Conceptual illustration of ddHodge, a geometry-preserving framework for reconstructing cell-state dynamics from single-cell data (IMAGE)
Caption
The left panel depicts a potential-like landscape, where white streamlines represent the inferred trajectory of cell states across time. Although single-cell RNA sequencing captures only static snapshots of cells, ddHodge reconstructs the patch-worked dynamical structure by connecting many local “nows” into a coherent global picture. The right panel shows a graph-based representation of high-dimensional cell states, on which local geometry is preserved to quantify key dynamical properties such as stability, divergence, and rotational components.
Credit
Kazumitsu Maehara / Kyushu University
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