residuals-methods: Extract residuals from 'dreamletResult'

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

Extract residuals from dreamletResult

Description

Extract residuals from dreamletResult

Usage

## S4 method for signature 'dreamletResult'
residuals(object, y, ..., type = c("response", "pearson"))

Arguments

object	dreamletResult object
y	dreamletProcessedData object
...	other arguments
type	compute either "response" residuals or "pearson" residuals.

Details

"response" residuals are the typical residuals returned from lm(). "pearson" residuals divides each residual value by its estimated standard error. This requires specifying y

Value

residuals from model fit

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)

# extract typical residuals for each assay (i.e. cell type)
# Return list with entry for each assay with for retained samples and genes
resid.lst <- residuals(res.dl)

# Get Pearson residuals:
# typical residuals scaled by the standard deviation
residPearson.lst <- residuals(res.dl, res.proc, type = "pearson")

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