estimate_batch_effect

Roughly estimates the potential impact of batch effects
using available metadata.

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

estimate_batch_effect function

<dl><dt>seurat_obj</dt>
<dd>Seurat object</dd>
<dt>assay</dt>
<dd>Assay name</dd></dl>

Arguments

Estimate Batch Effect Strength (Use in package) — estimate_batch_effect

<dl>

<dt>seurat_obj</dt>
<dd>Seurat object</dd>


<dt>assay</dt>
<dd>Assay name</dd>

</dl>

estimate_batch_effect: Estimate Batch Effect Strength (Use in package)

Description

Usage

Value

Arguments

See Also