Faculty, Staff and Student Publications
Publication Date
3-29-2025
Journal
Bioinformatics
Abstract
Summary: Cells differentiate to their final fates along unique trajectories, often involving multi-potent progenitors that can produce multiple terminally differentiated cell types. Recent developments in single-cell transcriptomic and epigenomic measurement provide tremendous opportunities for mapping these trajectories. The visualization of single-cell data often relies on dimension reduction methods such as UMAP to simplify high-dimensional single-cell data down to an understandable 2D form. However, these dimension reduction methods are not constructed to allow direct interpretation of the reduced dimensions in terms of cell differentiation. To address these limitations, we developed a new approach that places each cell from a single-cell dataset within a simplex whose vertices correspond to terminally differentiated cell types. Our approach can quantify and visualize current cell fate commitment and future cell potential. We developed CytoSimplex, a standalone open-source package implemented in R and Python that provides simple and intuitive visualizations of cell differentiation in 2D ternary and 3D quaternary plots. We believe that CytoSimplex can help researchers gain a better understanding of cell type transitions in specific tissues and characterize developmental processes.
Keywords
Single-Cell Analysis, Cell Differentiation, Software, Humans, Computational Biology
DOI
10.1093/bioinformatics/btaf119
PMID
40119904
PMCID
PMC11992338
PubMedCentral® Posted Date
3-22-2025
PubMedCentral® Full Text Version
Post-print
Recommended Citation
Liu, Jialin; Wang, Yichen; Li, Chen; Gu, Yichen; Ono, Noriaki; and Welch, Joshua, "CytoSimplex: Visualizing Single-Cell Fates and Transitions on a Simplex" (2025). Faculty, Staff and Student Publications. 99.
https://digitalcommons.library.tmc.edu/uthdb_docs/99
Published Open-Access
yes