Machine Learning-Assisted, Marker-Based Tool for Single-Cell and Spatial Transcriptomics Annotation
Calculate Cluster Variability
Calculate Expression Distribution Skewness
Counts average expression of gene set (Use in package)
Calculate gene set expression and infer probabilities with control dat...
Uses "marker_list" from Cellmarker2 for cell annotation
Uses "marker_list" to generate combined plot for cell annotation
Uses "marker_list" from Excel input for cell annotation
Annotate cell types using features plot with different marker database...
Uses "marker_list" to generate heatmap for cell annotation
Uses "marker_list" from PanglaoDB for cell annotation
Uses "marker_list" from Seurat object for cell annotation
Annotate Seurat Object with SlimR Cell Type Predictions
Uses "marker_list" to calculate probability, prediction results, AUC a...
Perform cell type verification and generate the validation dotplot
Estimate Batch Effect Strength
Extract Dataset Characteristics for Machine Learning
Generate Training Data for Machine Learning Model
Create Marker_list from the Cellmarkers2 database
Create Marker_list from the PanglaoDB database
Adaptive Parameter Tuning for Single-Cell Data Annotation in SlimR
Post-process Predicted Parameters
Predict Optimal Parameters Using Trained Model
Create "Marker_list" from Excel files ".xlsx"
Create "Marker_list" from Seurat object
Train Parameter Prediction Model
Annotates single-cell and spatial-transcriptomic (ST) data using marker datasets. Supports unified markers list ('Markers_list') creation from built-in databases (e.g., 'Cellmarker2', 'PanglaoDB', 'scIBD', 'TCellSI'), Seurat objects, or user-supplied Excel files. SlimR can predict calculate parameters by machine learning algorithms (e.g., 'Random Forest', 'Gradient Boosting', 'Support Vector Machine', 'Ensemble Learning'), and based on Markers_list, calculate gene expression of different cell types and predict annotation information and calculate corresponding AUC and annotate it, then verify it. 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'). For more details see Kabacoff (2020, ISBN:9787115420572).