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SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer

Yupu Xu, Yuzhou Wang, Shisong Ma
doi: https://doi.org/10.1101/2023.02.05.526424
Yupu Xu
1MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
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Yuzhou Wang
1MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
2The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
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Shisong Ma
1MOE Key Laboratory for Cellular Dynamics, School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Innovation Academy for Seed Design, Chinese Academy of Sciences, Hefei, China
3School of Data Science, University of Science and Technology of China, Hefei, China
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  • For correspondence: sma@ustc.edu.cn
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Abstract

Gene co-expression analysis of single-cell transcriptomes that aims to define functional relationships between genes is challenging due to excessive dropout values. Here, we developed a single-cell graphical Gaussian model (SingleCellGGM) algorithm to conduct single-cell gene co-expression network analysis. When applied to mouse single-cell datasets, SingleCellGGM constructed networks from which gene co-expression modules with highly significant functional enrichment were identified. We considered the modules to be gene expression programs (GEPs). These GEPs enable direct cell-type annotation of individual cells without cell clustering, and they are enriched with genes required for the functions of the corresponding cells, sometimes at a level greater than 10-fold. The GEPs are conserved across datasets and enable universal cell-type label transfer across different studies. We also proposed a dimension-reduction method through averaging-by-GEPs for single-cell analysis, enhancing the interpretability of results. Thus, SingleCellGGM offers a unique GEP-based perspective to analyze single-cell transcriptomes and reveals biological insights shared by different single-cell datasets.

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 February 05, 2023.
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SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer
Yupu Xu, Yuzhou Wang, Shisong Ma
bioRxiv 2023.02.05.526424; doi: https://doi.org/10.1101/2023.02.05.526424
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SingleCellGGM enables gene expression program identification from single-cell transcriptomes and facilitates universal cell label transfer
Yupu Xu, Yuzhou Wang, Shisong Ma
bioRxiv 2023.02.05.526424; doi: https://doi.org/10.1101/2023.02.05.526424

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