Yasuaki Hiraoka (01/15/2025): Single-cell trajectory inference framework using Gaussian Mixture OT скачать в хорошем качестве

Yasuaki Hiraoka (01/15/2025): Single-cell trajectory inference framework using Gaussian Mixture OT

scEGOT: Single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport Abstract: This talk mainly focuses on geometric data analysis in human biology. In particular, I present scEGOT, a recently developed method for comprehensive single-cell trajectory inferences based on entropic Gaussian mixture optimal transport. The main advantage of scEGOT allows us to go back and forth between continuous and discrete problems, and it provides versatile trajectory inference methods at a low computational cost. Applied to the human primordial germ cell-like cell (PGCLC) induction system, scEGOT identified the PGCLC progenitor population and bifurcation time of segregation. Furthermore, our analysis found a novel gene (NKX1-2) for appropriately identifying the PGCLC progenitor.