R: Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation

# Adaptive Machine Learning-Powered, Context-Matching Tool for Single-Cell and Spatial Transcriptomics Annotation

## Documentation for package ‘SlimR’ version 1.1.3

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

calculate_cluster_variability | Calculate Cluster Variability (Use in package)
calculate_expression | Counts average expression of gene set (Use in package)
calculate_expression_skewness | Calculate Expression Distribution Skewness (Use in package)
calculate_probability | Calculate gene set expression and infer probabilities with control datasets (Use in package)
Cellmarker2 | Cellmarker2 dataset
Cellmarker2_raw | Cellmarker2 raw dataset
Cellmarker2_table | Cellmarker2 table
Celltype_Annotation | Annotate Seurat Object with SlimR Cell Type Predictions
Celltype_annotation_Cellmarker2 | Uses "marker_list" from Cellmarker2 for cell annotation
Celltype_Annotation_Combined | Uses "marker_list" to generate combined plot for cell annotation
Celltype_annotation_Excel | Uses "marker_list" from Excel input for cell annotation
Celltype_Annotation_Features | Annotate cell types using features plot with different marker databases
Celltype_Annotation_Heatmap | Uses "marker_list" to generate heatmap for cell annotation
Celltype_annotation_PanglaoDB | Uses "marker_list" from PanglaoDB for cell annotation
Celltype_Annotation_PerCell | Annotate Seurat Object with Per-Cell SlimR Predictions
Celltype_annotation_Seurat | Uses "marker_list" from Seurat object for cell annotation
Celltype_Calculate | Uses "marker_list" to calculate probability, prediction results, AUC and generate heatmap for cell annotation
Celltype_Calculate_PerCell | Per-cell annotation using marker expression and optional UMAP spatial smoothing
Celltype_Verification | Perform cell type verification and generate the validation dotplot
Celltype_Verification_PerCell | Verify per-cell annotations with marker expression dotplot
compute_adaptive_parameters | Compute Adaptive Parameters Based on Dataset Features (Use in package)
estimate_batch_effect | Estimate Batch Effect Strength (Use in package)
extract_dataset_features | Extract Dataset Characteristics for Adaptive Parameter Calculation (Use in package)
Markers_filter_Cellmarker2 | Create Marker_list from the Cellmarkers2 database
Markers_filter_PanglaoDB | Create Marker_list from the PanglaoDB database
Markers_filter_ScType | Create Marker_list from the ScType database
Markers_list_PCTAM | List of Macrophage subtype markers in the article "Macrophage diversity in cancer revisited in the era of single-cell omics"
Markers_list_PCTIT | List of T cell subtype markers in the article "Pan-cancer single cell landscape of tumor-infiltrating T cells"
Markers_list_scIBD | List of cell type markers in the article scIBD
Markers_list_TCellSI | List of T cell subtype markers in the article TCellSI
PanglaoDB | PanglaoDB dataset
PanglaoDB_raw | PanglaoDB raw dataset
PanglaoDB_table | PanglaoDB table
Parameter_Calculate | Adaptive Parameter Tuning for Single-Cell Data Annotation in SlimR
percell_workflow | Per-Cell Annotation Workflow Example
plot.pheatmap | Plot Method for pheatmap Objects
Read_excel_markers | Create "Marker_list" from Excel files ".xlsx"
Read_seurat_markers | Create "Marker_list" from Seurat object
ScType | ScType dataset
ScType_raw | ScType raw dataset
ScType_table | ScType metadata table