compute_adaptive_parameters: Compute Adaptive Parameters Based on Dataset Features (Use in...

In SlimR: Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation

Compute Adaptive Parameters Based on Dataset Features (Use in package)

Description

Calculates optimal min_expression, specificity_weight, and threshold parameters using continuous adaptive algorithms based on dataset characteristics.

Usage

compute_adaptive_parameters(dataset_features, n_celltypes = 50)

Arguments

dataset_features	List of dataset characteristics from extract_dataset_features()
n_celltypes	Expected number of cell types in marker database

Value

List containing min_expression, specificity_weight, threshold, and rationale

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()

SlimR documentation built on Feb. 5, 2026, 5:08 p.m.