Processing SingleCellExperiment to dreamletProcessedData

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

Value

Object of class dreamletProcessedData storing voom-style normalized expression data

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.

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)
#>   B cells...
#> 0.19 secs
#>   CD14+ Monocytes...
#> 0.31 secs
#>   CD4 T cells...
#> 0.24 secs
#>   CD8 T cells...
#> 0.13 secs
#>   FCGR3A+ Monocytes...
#> 0.27 secs

# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(res.proc, ~group_id)
#>   B cells...
#> 0.2 secs
#>   CD14+ Monocytes...
#> 0.26 secs
#>   CD4 T cells...
#> 0.21 secs
#>   CD8 T cells...
#> 0.13 secs
#>   FCGR3A+ Monocytes...
#> 0.25 secs
#