Recent advance in single-cell RNA sequencing (scRNA-seq) has enabled large-scale transcriptional characterization of thousands of cells in multiple complex tissues, in which accurate cell type identification becomes the prerequisite and vital step for scRNA-seq studies.

To addresses this challenge, we developed a pre-trained cell-type annotation method, namely scDeepSort, using a state-of-the-art deep learning algorithm, i.e. a modified graph neural network (GNN) model. In brief, scDeepSort was constructed based on our weighted GNN framework and was then learned in two embedded high-quality scRNA-seq atlases containing 764,741 cells across 88 tissues of human and mouse, which are the most comprehensive multiple-organs scRNA-seq data resources to date. For more information, please refer to a preprint in bioRxiv 2020.05.13.094953.

Download scDeepSort-v1.0-cu102.tar.gz from the release page and execute the following command:

pip install scDeepSort-v1.0-cu102.tar.gz

The test single-cell transcriptomics csv data file should be pre-processed by first revising gene symbols according to NCBI Gene database updated on Jan. 10, 2020, wherein unmatched genes and duplicated genes will be removed. Then the data should be normalized with the defalut LogNormalize method in Seurat (R package), detailed in pre-process.R.

• Predict using pre-trained models DeepSortPredictor

• Train your own model and predict DeepSortClassifier

Please refer to the document of scDeepSort for detailed guidence using scDeepSort as a python package.

scDeepSort manuscript is under major revision. Should you have any questions, please contact Xin Shao at xin_shao@zju.edu.cn, Haihong Yang at capriceyhh@zju.edu.cn, or Xiang Zhuang at 3160105000@zju.edu.cn