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CINS: Cell Interaction Network inference from Single cell expression data

Ye Yuan, Carlos Cosme Jr., Taylor Sterling Adams, View ORCID ProfileJonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, View ORCID ProfileNaftali Kaminski, Ziv Bar-Joseph
doi: https://doi.org/10.1101/2021.02.22.432206
Ye Yuan
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
2Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Carlos Cosme Jr.
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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Taylor Sterling Adams
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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Jonas Schupp
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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  • ORCID record for Jonas Schupp
Koji Sakamoto
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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Nikos Xylourgidis
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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Matthew Ruffalo
4Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Naftali Kaminski
3Section of Pulmonary, Critical Care and Sleep Medicine, Yale University School of Medicine, New Haven, CT 06520, USA
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  • ORCID record for Naftali Kaminski
Ziv Bar-Joseph
2Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
4Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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  • For correspondence: zivbj@andrew.cmu.edu
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Abstract

Studies comparing single cell RNA-Seq (scRNA-Seq) data between conditions mainly focus on differences in the proportion of cell types or on differentially expressed genes. In many cases these differences are driven by changes in cell interactions which are challenging to infer without spatial information. To determine cell-cell interactions that differ between conditions we developed the Cell Interaction Network Inference (CINS) pipeline. CINS combines Bayesian network analysis with regression-based modeling to identify differential cell type interactions and the proteins that underlie them. We tested CINS on a disease case control and on an aging human dataset. In both cases CINS correctly identifies cell type interactions and the ligands involved in these interactions. We performed additional mouse aging scRNA-Seq experiments which further support the interactions identified by CINS.

Footnotes

  • ↵* e-mail: zivbj{at}cs.cmu.edu

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-ND 4.0 International license.
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Posted February 25, 2021.
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CINS: Cell Interaction Network inference from Single cell expression data
Ye Yuan, Carlos Cosme Jr., Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Naftali Kaminski, Ziv Bar-Joseph
bioRxiv 2021.02.22.432206; doi: https://doi.org/10.1101/2021.02.22.432206
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CINS: Cell Interaction Network inference from Single cell expression data
Ye Yuan, Carlos Cosme Jr., Taylor Sterling Adams, Jonas Schupp, Koji Sakamoto, Nikos Xylourgidis, Matthew Ruffalo, Naftali Kaminski, Ziv Bar-Joseph
bioRxiv 2021.02.22.432206; doi: https://doi.org/10.1101/2021.02.22.432206

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