Transformer for one stop interpretable cell type annotation

Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and…

• NN
  National Natural Science Foundation of China

  Awards: 32088101, 2017SHZDZX01, 92049302
• MO
  Ministry of Science and Technology of the People's Republic of China

  Award: 2020YFA0804000
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 2017SHZDZX01