scPred: Single cell prediction using singular value decomposition and machine learning classification

scPred is a general method to predict cell types based on variance structure decomposition. It selects the most cell type-informative principal components from a dataset and trains a prediction model for each cell type. The principal training axes are projected onto the test dataset to obtain the PCs scores for the test dataset and the trained model(s) is/are used to classify single cells.

For more details see our pre-print on bioRxiv:

scPred: Single cell prediction using singular value decomposition and machine learning classification

This introduction to scPred shows a basic workflow for cell type prediction.

Authors

• José Alquicira-Hernández
• Quan Nguyen
• Joseph E Powell