SlimR: Adaptive Machine Learning-Powered, Context-Matching Tool for
Single-Cell and Spatial Transcriptomics Annotation
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).
| Version: |
1.1.1 |
| Depends: |
R (≥ 3.5) |
| Imports: |
cowplot, dplyr, ggplot2, patchwork, pheatmap, readxl, scales, Seurat, tidyr, tools, tibble |
| Suggests: |
crayon, RANN, testthat (≥ 3.0.0) |
| Published: |
2026-02-05 |
| DOI: |
10.32614/CRAN.package.SlimR |
| Author: |
Zhaoqing Wang 
[aut, cre] |
| Maintainer: |
Zhaoqing Wang <zhaoqingwang at mail.sdu.edu.cn> |
| BugReports: |
https://github.com/zhaoqing-wang/SlimR/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/zhaoqing-wang/SlimR |
| NeedsCompilation: |
no |
| Materials: |
README, NEWS |
| CRAN checks: |
SlimR results |
Documentation:
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