diffVar-methods: Test differential variance

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

Test differential variance

Description

Test the association between a covariate of interest and the response's deviation from expectation.

Usage

## S4 method for signature 'dreamletResult'
diffVar(
  fit,
  method = c("AD", "SQ"),
  scale = c("leverage", "none"),
  BPPARAM = SerialParam(),
  ...
)

Arguments

fit	model fit from dream()
method	transform the residuals using absolute deviation ("AD") or squared deviation ("SQ").
scale	scale each observation by "leverage", or no scaling ("none")
BPPARAM	parameters for parallel evaluation
...	other parameters passed to dream()

Details

This method performs a test of differential variance between two subsets of the data, in a way that generalizes to multiple categories, continuous variables and metrics of spread beyond variance. For the two category test, this method is simular to Levene's test. This model was adapted from Phipson, et al (2014), extended to linear mixed models, and adapted to be compatible with variancePartition::dream() and dreamlet::dreamlet().

This method is composed of multiple steps where 1) a typical linear (mixed) model is fit with dreamlet(), 2) residuals are computed and transformed based on an absolute value or squaring transform, 3) a second regression is performed with dreamlet() to test if a variable is associated with increased deviation from expectation. Both regression take advantage of the dreamlet() linear (mixed) modelling framework followed by empirical Bayes shrinkage that extends the limma::voom() framework.

Note that diffVar() takes the results of the first regression as a parameter to use as a starting point.

References

\insertRef

phipson2014diffvarvariancePartition

See Also

variancePartition::diffVar()

variancePartition::diffVar(), missMethyl::diffVar()

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)

# Differential variance analysis
# result is a dreamlet fit
res.dvar <- diffVar(res.dl)

# Examine results
res.dvar

# Examine details for each assay
details(res.dvar)

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

# extract results using limma-style syntax
# combines all cell types together
# adj.P.Val gives study-wide FDR
topTable(res.dvar, coef = "group_idstim", number = 3)

# Plot top hit to see differential variance
# Note that this is a toy example with only 4 samples
cellType <- "CD4 T cells"
gene <- "DYNLRB1"

y <- res.proc[[cellType]]$E[gene, ]
x <- colData(res.proc)$group_id

boxplot(y ~ x,
  xlab = "Stimulation status",
  ylab = "Gene expression",
  main = paste(cellType, gene)
)
#

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