ScEGOT: single-cell trajectory inference framework based on entropic Gaussian mixture optimal transport.

ABSTRACT:

Background

Time-series scRNA-seq data have opened a door to elucidate cell differentiation, and in this context, the optimal transport theory has been attracting much attention. However, there remain critical issues in interpretability and computational cost.

Results

We present scEGOT, a comprehensive framework for single-cell trajectory inference, as a generative model with high interpretability and 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. Our analysis shows TFAP2A is insufficient for identifying PGCLC progenitors, requiring NKX1-2. Additionally, MESP1 and GATA6 are also crucial for PGCLC/somatic cell segregation.

Conclusions

SUBMITTER: Yachimura T 

PROVIDER: S-EPMC11665215 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature