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Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Yuansong Zeng, Xiang Zhou, Jiahua Rao, Yutong Lu, View ORCID ProfileYuedong Yang
doi: https://doi.org/10.1101/2020.09.02.278804
Yuansong Zeng
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Xiang Zhou
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Jiahua Rao
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Yutong Lu
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
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Yuedong Yang
1School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
2Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, China
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  • ORCID record for Yuedong Yang
  • For correspondence: yangyd25@mail.sysu.edu.cn
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Abstract

Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great opportunity to study gene expression at cellular resolution, and the scRNA-seq data has been routinely conducted to unfold cell heterogeneity and diversity. A critical step for the scRNA-seq analyses is to cluster the same type of cells, and many methods have been developed for cell clustering. However, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed a new method (GraphSCC) to cluster cells based on scRNA-seq data by accounting structural relations between cells through a graph convolutional network. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. Extensive experiments indicate that GraphSCC model outperforms state-of-the-art methods in various evaluation metrics on both simulated and real datasets. Further visualizations show that GraphSCC provides representations for better intra-cluster compactness and inter-cluster separability.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵* yangyd25{at}mail.sysu.edu.cn; yutong.lu{at}nscc-gz.cn

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 03, 2020.
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Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network
Yuansong Zeng, Xiang Zhou, Jiahua Rao, Yutong Lu, Yuedong Yang
bioRxiv 2020.09.02.278804; doi: https://doi.org/10.1101/2020.09.02.278804
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Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network
Yuansong Zeng, Xiang Zhou, Jiahua Rao, Yutong Lu, Yuedong Yang
bioRxiv 2020.09.02.278804; doi: https://doi.org/10.1101/2020.09.02.278804

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