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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures

Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, View ORCID ProfileYue Li
doi: https://doi.org/10.1101/2023.01.31.526312
Manqi Zhou
1Department of Computational Biology, Cornell University
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Hao Zhang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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Zilong Bai
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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Dylan Mann-Krzisnik
3Quantitative Life Science, McGill University
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Fei Wang
2Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine
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  • For correspondence: few2001@med.cornell.edu yueli@cs.mcgill.ca
Yue Li
3Quantitative Life Science, McGill University
4School of Computer Science, McGill University
5Mila - Quebec AI Institute
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  • ORCID record for Yue Li
  • For correspondence: few2001@med.cornell.edu yueli@cs.mcgill.ca
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Abstract

The advent of single-cell multi-omics sequencing technology makes it possible for researchers to leverage multiple modalities for individual cells and explore cell heterogeneity. However, the high dimensional, discrete, and sparse nature of the data make the downstream analysis particularly challenging. Most of the existing computational methods for single-cell data analysis are either limited to single modality or lack flexibility and interpretability. In this study, we propose an interpretable deep learning method called multi-omic embedded topic model (moETM) to effectively perform integrative analysis of high-dimensional single-cell multimodal data. moETM integrates multiple omics data via a product-of-experts in the encoder for efficient variational inference and then employs multiple linear decoders to learn the multi-omic signatures of the gene regulatory programs. Through comprehensive experiments on public single-cell transcriptome and chromatin accessibility data (i.e., scRNA+scATAC), as well as scRNA and proteomic data (i.e., CITE-seq), moETM demonstrates superior performance compared with six state-of-the-art single-cell data analysis methods on seven publicly available datasets. By applying moETM to the scRNA+scATAC data in human peripheral blood mononuclear cells (PBMCs), we identified sequence motifs corresponding to the transcription factors that regulate immune gene signatures. Applying moETM analysis to CITE-seq data from the COVID-19 patients revealed not only known immune cell-type-specific signatures but also composite multi-omic biomarkers of critical conditions due to COVID-19, thus providing insights from both biological and clinical perspectives.

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 January 31, 2023.
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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, Yue Li
bioRxiv 2023.01.31.526312; doi: https://doi.org/10.1101/2023.01.31.526312
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Single-cell multi-omic topic embedding reveals cell-type-specific and COVID-19 severity-related immune signatures
Manqi Zhou, Hao Zhang, Zilong Bai, Dylan Mann-Krzisnik, Fei Wang, Yue Li
bioRxiv 2023.01.31.526312; doi: https://doi.org/10.1101/2023.01.31.526312

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