Update README.md

Author: multitalk

README.md

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 ### Cell-type Annotation 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. It’s the first time that GNN is introduced into scRNA-seq studies and demonstrate its ground-breaking performances in this application scenario. 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.](https://www.biorxiv.org/content/10.1101/2020.05.13.094953v1)
+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.](https://www.biorxiv.org/content/10.1101/2020.05.13.094953v1)
 
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