Aggregation of single-cell signals

Aggregation of single-cell to pseudobulk data for non-count data.

Usage

aggregateNonCountSignal(
  sce,
  assay = NULL,
  sample_id = NULL,
  cluster_id = NULL,
  min.cells = 10,
  min.signal = 0.01,
  min.samples = 4,
  min.prop = 0.4,
  verbose = TRUE,
  BPPARAM = SerialParam(progressbar = verbose)
)

Arguments

sce

a SingleCellExperiment.

assay

character string specifying the assay slot to use as input data. Defaults to the 1st available (assayNames(x)[1]).

sample_id

character string specifying which variable to use as sample id

cluster_id

character string specifying which variable to use as cluster id

min.cells

minimum number of observed cells for a sample to be included in the analysis

min.signal

minimum signal value for a gene to be considered expressed in a sample. Proper value for this cutoff depends on the type of signal value

min.samples

minimum number of samples passing cutoffs for cell cluster to be retained

min.prop

minimum proportion of retained samples with non-zero counts for a gene to be

verbose

logical. Should information on progress be reported?

BPPARAM

a BiocParallelParam object specifying how aggregation should be parallelized.

Value

a dreamletProcessedData object

Details

The dreamlet workflow can also be applied to non-count data. In this case, a signal is averaged across all cells from a given sample and cell type. Here aggregateNonCountSignal() performs the roles of aggregateToPseudoBulk() followed by processAssays() but using non-count data.

For each cell cluster, samples with at least min.cells are retained. Only clusters with at least min.samples retained samples are kept. Features are retained if they have at least min.signal in at least min.prop fraction of the samples.

The precision of a measurement is the inverse of its sampling variance. The precision weights are computed as 1/sem^2, where sem = sd(signal) / sqrt(n), signal stores the values averaged across cells, and n is the number of cells.

Examples

library(muscat)
library(SingleCellExperiment)

data(example_sce)

# create pseudobulk for each sample and cell cluster
# using non-count signal
pb.signal <- aggregateNonCountSignal(example_sce,
  assay = "logcounts",
  cluster_id = "cluster_id",
  sample_id = "sample_id",
  verbose = FALSE
)

# Differential expression analysis within each assay,
# evaluated on the voom normalized data
res.dl <- dreamlet(pb.signal, ~group_id)
#>   B cells...
#> 0.34 secs
#>   CD14+ Monocytes...
#> 0.34 secs
#>   CD4 T cells...
#> 0.29 secs
#>   CD8 T cells...
#> 0.33 secs
#>   FCGR3A+ Monocytes...
#> 0.36 secs