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Per-sample variance of single-cell counts

Source: R/aggregateVar.R
aggregateVar.Rd

Aggregation function for single-cell log-normalized counts to calculate per-sample variance for dreamlet.

Usage

aggregateVar(
  sce,
  assay = NULL,
  cluster_id = NULL,
  sample_id = NULL,
  min.cells = 10,
  min.var = 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]).

cluster_id

character string specifying which variable to use as cluster id

sample_id

character string specifying which variable to use as sample id

min.cells

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

min.var

minimum variance for a gene to be considered expressed in a sample

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 model gene expression variance. In this case, a per-sample per-gene variance is calculated across all cells from a given sample and cell type. Here aggregateVar() performs the roles of aggregateToPseudoBulk() followed by processAssays() but using log-normalized 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.var 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 / sqrt(n) and n is the number of cells.

Examples

library(muscat)
library(SingleCellExperiment)

data(example_sce)

# Compute variance for each sample and cell cluster
pbVar <- aggregateVar(example_sce,
  assay = "counts",
  cluster_id = "cluster_id",
  sample_id = "sample_id",
  verbose = FALSE
)