{
  "_id": "6a441a2e31091564a7deeeb8",
  "Package": "SlimR",
  "Version": "1.1.6",
  "Title": "Adaptive Machine Learning-Powered, Context-Matching Tool for\nSingle-Cell and Spatial Transcriptomics Annotation",
  "Description": "Annotates single-cell and spatial-transcriptomic (ST) data\nusing context-matching marker datasets. It creates a unified\nmarker list (`Markers_list`) from multiple sources: built-in\ncurated databases ('Cellmarker2', 'PanglaoDB', 'ScType',\n'scIBD', 'TCellSI', 'PCTIT', 'PCTAM'), Seurat objects with cell\nlabels, or user-provided Excel tables. SlimR first uses\nadaptive machine learning for parameter optimization, and then\noffers two automated annotation approaches: 'cluster-based' and\n'per-cell'. Cluster-based annotation assigns one label per\ncluster, expression-based probability calculation, and AUC\nvalidation. Per-cell annotation assigns labels to individual\ncells using three scoring methods with adaptive thresholds and\nratio-based confidence filtering, plus optional UMAP spatial\nsmoothing, making it ideal for heterogeneous clusters and rare\ncell types. The package also supports semi-automated workflows\nwith heatmaps, feature plots, and combined visualizations for\nmanual annotation. For more information, see the package\ndocumentation at <https://github.com/zhaoqing-wang/SlimR>.",
  "Authors@R": "person(given = \"Zhaoqing\",family = \"Wang\",\nemail = c(\"zhaoqingwang@mail.sdu.edu.cn\"),\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-8348-7245\"))",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/zhaoqing-wang/SlimR",
  "BugReports": "https://github.com/zhaoqing-wang/SlimR/issues",
  "Encoding": "UTF-8",
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  "Date": "2026-06-30",
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  "Author": "Zhaoqing Wang [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-8348-7245>)",
  "Maintainer": "Zhaoqing Wang <zhaoqingwang@mail.sdu.edu.cn>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-06-30 19:12:24 UTC",
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    "Celltype_annotation_Seurat",
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    "Celltype_Calculate_PerCell",
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    "Celltype_Verification_PerCell",
    "Compute_Gene_AUC_ROC",
    "Markers_filter_Cellmarker2",
    "Markers_filter_PanglaoDB",
    "Markers_filter_ScType",
    "Parameter_Calculate",
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    "Read_seurat_markers"
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      "title": "Calculate Cluster Variability (Use in package)",
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      "page": "Celltype_Annotation",
      "title": "Annotate Seurat Object with SlimR Cell Type Predictions",
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        "Section_3_Automated_Annotation"
      ],
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        "Celltype_Annotation"
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      "page": "Celltype_annotation_Cellmarker2",
      "title": "Uses \"marker_list\" from Cellmarker2 for cell annotation",
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    {
      "page": "Celltype_Annotation_Combined",
      "title": "Uses \"marker_list\" to generate combined plot for cell annotation",
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      ],
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        "Celltype_Annotation_Combined"
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      "page": "Celltype_annotation_Excel",
      "title": "Uses \"marker_list\" from Excel input for cell annotation",
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        "Section_5_Other_Functions_Provided"
      ],
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        "Celltype_annotation_Excel"
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      "page": "Celltype_Annotation_Features",
      "title": "Annotate cell types using features plot with different marker databases",
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        "Section_4_Semi_Automated_Annotation"
      ],
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        "Celltype_Annotation_Features"
      ]
    },
    {
      "page": "Celltype_Annotation_Heatmap",
      "title": "Uses \"marker_list\" to generate heatmap for cell annotation",
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        "Section_4_Semi_Automated_Annotation"
      ],
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        "Celltype_Annotation_Heatmap"
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    {
      "page": "Celltype_annotation_PanglaoDB",
      "title": "Uses \"marker_list\" from PanglaoDB for cell annotation",
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        "Section_5_Other_Functions_Provided"
      ],
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        "Celltype_annotation_PanglaoDB"
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    {
      "page": "Celltype_Annotation_PerCell",
      "title": "Annotate Seurat Object with Per-Cell SlimR Predictions",
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        "Section_3_Automated_Annotation"
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        "Celltype_Annotation_PerCell"
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    {
      "page": "Celltype_annotation_Seurat",
      "title": "Uses \"marker_list\" from Seurat object for cell annotation",
      "concept": [
        "Section_5_Other_Functions_Provided"
      ],
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        "Celltype_annotation_Seurat"
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    {
      "page": "Celltype_Calculate",
      "title": "Uses \"marker_list\" to calculate probability, prediction results, AUC and generate heatmap for cell annotation",
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        "Celltype_Calculate"
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        "Celltype_Verification"
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        "Celltype_Verification_PerCell"
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      "title": "Compute AUC and Optionally Plot ROC Curve for a Single Gene",
      "concept": [
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        "Compute_Gene_AUC_ROC"
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      "title": "Create Marker_list from the Cellmarkers2 database",
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      "topics": [
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    },
    {
      "page": "Markers_filter_PanglaoDB",
      "title": "Create Marker_list from the PanglaoDB database",
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