postprocess_parameters: Post-process Predicted Parameters (Use in package)

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

Post-process Predicted Parameters (Use in package)

Description

Applies constraints and dataset-specific adjustments to ensure predicted parameters are within reasonable ranges.

Usage

postprocess_parameters(predicted_params, dataset_features)

Arguments

predicted_params	List of raw predicted parameters
dataset_features	Characteristics of current dataset

Value

List of finalized parameters after post-processing

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(), generate_training_data(), predict_optimal_parameters(), train_parameter_model()

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