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