fitVarPart: Variance Partition analysis for each assay

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

Variance Partition analysis for each assay

Description

Perform Variance Partition analysis for each assay

Usage

fitVarPart(
  x,
  formula,
  data = colData(x),
  assays = assayNames(x),
  quiet = FALSE,
  BPPARAM = SerialParam(),
  ...
)

## S4 method for signature 'dreamletProcessedData'
fitVarPart(
  x,
  formula,
  data = colData(x),
  assays = assayNames(x),
  quiet = FALSE,
  BPPARAM = SerialParam(),
  ...
)

Arguments

x	SingleCellExperiment or dreamletProcessedData object
formula	regression formula for differential expression analysis
data	metadata used in regression formula
assays	array of assay names to include in analysis. Defaults to assayNames(x)
quiet	show messages
BPPARAM	parameters for parallel evaluation
...	other arguments passed to dream

Value

Object of class vpDF inheriting from DataFrame storing the variance fractions for each gene and 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)

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

# Show variance fractions at the gene-level for each cell type
genes <- vp$gene[2:4]
plotPercentBars(vp[vp$gene %in% genes, ])

# Summarize variance fractions genome-wide for each cell type
plotVarPart(vp)

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