Extract Dataset Dimensional Properties and Summary Statistics

Computes key dimensional and biological properties from single-cell or multiomics count matrices, including cell counts, feature counts, sparsity percentage, biological group/cell type counts, batch counts, median library size, median detected features per cell, and mean expression level.

Usage

extract_dataset_summary(
  mat,
  role = "Reference",
  method_name = "Dataset",
  modality = "scRNA-seq",
  cell_types = NULL,
  batch_info = NULL
)

Arguments

mat

A count matrix (genes/features x cells), dgCMatrix, SingleCellExperiment, or Seurat object.

role

Character string describing the role of the dataset (e.g. "Biological Reference" or "Simulated"). Default is "Reference".

method_name

Character string indicating the method or dataset name (e.g. "Empirical Reference", "Splatter"). Default is "Dataset".

modality

Character string describing the molecular modality (e.g. "scRNA-seq", "scATAC-seq"). Default is "scRNA-seq".

cell_types

Optional factor or character vector of cell type annotations.

batch_info

Optional factor or character vector of batch annotations.

Value

A data.frame with 1 row summarizing the dataset properties.

Examples

data(example_scrna, package = "scSimEval")
extract_dataset_summary(example_scrna$ref, role = "Reference", method_name = "Empirical")
#>   Dataset / Simulator      Role  Modality Cells (N) Features (P) Sparsity
#> 1           Empirical Reference scRNA-seq        80           60   17.92%
#>   Cell Types (Groups) Batches Median Lib Size Median Detected Features
#> 1       Not specified       1           257.0                       49
#>   Mean Expression
#> 1           4.272