Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation R package SlimR version 1.1.8 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', 'ScType', 'CellTypist', '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 information, see the package documentation at <https://github.com/zhaoqing-wang/SlimR>. Software Depends: R (>= 4.1.0) Imports: cowplot, dplyr, ggplot2, patchwork, pheatmap, readxl, scales, Seurat, tidyr, tools, tibble, gtools Suggests: crayon, RANN, testthat (>= 3.0.0), WeightedTreemaps Zhaoqing Wang <zhaoqingwang@mail.sdu.edu.cn> Comprehensive R Archive Network (CRAN) Zhaoqing Wang [aut, cre] (ORCID: <https://orcid.org/0000-0001-8348-7245>) MIT + file LICENSE (https://CRAN.R-project.org/package=SlimR/LICENSE) 2026-07-31 application/tgz https://CRAN.R-project.org/package=SlimR doi:10.32614/CRAN.package.SlimR