Home

SingleCellNet predicts the cell type of query scRNA-seq data by Random forest multi-class classification. See Tan 2019 for more details. It was originally written in R. PySingleCellNet (PySCN) is the Python version, and it is compatible with Scanpy. PySCN was crafted to aid in the analysis of engineered cell populations (i.e. cells derived via directed differentiation of pluripotent stem cells or via direct conversion), but can just as easily be used to perform cell typing on data dervived from other sources as long as adequate training data is available.

PySCN returns both cell type label predictions, and it annotates those predictions as either 'Singular', 'Intermediate', 'Hybrid', 'None', or 'Random'.