# RNA Velocity

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.

JuloVelo moves gene kinetics pattern identification ahead of neural network training: each gene is first classified as Circle, Complete induction, or Complete repression from its unspliced–spliced RNA relationship, and training starts from pattern-matched pretrained weights, steering cell-specific kinetics away from biologically implausible parameter regimes. Across eight biological datasets spanning erythrogenesis, neurogenesis, and endocrinogenesis, plus simulated data with known kinetics, it was more stable than constant-rate and cell-specific models, was the only method to resolve all five hippocampal neurogenesis branches, and used promoter chromatin accessibility to process multiome data.

Glioblastoma malignant states were treated as parallel subtypes, and a single biopsy could not represent the whole tumour. Pairing single-nucleus and spatial transcriptomics across 12 IDH-wild-type primary tumours and 57 sampling sites revealed a trajectory from developmental-like states toward glial injury response and hypoxia, layered in space from the infiltrating edge to the necrotic core; the trajectory appears across tumour subtypes and genetically distinct subclones and is recapitulated in patient-derived organoids.

Glioblastoma malignant states were treated as parallel subtypes, and a single biopsy could not represent the whole tumour. Pairing single-nucleus and spatial transcriptomics across 12 IDH-wild-type primary tumours and 57 sampling sites revealed a trajectory from developmental-like states toward glial injury response and hypoxia, layered in space from the infiltrating edge to the necrotic core; the trajectory appears across tumour subtypes and genetically distinct subclones and is recapitulated in patient-derived organoids.

A single-cell transcriptome snapshot carries both where a cell sits along a trajectory and which way its expression is moving, and these were usually estimated separately. ManifoldVelo places each cell on a differentiable unspliced/spliced RNA expression curve and matches the curve's derivatives to a transcription–splicing–degradation kinetic field, including between observed cells, so coordinates, curves and kinetics are fitted together. In multipeak cyclic simulations, spliced-only queries gave circular time errors of 0.64% and 7.40% with spliced-velocity cosines of 0.9776 and 0.9421; on WT2, phase error fell 47.0% relative to the recorded VeloCycle result, while VeloCycle did better on WT1 and the smooth cycle.

A single-cell transcriptome snapshot carries both where a cell sits along a trajectory and which way its expression is moving, and these were usually estimated separately. ManifoldVelo places each cell on a differentiable unspliced/spliced RNA expression curve and matches the curve's derivatives to a transcription–splicing–degradation kinetic field, including between observed cells, so coordinates, curves and kinetics are fitted together. In multipeak cyclic simulations, spliced-only queries gave circular time errors of 0.64% and 7.40% with spliced-velocity cosines of 0.9776 and 0.9421; on WT2, phase error fell 47.0% relative to the recorded VeloCycle result, while VeloCycle did better on WT1 and the smooth cycle.