PT  - JOURNAL ARTICLE
AU  - Smita Krishnaswamy
AU  - Nevena Zivanovic
AU  - Roshan Sharma
AU  - Dana Pe’er
AU  - Bernd Bodenmiller
TI  - Learning Edge Rewiring in EMT From Single Cell Data
AID  - 10.1101/155028
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 155028
4099  - http://biorxiv.org/content/early/2017/06/25/155028.short
4100  - http://biorxiv.org/content/early/2017/06/25/155028.full
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.