Geometry-preserving vector field reconstruction of high-dimensional cell-state dynamics using ddHodge.

ABSTRACT: The differentiation potency of cells is governed by dynamic changes in gene expression, which can be inferred from single-cell RNA sequencing (scRNA-seq) data. While velocity-based approaches have been used to analyze cell state changes as vector fields, extracting acceleration (change of change) information remains challenging because of the sparsity and high-dimensionality of the data. Here, we develop ddHodge, a framework based on Hodge decomposition for precise vector-field reconstruction. ddHodge accurately recovers all basic components of the vector field, namely, the gradient, curl, and divergence, including the acceleration of the cell state, as second-order derivatives, even from biased and sparse samples. Furthermore, we extend the method to approximate high-dimensional gene expr

SUBMITTER: Maehara K 

PROVIDER: S-EPMC12749697 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature