SlimR: Machine Learning-Assisted, Marker-Based Tool for Single-Cell and Spatial Transcriptomics Annotation

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', 'PCTIT', 'PCTAM'), Seurat objects, or user-supplied Excel files. SlimR can predict calculation 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).

install.packages("SlimR")

Try the SlimR package in your browser