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Downsample datasets

Source: R/downsample.R
downsample.Rd

This function mainly aims at downsampling datasets to a size suitable for plotting or expensive in-memmory calculation.

Users can balance the sample size of categories of interests with balance. Multi-variable specification to balance is supported, so that at most maxCells cells will be sampled from each combination of categories from the variables. For example, when two datasets are presented and three clusters labeled across them, there would then be at most \(2 \times 3 \times maxCells\) cells being selected. Note that "dataset" will automatically be added as one variable when balancing the downsampling. However, if users want to balance the downsampling solely basing on dataset origin, users have to explicitly set balance = "dataset".

Usage

downsample(
  object,
  balance = NULL,
  maxCells = 1000,
  useDatasets = NULL,
  seed = 1,
  returnIndex = FALSE,
  ...
)

Arguments

object

liger object

balance

Character vector of categorical variable names in cellMeta slot, to subsample maxCells cells from each combination of all specified variables. Default NULL samples maxCells cells from the whole object.

maxCells

Max number of cells to sample from the grouping based on balance.

useDatasets

Index selection of datasets to include Default NULL for using all datasets.

seed

Random seed for reproducibility. Default 1.

returnIndex

Logical, whether to only return the numeric index that can subset the original object instead of a subset object. Default FALSE.

...

Arguments passed to subsetLiger, where cellIdx is occupied by internal implementation.

Value

By default, a subset of liger

object. Alternatively when returnIndex = TRUE, a numeric vector to be used with the original object.

Examples

# Subsetting an object
pbmc <- downsample(pbmc)
#> ℹ Subsetting dataset: "ctrl"
#> ℹ Subsetting dataset: "stim"
#> ✔ Subsetting dataset: "stim" ... done
#> 
#> ℹ Subsetting dataset: "ctrl"

#> ✔ Subsetting dataset: "ctrl" ... done
#> 
# Creating a subsetting index
sampleIdx <- downsample(pbmcPlot, balance = "leiden_cluster",
                        maxCells = 10, returnIndex = TRUE)
plotClusterDimRed(pbmcPlot, cellIdx = sampleIdx)
#> ℹ Plotting feature "leiden_cluster" on 158 cells
#> ✔ Plotting feature "leiden_cluster" on 158 cells ... done
#>