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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment

Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
doi: https://doi.org/10.1101/2022.06.20.496916
Feng Zeng
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
2Department of Neuroscience, School of Medicine, Xiamen University, Xiamen, Fujian 361102, China
3Department of Neurosurgery, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, Fujian, China
5National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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  • For correspondence: zengfeng@xmu.edu.cn jhan@xmu.edu.cn
Xuwen Kong
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
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Fan Yang
1Department of Automation, Xiamen University, Xiamen, Fujian 361102, China
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Ting Chen
6Institute for Artificial Intelligence, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
7Tsinghua-Fuzhou Institute of Digital Technology, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China
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Jiahuai Han
4State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen 361005, China
5National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
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  • For correspondence: zengfeng@xmu.edu.cn jhan@xmu.edu.cn
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Abstract

Cell-to-cell variability is orchestrated by transcriptional variations participating in different biological processes. However, the dissection of transcriptional variability in specific biological process at single-cell level remains unavailable. Here, we present a deep generative model scPheno to integrate scRNA-seq with disease phenotypes to unravel the invisible phenotype-related transcriptional variations. We applied scPheno on COVID-19 blood scRNA-seq to separate transcriptional variations in regulating COVID-19 host immunity and transcriptional variations in maintaining cell-type identity. In silico, we found CLU+IFI27+S100A9+ monocyte as the efficient cellular marker for the prediction of COVID-19 diagnosis. Inspiringly, using only 4 genes upregulated in CLU+IFI27+S100A9+ monocytes can predict the COVID-19 diagnosis of individuals from different country with an accuracy up to 81.3%. We also found C1+CD163+ monocyte and 8 C1+CD163+ monocyte-upregulated genes as the efficient biomarkers for the prediction of severity assessment. Overall, scPheno is an effective method in dissecting the transcriptional basis of phenotype variations at single-cell level.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 21, 2022.
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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment
Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
bioRxiv 2022.06.20.496916; doi: https://doi.org/10.1101/2022.06.20.496916
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scPheno: A deep generative model to integrate scRNA-seq with disease phenotypes and its application on prediction of COVID-19 pneumonia and severe assessment
Feng Zeng, Xuwen Kong, Fan Yang, Ting Chen, Jiahuai Han
bioRxiv 2022.06.20.496916; doi: https://doi.org/10.1101/2022.06.20.496916

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