xiaoyingshi/SELINAr: Single-cell Assignment using Multiple-Adversarial Domain Adaptation Network with Large-scale References

SELINA is a deep learning-based framework for single cell assignment with multiple references. The algorithm consists of three main steps: cell type balancing, pre-training and fine-tuning. The rare cell types in reference data are first oversampled using SMOTE(Synthetic Minority Oversampling Technique), and then the reference data is trained with a supervised deep learning framework using MADA(Multi-Adversarial Domain Adaptation). An autoencoder is subsquently used to fine-tune the parameters of the pre-trained model. Finally, the labels from reference data are transferred to the query data based on the fully-trained model. Along with the annotation algorithm, we also collect 136 datasets which were uniformly processed and curated to provide users with comprehensive pretrained models.

install.packages("remotes")
remotes::install_github("xiaoyingshi/SELINAr")