Velde: constructing cell potential landscapes by RNA velocity vector field decomposition

The Waddington landscape serves as a metaphor illustrating the developmental process of cells, likening it to a small ball rolling down various trajectories into valleys. Constructing an epigenetic landscape of this nature aids in visualizing and gaining insights into cell differentiation. Developme...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Jia, Junbo, Chen, Luonan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.11.2023
Subjects
Cell cycle
Decomposition
Differentiation (biology)
Euclidean geometry
Euclidean space
Fields (mathematics)
Gene sequencing
Online AccessGet full text

Cover

More Information
Summary:The Waddington landscape serves as a metaphor illustrating the developmental process of cells, likening it to a small ball rolling down various trajectories into valleys. Constructing an epigenetic landscape of this nature aids in visualizing and gaining insights into cell differentiation. Development encompasses intricate processes involving both cell differentiation and cell cycles. However, current landscape methods solely focus on constructing a potential landscape for cell differentiation, neglecting the accompanying cell cycle. This paper introduces a novel method that simultaneously constructs two types of potential landscapes using single-cell RNA sequencing data. Specifically, it presents the natural Helmholtz-Hodge decomposition (nHHD) of a continuous vector field within a bounded domain in n-dimensional Euclidean space. This decomposition uniquely breaks down the vector field into a gradient field, a rotation field, and a harmonic field. Utilizing this approach, the RNA velocity vector field is separated into a curl-free component representing cell differentiation and a curl component representing the cell cycle. By calculating the corresponding potential functions, potential landscapes for both cell differentiation and the cell cycle are obtained. Finally, the efficacy of this method is demonstrated through its application to synthetic and real datasets.
ISSN:2331-8422