RT Journal Article
SR Electronic
T1 Learning Edge Rewiring in EMT From Single Cell Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 155028
DO 10.1101/155028
A1 Smita Krishnaswamy
A1 Nevena Zivanovic
A1 Roshan Sharma
A1 Dana Pe’er
A1 Bernd Bodenmiller
YR 2017
UL http://biorxiv.org/content/early/2017/06/25/155028.abstract
AB Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data. We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGFβ. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3DDREMI) to predict and validate the effect of drug perturbations on EMT.