calculate_expression_skewness

Computes the average skewness of gene expression distributions
across all features.

Annotates single-cell and spatial-transcriptomic (ST) data using context-matching marker datasets. It creates a unified marker list (`Markers_list`) from multiple sources: built-in curated databases ('Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI', 'PCTIT', 'PCTAM'), Seurat objects with cell labels, or user-provided Excel tables. SlimR first uses adaptive machine learning for parameter optimization, and then offers two automated annotation approaches: 'cluster-based' and 'per-cell'. Cluster-based annotation assigns one label per cluster, expression-based probability calculation, and AUC validation. Per-cell annotation assigns labels to individual cells using three scoring methods with adaptive thresholds and ratio-based confidence filtering, plus optional UMAP spatial smoothing, making it ideal for heterogeneous clusters and rare cell types. The package also supports semi-automated workflows with heatmaps, feature plots, and combined visualizations for manual annotation. 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

calculate_expression_skewness function

<dl><dt>expression_matrix</dt>
<dd>Matrix of expression values</dd></dl>

Arguments

Calculate Expression Distribution Skewness (Use in package) — calculate_expression_skewness

<dl>

<dt>expression_matrix</dt>
<dd>Matrix of expression values</dd>

</dl>

calculate_expression_skewness: Calculate Expression Distribution Skewness (Use in package)

Description

Usage

Value

Arguments

See Also