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

BMC Bioinformatics. 2024 Dec 23;25(1):388. doi: 10.1186/s12859-024-05988-z.

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: These findings shed light on the mechanism that segregates PGCLC from somatic lineages. Notably, not limited to scRNA-seq, scEGOT's versatility can extend to general single-cell data like scATAC-seq, and hence has the potential to revolutionize our understanding of such datasets and, thereby also, developmental biology.

Keywords: Epigenetic landscape; Gaussian mixture model; Optimal transport; Single-cell biology; Trajectory inference.

MeSH terms

  • Cell Differentiation
  • Entropy
  • Germ Cells / cytology
  • Germ Cells / metabolism
  • Humans
  • Single-Cell Analysis* / methods