Theory Lunch: Geoffrey Schiebinger


Friday, December 8, 2017, 12:00pm to 2:00pm


WAB 563

Geoffrey Schiebinger
Statistics + Data Science Center
Broad Institute & MIT

Title: Learning developmental landscapes from single-cell gene expression with optimal transport

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. In this talk, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neural- and placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.