis
pb.stack <- stackAssays(pb)

# voom-style normalization
# assay (i.e. cell type) can now be included as a covariate
res.proc <- processAssays(pb.stack, ~ group_id + stackedAssay)

# variance partitioning analysis
vp <- fitVarPart(res.proc, ~ group_id + stackedAssay)

# Summarize variance fractions across cell types
plotVarPart(sortCols(vp))

# Interaction analysis allows group_id
# to have a different effect within each stacedAssay
vp2 <- fitVarPart(res.proc, ~ group_id * stackedAssay)

plotVarPart(sortCols(vp2))

# Interaction model using random effects
form <- ~ (1 | group_id) + (1 | stackedAssay) + (1 | group_id:stackedAssay)
#

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