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


Authors: Krishnaswamy, S.; Zivanovic, N.; Sharma, R.; Pe'er, D.; Bodenmiller, B.
Article Title: Learning time-varying information flow from single-cell epithelial to mesenchymal transition data
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 epithelialto-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. © 2018 Krishnaswamy et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Journal Title: PLoS ONE
Volume: 13
Issue: 10
ISSN: 1932-6203
Publisher: Public Library of Science  
Date Published: 2018-10-29
Start Page: e0203389
Language: English
DOI: 10.1371/journal.pone.0203389
PUBMED: 30372433
PROVIDER: scopus
PMCID: PMC6205587
DOI/URL:
Notes: Article -- Export Date: 3 December 2018 -- Source: Scopus
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