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Learning time-varying information flow from single-cell epithelial to mesenchymal transition data

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  • Smita Krishnaswamy
  • Nevena Zivanovic
  • Roshan Sharma
  • Dana Pe’er
  • Bernd Bodenmiller

Abstract

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.

Suggested Citation

  • Smita Krishnaswamy & Nevena Zivanovic & Roshan Sharma & Dana Pe’er & Bernd Bodenmiller, 2018. "Learning time-varying information flow from single-cell epithelial to mesenchymal transition data," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-32, October.
    • Handle: RePEc:plo:pone00:0203389
    DOI: 10.1371/journal.pone.0203389
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