scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

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 paper in Genome Biology:

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

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

You can install scPred via devtools as follows:

devtools::install_github("powellgenomicslab/scPred")