random_strata_cells: Stratified Random Sampling of Cells

In IOBR: Immune Oncology Biological Research

Stratified Random Sampling of Cells

Description

Performs stratified random sampling of cells from single-cell data, ensuring proportional representation of each cell type while respecting minimum and maximum count constraints.

Usage

random_strata_cells(
  input,
  group,
  proportion = 0.1,
  minimum_count_include = 300,
  minimum_count = 200,
  maximum_count = 1000,
  sub_cluster = NULL,
  cell_type = NULL
)

Arguments

input	A data frame or Seurat object containing cell annotations.
group	Character string specifying the column name for cell type grouping.
proportion	Numeric value between 0 and 1 specifying the sampling proportion. Default is 0.1.
minimum_count_include	Integer specifying the minimum count threshold for a cell type to be included in sampling. Default is 300.
minimum_count	Integer specifying the minimum number of cells to sample per cell type. Default is 200.
maximum_count	Integer specifying the maximum number of cells to sample per cell type. Default is 1000.
sub_cluster	Optional character string specifying a sub-cluster column for filtering. Default is NULL.
cell_type	Optional character string specifying the cell type value to filter when 'sub_cluster' is provided. Default is NULL.

Value

A data frame containing the sampled cells with preserved cell type proportions.

Examples

# Create simulated cell annotation data
set.seed(123)
sim_cells <- data.frame(
  cell_id = paste0("Cell", 1:500),
  cell_type = sample(c("T_cell", "B_cell", "NK_cell", "Macrophage"), 500, replace = TRUE)
)
# Sample cells with stratified random sampling
sampled <- random_strata_cells(
  input = sim_cells,
  group = "cell_type",
  proportion = 0.2,
  minimum_count_include = 50,
  minimum_count = 20,
  maximum_count = 100
)
if (!is.null(sampled)) head(sampled)

IOBR documentation built on May 30, 2026, 5:07 p.m.