compute_adaptive_parameters

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

Annotates single-cell and spatial-transcriptomic (ST) data using marker datasets. Supports unified markers list ('Markers_list') creation from built-in databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI', 'PCTIT', 'PCTAM'), Seurat objects, or user-supplied Excel files. SlimR can predict calculation parameters by adaptive machine learning algorithms, and based on Markers_list, calculate gene expression of different cell types and predict annotation information, and calculate corresponding AUC and annotate it, then verify it. At the same time, it can calculate gene expression corresponding to the cell type to generate a reference map for manual annotation (e.g., 'Heat Map', 'Feature Plots', 'Combined Plots'). For more details, see Kabacoff (2020, ISBN:9787115420572).

Zhao qing Wang

SlimR

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

Zhaoqing Wang

compute_adaptive_parameters function

<dl><dt>dataset_features</dt>
<dd>List of dataset characteristics from extract_dataset_features()</dd>
<dt>n_celltypes</dt>
<dd>Expected number of cell types in marker database</dd></dl>

Arguments

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

<dl>

<dt>dataset_features</dt>
<dd>List of dataset characteristics from extract_dataset_features()</dd>


<dt>n_celltypes</dt>
<dd>Expected number of cell types in marker database</dd>

</dl>

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

Description

Usage

Value

Arguments

See Also