Package index • scLearn

Quality Control

Functions for Quality Control

Cell_qc(): Single-Cell RNA-Seq Data Quality Control

Cell_type_filter(): Filter Cell Types by Minimum Cell Count

Model Training

Functions to Model Training

runLMNN(): Triplet Loss-Based Metric Learning for Single-Cell Data

runDCA(): Discriminative Component Analysis (DCA) Transformation

runNCA(): Neighborhood Components Analysis (NCA) Transformation

runITML(): Information-Theoretic Metric Learning (ITML) for Single-Cell Data

runMMC(): Maximum Margin Clustering (MMC) Transformation (Memory-Optimized)

runMSL(): Multi Similarity Loss-Based Metric Learning for Single-Cell Data

scLearn_model_learning(): Train scLearn Reference Model with Multiple Metric Learning Options

scLearn_cell_assignment(): Cell Type Prediction Using scLearn Model

correlation(): Calculate Sample Similarity Matrix

Example data

RefCellData: Baron et al. (2016) Human Pancreas Single-Cell RNA-Seq Reference Data

QueryCellData: Muraro et al. (2016) Human Pancreas Single-Cell RNA-Seq Query Data

Feature Selection

Functions for advanced feature selection HVGs

Feature_selection_M3Drop(): Feature Selection Using M3Drop

Visualization

Functions for Visualization

sankey_plot(): Generate Sankey Diagram for Cell Type Projection Results

DrawCluster(): Visualize Clusters Using t-SNE or UMAP