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PLoS One. 2018 Oct 29;13(10):e0203389. doi: 10.1371/journal.pone.0203389. eCollection 2018.

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

Author information

1
Department of Genetics, Department of Computer Science, Yale University, New Haven, CT, United States of America.
2
Institute for Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
3
Department of Applied Physics and Applied Math, Columbia University, New York, NY, United States of America.
4
Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.

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.

PMID:
30372433
PMCID:
PMC6205587
DOI:
10.1371/journal.pone.0203389
[Indexed for MEDLINE]
Free PMC Article

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