generate_training_data: Generate Training Data for Machine Learning Model (Use in...

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

Generate Training Data for Machine Learning Model (Use in package)

Description

Creates synthetic training data based on empirical rules about optimal parameter relationships with dataset characteristics.

Usage

generate_training_data(dataset_features, n_samples = 1000)

Arguments

dataset_features	List of actual dataset characteristics
n_samples	Number of synthetic samples to generate

Value

Data frame with synthetic features and optimal parameter targets

See Also

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

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