Toshiaki Yachimura (Tohoku University): Reconstructing Cell-Fate Dynamics via Optimal Transport

In recent years, with advances in measurement technologies such as single-cell RNA sequencing (scRNA-seq), there has been growing interest in reconstructing the dynamics of cell differentiation from gene-expression data. In this talk, I will present scEGOT, a comprehensive trajectory inference framework for reconstructing cell-fate dynamics from time-series scRNA-seq data based on entropic Gaussian mixture optimal transport (EGOT). scEGOT jointly infers directed cell-state graphs, gene-expression velocities, and dynamic trajectories (animations). Furthermore, it reconstructs Waddington-like potential landscapes that capture differentiation plasticity and infers gene regulatory networks shaping these dynamics, all within a computationally efficient framework.

As a case study, I will present results on time-series scRNA-seq data during the induction of human primordial germ cell-like cells (hPGCLCs) from iPS cells. scEGOT identifies progenitor populations and novel marker genes implicated in their induction.

Zoom Information:

https://us02web.zoom.us/j/2022111100

(Meeting code: 2022111100 Password: skcm2)