Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories

Visual representations are widely used to interpret trajectories in single-cell data; however, they do not always faithfully capture inferred trajectory structure. As a result, interpretation of cellular dynamics and downstream analyses may be compromised. Here, we present Pseudotime Graph Diffusion (PGD), a lightweight and interpretable post hoc framework for smoothing cell-level features along pseudotime. PGD operates by performing random-walk diffusion on a pseudotime graph, propagating information along inferred trajectory paths to enhance continuity and structure. We demonstrate that PGD-smoothed embeddings improve visualization of increasingly complex inferred trajectories of monocytes and macrophages during wound healing. We further show that PGD extends naturally to trajectory-aware gene expression smoothing and scales to atlas-sized datasets. By improving agreement between visual representations and inferred trajectories, PGD enables more faithful interpretation of dynamic cellular processes.