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Tipping-point analysis uncovers critical transition signals from gene expression profiles

Xinan H Yang, Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Megan Rowton, Yanqiu Wang, Dannie Griggs, Ivan Moskowitz, John M Cunningham
doi: https://doi.org/10.1101/668442
Xinan H Yang
1Department of Pediatrics, The University of Chicago, Chicago, USA
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  • For correspondence: xyang2@uchicago.edu
Zhezhen Wang
1Department of Pediatrics, The University of Chicago, Chicago, USA
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Andrew Goldstein
2Department of Statistics, The University of Chicago, Chicago, USA
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Yuxi Sun
1Department of Pediatrics, The University of Chicago, Chicago, USA
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Megan Rowton
1Department of Pediatrics, The University of Chicago, Chicago, USA
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Yanqiu Wang
3Stanford University, Stanford, USA
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Dannie Griggs
1Department of Pediatrics, The University of Chicago, Chicago, USA
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Ivan Moskowitz
1Department of Pediatrics, The University of Chicago, Chicago, USA
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John M Cunningham
1Department of Pediatrics, The University of Chicago, Chicago, USA
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Summary

Differentiation involves bifurcations between discrete cell states, each defined by a distinct gene expression profile. Single-cell RNA profiling allows the detection of bifurcations. However, while current methods capture these events, they do not identify characteristic gene signals. Here we show that BioTIP – a tipping-point theory-based analysis – can accurately, robustly, and reliably identify critical transition signals (CTSs). A CTS is a small group of genes with high covariance in expression that mark the cells approaching a bifurcation. We validated its accuracy in the cardiogenesis with known a tipping point and demonstrated the identified CTSs contain verified differentiation-driving transcription factors. We then demonstrated the application on a published mouse gastrulation dataset, validated the predicted CTSs using independent in-vivo samples, and inferred the key developing mesoderm regulator Etv2. Taken together, BioTIP is broadly applicable for the characterization of the plasticity, heterogeneity, and rapid switches in developmental processes, particularly in single-cell data analysis.

Highlights

  • Identifying significant critical transition signals (CTSs) from expression noise

  • A significant CTS contains or is targeted by key transcription factors

  • BioTIP identifies CTSs accurately and independent of trajectory topologies

  • Significant CTSs reproducibly indicate bifurcations across datasets

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • 1) Focus on the application to single-cell transcriptome. 2) Adding a new analysis on data with a verified tipping point and driving transcription factors.

  • https://github.com/xyang2uchicago/BioTIP_application

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted June 26, 2021.
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Tipping-point analysis uncovers critical transition signals from gene expression profiles
Xinan H Yang, Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Megan Rowton, Yanqiu Wang, Dannie Griggs, Ivan Moskowitz, John M Cunningham
bioRxiv 668442; doi: https://doi.org/10.1101/668442
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Tipping-point analysis uncovers critical transition signals from gene expression profiles
Xinan H Yang, Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Megan Rowton, Yanqiu Wang, Dannie Griggs, Ivan Moskowitz, John M Cunningham
bioRxiv 668442; doi: https://doi.org/10.1101/668442

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