# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "SlimR" in publications use:' type: software license: MIT title: 'SlimR: Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation' version: 1.1.5 doi: 10.32614/CRAN.package.SlimR abstract: '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'', ''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 .' authors: - family-names: Wang given-names: Zhaoqing email: zhaoqingwang@mail.sdu.edu.cn orcid: https://orcid.org/0000-0001-8348-7245 repository: https://zhaoqing-wang.r-universe.dev repository-code: https://github.com/zhaoqing-wang/SlimR commit: b7d9013c4200291ca8470676416aca18a84fff7b url: https://github.com/zhaoqing-wang/SlimR date-released: '2026-06-03' contact: - family-names: Wang given-names: Zhaoqing email: zhaoqingwang@mail.sdu.edu.cn orcid: https://orcid.org/0000-0001-8348-7245