processAssays: Processing SingleCellExperiment to dreamletProcessedData

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

Processing SingleCellExperiment to dreamletProcessedData

Description

For raw counts, estimate precision weights using linear mixed model weighting by number of cells observed for each sample. For normalized data, only weight by number of cells.

Usage

processAssays(
  sceObj,
  formula,
  assays = assayNames(sceObj),
  min.cells = 5,
  min.count = 5,
  min.samples = 4,
  min.prop = 0.4,
  isCounts = TRUE,
  normalize.method = "TMM",
  span = "auto",
  quiet = FALSE,
  weightsList = NULL,
  BPPARAM = SerialParam(),
  ...
)

Arguments

sceObj	SingleCellExperiment object
formula	regression formula for differential expression analysis
assays	array of assay names to include in analysis. Defaults to assayNames(sceObj)
min.cells	minimum number of observed cells for a sample to be included in the analysis
min.count	used to compute a CPM threshold of CPM.cutoff = min.count/median(lib.size)*1e6. Passed to edgeR::filterByExpr()
min.samples	minimum number of samples passing cutoffs for cell cluster to be retained
min.prop	minimum proportion of retained samples with CPM > CPM.cutoff
isCounts	logical, indicating if data is raw counts
normalize.method	normalization method to be used by calcNormFactors
span	Lowess smoothing parameter using by variancePartition::voomWithDreamWeights()
quiet	show messages
weightsList	list storing matrix of precision weights for each cell type. If NULL precision weights are set to 1
BPPARAM	parameters for parallel evaluation
...	other arguments passed to dream

Details

For each cell cluster, samples with at least min.cells are retained. Only clusters with at least min.samples retained samples are kept. Genes are retained if they have at least min.count reads in at least min.prop fraction of the samples. Current values are reasonable defaults, since genes that don't pass these cutoffs are very underpowered for differential expression analysis and only increase the multiple testing burden. But values of min.cells = 2 and min.count = 2 are also reasonable to include more genes in the analysis.

The precision weights are estimated using the residuals fit from the specified formula. These weights are robust to changes in the formula as long as the major variables explaining the highest fraction of the variance are included.

If weightsList is NULL, precision weights are set to 1 internally.

Value

Object of class dreamletProcessedData storing voom-style normalized expression data

See Also

voomWithDreamWeights()

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)
#

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