scDeepSort Documentation¶
scDeepSort: A Pre-trained Cell-type Annotation Method for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network
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.
Reference¶
@article{shao2020reference,
title={scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network},
author={Shao, Xin and Yang, Haihong and Zhuang, Xiang and Liao, Jie and Yang, Penghui and Cheng, Junyun and Lu, Xiaoyan and Chen, Huajun and Fan, Xiaohui},
journal={bioRxiv},
year={2020},
publisher={Cold Spring Harbor Laboratory}
}