How zhaoqing-wang/slimr works
SlimR is an R package for marker-based cell-type annotation in single-cell and spatial transcriptomics. Its main workflow standardizes or filters marker inputs, derives data-adaptive parameters, then assigns cell types either by cluster or per cell; results can be reviewed with semi-automated plots. The README also documents mapping, gene-level evaluation, and visualization utilities. Relationships here follow those documented workflows and capabilities; source excerpts were unavailable, so internal call ownership and finer wiring are not asserted.
Main entry points: Probability_parameter.R Adaptive parameters · Celltype_Calculate.R Cluster scoring · Celltype_Calculate_PerCell.R Per-cell scoring · Markers_filter_Cellmarker2.R Filter CellMarker2
Components 27
Marker Inputs
- CellMarker2
R/Database_Cellmarker2.R - PanglaoDB
R/Database_PanglaoDB.R - ScType
R/Database_ScType.R - CellTypist atlas
R/Database_CellTypist.R - Built-in marker lists
R/Database_Markers_list_PCTAM.R - Filter CellMarker2
R/Markers_filter_Cellmarker2.R - Filter PanglaoDB
R/Markers_filter_PanglaoDB.R - Filter ScType
R/Markers_filter_ScType.R - Marker import
R/Read_excel_markers.R - Seurat marker import
R/Read_seurat_markers.R
Annotation
- Adaptive parameters
R/Probability_parameter.R - Cluster scoring
R/Celltype_Calculate.R - Cluster annotation
R/Celltype_Annotation.R - Per-cell scoring
R/Celltype_Calculate_PerCell.R - Per-cell annotation
R/Celltype_Annotation_PerCell.R
Review and Analysis
- Annotation verification
R/Celltype_Verification.R - Annotation comparison
R/Celltype_Compare.R - Gene AUC/ROC
R/Compute_Gene_AUC_ROC.R
Visualization
- Feature plots
R/Celltype_Annotation_Features.R - Annotation heatmap
R/Celltype_Annotation_Heatmap.R - Combined plots
R/Celltype_Annotation_Combined.R - Hierarchy proportions
R/Plot_Hierarchy_Proportion.R - Weighted Voronoi
R/Plot_Voronoi_Diagram.R - Grouped gene heatmap
R/Plot_Group_Gene_Heatmap.R - Colour palettes
R/paletteDiscrete.R
Other components
- Analyst
- Seurat object
Connections 31
- Analyst → Filter CellMarker2: filters markers
- CellMarker2 → Filter CellMarker2: marker data
- Analyst → Filter PanglaoDB: filters markers
- PanglaoDB → Filter PanglaoDB: marker data
- Analyst → Filter ScType: filters markers
- ScType → Filter ScType: marker data
- Analyst → Marker import: imports markers
- Analyst → Seurat marker import: imports markers
- Seurat object → Seurat marker import: provides markers
- Analyst → Adaptive parameters: sets parameters
- Seurat object → Adaptive parameters: provides data
- Analyst → Cluster scoring: runs scoring
- Seurat object → Cluster scoring: provides expression
- Adaptive parameters → Cluster scoring: parameterizes
- Analyst → Cluster annotation: runs annotation
- Cluster scoring → Cluster annotation: scores candidates
- Analyst → Per-cell scoring: runs scoring
- Seurat object → Per-cell scoring: provides expression
- Adaptive parameters → Per-cell scoring: parameterizes
- Analyst → Per-cell annotation: runs annotation
- Per-cell scoring → Per-cell annotation: scores candidates
- Analyst → Annotation verification: reviews labels
- Analyst → Annotation comparison: compares labels
- Analyst → Feature plots: explores annotations
- Analyst → Annotation heatmap: reviews annotations
- Analyst → Combined plots: reviews annotations
- Analyst → Gene AUC/ROC: evaluates genes
- Analyst → Hierarchy proportions: plots proportions
- Analyst → Weighted Voronoi: plots spatial data
- Analyst → Grouped gene heatmap: plots gene scores
- Analyst → Colour palettes: selects colours