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Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions

View ORCID ProfileTinyi Chu, Zhong Wang, View ORCID ProfileDana Pe’er, View ORCID ProfileCharles G. Danko
doi: https://doi.org/10.1101/2020.01.07.897900
Tinyi Chu
1Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
2Graduate field of Computational Biology, Cornell University, Ithaca, NY 14853
3Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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  • For correspondence: dankoc@gmail.com tc532@cornell.edu
Zhong Wang
4School of Software Technology, Dalian University of Technology, Dalian 116023, China
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Dana Pe’er
3Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065
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Charles G. Danko
1Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
5Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853
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  • For correspondence: dankoc@gmail.com tc532@cornell.edu
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Abstract

Understanding the interactions between cells in their environment is a major challenge in genomics. Here we developed BayesPrism, a Bayesian method to jointly predict cellular composition and gene expression in each cell type, including heterogeneous malignant cells, from bulk RNA-seq using scRNA-seq as prior information. We conducted an integrative analysis of 1,412 bulk RNA-seq samples in primary glioblastoma, head and neck squamous cell carcinoma, and melanoma using single-cell datasets of 85 patients. We identified cell types correlated with clinical outcomes and explored spatial heterogeneity in malignant cell states and non-malignant cell type composition. We refined subtypes using gene expression in malignant cells, after excluding confounding non-malignant cell types. Finally, we identified genes whose expression in malignant cells correlated with infiltration of macrophages, T cells, fibroblasts, and endothelial cells across multiple tumor types. Our work introduces a new lens that uses scRNA-seq to accurately infer cellular composition and expression in large cohorts of bulk data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We have made several changes following a rigrous peer review. In addition, we have added two authors who have contributed significantly to our revision.

Copyright 
The copyright holder has placed this preprint in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.
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Posted November 13, 2021.
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Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
Tinyi Chu, Zhong Wang, Dana Pe’er, Charles G. Danko
bioRxiv 2020.01.07.897900; doi: https://doi.org/10.1101/2020.01.07.897900
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Bayesian cell-type deconvolution and gene expression inference reveals tumor-microenvironment interactions
Tinyi Chu, Zhong Wang, Dana Pe’er, Charles G. Danko
bioRxiv 2020.01.07.897900; doi: https://doi.org/10.1101/2020.01.07.897900

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