Learning Edge Rewiring in EMT From Single Cell Data

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. Introduction Different cell types exhibit distinct responses to environmental cues, resulting in changes in cellular state. Responses to environmental cues play a key role in development, cellular differentiation and fate. For instance, growth factors like TGFβ [1] guide the patterning of tissues during embryogenesis [2]. In hematopoiesis growth factors such as GM-CSF can drive the development of neutrophils, monocytes and macrophages [3]. . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/155028 doi: bioRxiv preprint first posted online Jun. 25, 2017;