# Pseudotime Inference

Benchmarking single-cell methods has been limited by costly annotation of real data and by datasets that capture only one observed condition. scDeepSim encodes expression into latent subvectors assigned to cell type, batch and residual variation, learns their distribution with latent diffusion, and at generation time modifies only the target subvector, imposing batch effects or constructing branching trajectories at user-set strengths. On scIB human pancreas data at intervention strength α=1, the structured model reached a mean cell-type silhouette width of 0.519 versus 0.432 for a plain variational autoencoder.

Benchmarking single-cell methods has been limited by costly annotation of real data and by datasets that capture only one observed condition. scDeepSim encodes expression into latent subvectors assigned to cell type, batch and residual variation, learns their distribution with latent diffusion, and at generation time modifies only the target subvector, imposing batch effects or constructing branching trajectories at user-set strengths. On scIB human pancreas data at intervention strength α=1, the structured model reached a mean cell-type silhouette width of 0.519 versus 0.432 for a plain variational autoencoder.

Technologies that simultaneously sequence single-cell transcriptome and 3D genome already yield paired measurements from the same cell, but the two modalities differ sharply in noise, scale and biological meaning, and were previously analyzed separately with latent spaces that need not align. scHiR maps gene expression and chromatin contact matrices into one hyperspherical latent space through modality-specific encoders and reconstructs both signals. On paired data from HiRES, GAGE-seq and LiMCA, the joint embedding matched or exceeded single-omics methods in clustering, and on mouse main olfactory epithelium it raised the Spearman correlation between pseudotime and developmental stage from 0.357 and 0.205 for single-omics embeddings to 0.517.

Technologies that simultaneously sequence single-cell transcriptome and 3D genome already yield paired measurements from the same cell, but the two modalities differ sharply in noise, scale and biological meaning, and were previously analyzed separately with latent spaces that need not align. scHiR maps gene expression and chromatin contact matrices into one hyperspherical latent space through modality-specific encoders and reconstructs both signals. On paired data from HiRES, GAGE-seq and LiMCA, the joint embedding matched or exceeded single-omics methods in clustering, and on mouse main olfactory epithelium it raised the Spearman correlation between pseudotime and developmental stage from 0.357 and 0.205 for single-omics embeddings to 0.517.

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.