SlimR is an R package designed for annotating single-cell and spatial-transcriptomics (ST) datasets. It supports the creation of a unified marker list,Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI'), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
SlimR can predict calculation parameters by machine learning algorithms (e.g., 'Random Forest', 'Gradient Boosting', 'Support Vector Machine', 'Ensemble Learning') byParameter_Calculate(), and based onMarkers_list, calculate gene expression of different cell types and predict annotation information and calculate corresponding AUC byCelltype_Calculate(), and annotate it byCelltype_Annotation(), then verify it byCelltype_Verification(). 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').

SlimR version: v1.0.8

• This version adds the function of machine learning (e.g., 'Random Forest', 'Gradient Boosting', 'Support Vector Machine', 'Ensemble Learning') for cell types probability calculation parameter recognition.
• Optimize the data filter mode of "Markers_list_scIBD" in the package, and filter throughsort_by = "logFC" andgene_filter = 20 parameter.
• Adjust the calculation process of the 'FSS' value in theread_seurat_markers() function when 'resources' is set to 'presto'.
• Optimize the prompt output during the execution of theCelltype_Verification() function.
• Modify and optimize the README and NEWS files.

SlimR is an R package designed for annotating single-cell and spatial-transcriptomics (ST) datasets. It supports the creation of a unified marker list, Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI'), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
Based on the Markers_list, SlimR can calculate gene expression of different cell types and predict annotation information and calculate corresponding AUC by 'Celltype_Calculate()', and annotate it by 'Celltype_Annotation()', then verify it by 'Celltype_Verification()'. At the same time, it can calculate gene expression corresponding to the cell type to generate the corresponding annotation reference map for manual annotation (e.g., 'Heatmap', 'Features plot', 'Combined plot').

SlimR version: v1.0.7

• Added new functionCelltype_Verification() for predicted cell types validation and generate the validation dotplot.
• Optimize the function 'Read_seurat_markers()'. This is compatible with the 'presto::wilcoxauc()' source tag, and the 'FSS' (product of 'log2FC' and 'expression ratio') can be calculated and sorted accordingly.
• Add custom color parameterscolour_low andcolour_high to all ploting output functions.
• RenamedCelltype_annotation_Dotplot() toCelltype_Annotation_Features(),Celltype_annotation_Box() toCelltype_Annotation_Combined(),read_seurat_markers() toRead_seurat_markers(),read_excel_markers() toRead_excel_markers() for unified function naming structure.
• Enhanced README with detailed process descriptions.
• Optimized message output system for cleaner console feedback.
• Resolved various code bugs reported by users.
• Modified codebase to meet CRAN standards and policies.

Annotating single-cell and spatial-transcriptomic (ST) data based on the Marker dataset. It supports the creation of a unified marker list, Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD'， 'TCellSI'), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
Based on the Markers_list, SlimR can calculate gene expression of different cell types and predict annotation information and calculate AUC ('Celltype_Calculate') with one click, and annotate it ('Celltype_Annotation'). At the same time, it can calculate gene expression corresponding to the cell type to generate the corresponding annotation reference map for manual annotation (for example, 'Annotation_heatmap', 'Annotation Dot Plot', 'Annotation Box plot').

SlimR version: v1.0.6

• Integrated "scIBD" human intestine reference database.
• Added AUC calculation and visualization toCelltype_Calculate().
• Implemented AUC-based prediction correction in cell typing.
• Streamlined code output formatting.
• Fixed critical bugs in prediction pipeline.
• Modified code to meet CRAN submission requirements.

Annotating single-cell and spatial-transcriptomic (ST) data based on the Marker dataset. It supports the creation of a unified marker list, Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., 'Cellmarker2', 'PanglaoDB', 'TCellSI'), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
Based on the Markers_list, SlimR can calculate gene expression of different cell types and predict annotation information ('Celltype_Calculate') with one click, and annotate it ('Celltype_Annotation'). At the same time, it can calculate gene expression corresponding to the cell type to generate the corresponding annotation reference map for manual annotation (for example, 'Annotation_heatmap', 'Annotation Dot Plot', 'Annotation Box plot').

SlimR version: v1.0.5

• Added "TCellSI" T-cell reference database.
• IntroducedCelltype_Calculate() for automated scoring.
• AddedCelltype_Annotation() for end-to-end cell typing.
• Improved message output system.
• Resolved multiple code errors.
• Modified code to meet CRAN standards and policies.

Annotating single-cell and spatial-transcriptomic (ST) data based on the Marker dataset. It supports the creation of a unified marker list, Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., 'Cellmarker2', 'PanglaoDB'), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
Based on the Markers_list, 'SlimR' can iterate through different cell types to generate corresponding annotation reference plots (e.g., 'Markers_Dotplot', 'Metric_Heatmap', 'Markers_Box_plot'). Furthermore, it enables one-click generation of an annotation heatmap ('Annotation_Heatmap') visualizing the relationship between input cell types and the reference marker list.

SlimR version: v1.0.4

• OptimizedCelltype_annotation_Heatmap() performance.
• Enhanced probability calculation incalculate_probability().
• Modified code to meet CRAN submission requirements.
• Change the License type from "GPL-3" to "MIT".

SlimR is an R package designed for annotating single-cell and spatial transcriptomics datasets. It supports the creation of a unified marker list ("Markers_list") using multiple sources including: user-provided Excel tables mapping cell types to markers, Seurat objects containing cell label information, and the package's built-in curated species-specific cell type and marker reference databases (e.g., Cellmarker2, PanglaoDB).

Based on the "Markers_list", SlimR enables one-click generation of annotation heatmaps ("Annotation_heatmap") visualizing relationships between input cell types and reference marker lists. Additionally, it can iterate through different cell types to generate corresponding annotation reference plots (e.g., Markers_dotplot, Metric_heatmap, Mean_expression_bar_plot).

SlimR version: v1.0.1

• ChangedCelltype_annotation_Bar() toCelltype_annotation_Box() with improved visualization capabilities.

SlimR is an R package designed for annotating single-cell and spatial transcriptomics datasets. It supports the creation of a unified marker list ("Markers_list") using multiple sources including: user-provided Excel tables mapping cell types to markers, Seurat objects containing cell label information, and the package's built-in curated species-specific cell type and marker reference databases (e.g., Cellmarker2, PanglaoDB).

Based on the "Markers_list", SlimR enables one-click generation of annotation heatmaps ("Annotation_heatmap") visualizing relationships between input cell types and reference marker lists. Additionally, it can iterate through different cell types to generate corresponding annotation reference plots (e.g., Markers_dotplot, Metric_heatmap, Mean_expression_bar_plot).

The first program version：SlimR v1.0.0

• Initial release of SlimR package with core cell type annotation framework and basic visualization functions.

Provide an R software toolkit for annotating single-cell and spatial-transcriptomic data based on the Marker dataset. It supports the creation of a unified marker list, Markers_list, using sources including: the package's built-in curated species-specific cell type and marker reference databases (e.g., ‘Cellmarker2’, ‘PanglaoDB’), Seurat objects containing cell label information, or user-provided Excel tables mapping cell types to markers.
Based on the Markers_list, ‘SlimR’ can iterate through different cell types to generate corresponding annotation reference plots (e.g., ‘Markers_Dotplot’, ‘Metric_Heatmap’, ‘Mean_expression_Box_plot’). Furthermore, it enables one-click generation of an annotation heatmap (‘Annotation_Heatmap’) visualizing the relationship between input cell types and the reference marker list.

SlimR version: v1.0.3

• Replacedcalculate_mean_expression() withcalculate_probability() inCelltype_annotation_Heatmap().
• Modified code to meet CRAN standards and policies.