extract_dataset_features: Extract Dataset Characteristics for Machine Learning (Use in...

In SlimR: Machine Learning-Assisted, Marker-Based Tool for Single-Cell and Spatial Transcriptomics Annotation

Extract Dataset Characteristics for Machine Learning (Use in package)

Description

Computes various statistical features from single-cell data that are used as input for the parameter prediction model.

Usage

extract_dataset_features(
  seurat_obj,
  features,
  assay = NULL,
  cluster_col = NULL
)

Arguments

seurat_obj	Seurat object
features	Features to analyze
assay	Assay name
cluster_col	Cluster column name

Value

List of dataset characteristics including expression statistics, variability measures, and cluster properties

See Also

Other Section_1_Functions_Use_in_Package: calculate_cluster_variability(), calculate_expression(), calculate_expression_skewness(), calculate_probability(), estimate_batch_effect(), generate_training_data(), postprocess_parameters(), predict_optimal_parameters(), train_parameter_model()

SlimR documentation built on Dec. 17, 2025, 5:09 p.m.