Package: scVeloR 0.1.0

scVeloR: RNA Velocity Analysis in R

A comprehensive R implementation of RNA velocity analysis based on the scvelo Python package. This package enables estimation of RNA velocities for single-cell RNA-seq data, supporting deterministic (steady-state), stochastic, and dynamical models. The dynamical model uses an Expectation-Maximization algorithm to infer full transcriptional kinetics including transcription, splicing, and degradation rates. The package integrates seamlessly with Seurat objects and provides visualization functions for velocity embedding, stream plots, and phase portraits.

Authors:Zaoqu Liu [aut, cre], scVelo Team [ctb]

scVeloR_0.1.0.tar.gz
scVeloR_0.1.0.zip(r-4.7-x86_64)scVeloR_0.1.0.zip(r-4.7-arm64)scVeloR_0.1.0.zip(r-4.6-x86_64)scVeloR_0.1.0.zip(r-4.6-arm64)
scVeloR_0.1.0.tgz(r-4.6-x86_64)scVeloR_0.1.0.tgz(r-4.6-arm64)
scVeloR_0.1.0.tar.gz(r-4.7-arm64)scVeloR_0.1.0.tar.gz(r-4.7-x86_64)scVeloR_0.1.0.tar.gz(r-4.6-arm64)scVeloR_0.1.0.tar.gz(r-4.6-x86_64)
scVeloR_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scVeloR/json (API)

# Install 'scVeloR' in R:
install.packages('scVeloR', repos = c('https://zaoqu-liu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zaoqu-liu/scvelor/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.78 score 2 stars 8 scripts 57 exports 5 dependencies

Last updated from:319e312f72 (on main). Checks:11 ERROR, 3 FAIL, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR163
linux-devel-x86_64ERROR196
source / vignettesERROR295
linux-release-arm64ERROR162
linux-release-x86_64ERROR196
macos-release-arm64ERROR297
macos-release-x86_64ERROR392
macos-oldrel-arm64FAIL97
macos-oldrel-x86_64FAIL133
windows-devel-arm64ERROR298
windows-devel-x86_64ERROR292
windows-release-arm64ERROR368
windows-release-x86_64ERROR179
windows-oldrel-x86_64FAIL87
wasm-releaseOK138

Exports:assign_tauBaseDynamicscompute_divergencecompute_neighborscompute_shared_timecompute_transition_matrixcompute_velocity_from_paramscreate_velocity_seuratcross_momentsDynamicsRecoveryexport_to_anndataexport_velocity_resultsfilter_cellsfilter_genesget_connectivitiesget_embeddingget_layer_matrixget_velocity_dataimport_from_anndataimport_velocity_datalatent_timemomentsmRNAnormalize_layersplot_phaseplot_velocityplot_velocity_gridplot_velocity_streamprepare_velocityrank_velocity_genesrecover_dynamicsrecover_dynamics_multiroot_timerun_velocitysecond_order_momentsSecondOrderSteadyStateModelsplicedSplicingDynamicssteady_statetau_invtau_inv_uterminal_statesunsplicedvectorizevelocityvelocity_cell_cyclevelocity_coherencevelocity_confidencevelocity_embeddingvelocity_from_dynamicsvelocity_graphvelocity_gridvelocity_pseudotimevelocity_steady_statevelocity_stochasticvelocity_streamlinesvelocity_summary

Dependencies:latticeMatrixR6RcppRcppArmadillo

Dynamical Model Deep Dive
Introduction | Mathematical Foundation | The Transcriptional Switch Model | ODE System | Analytical Solutions | The EM Algorithm | Overview | E-Step: Time Assignment | M-Step: Parameter Update | Implementation in scVeloR | Basic Usage | Step-by-Step Approach | Parameter Details | Interpreting Results | Kinetic Parameters | Latent Time | Quality Control | Gene Fit Quality | Convergence Check | Velocity Confidence | Troubleshooting | Common Issues | Best Practices | Advanced Topics | Scaling Factor | Parallel Processing | Summary | Session Information
Rendered fromdynamical-model.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25

Mathematical Framework of RNA Velocity
Introduction | The Central Dogma and mRNA Kinetics | Biological Background | The Kinetic Model | Analytical Solutions | Constant Transcription (Induction Phase) | Zero Transcription (Repression Phase) | Steady State | Velocity Estimation Models | Model 1: Deterministic (Steady-State) | Model 2: Stochastic | Model 3: Dynamical | Velocity Graph Construction | Cosine Similarity | Transition Probability | Latent Time Inference | Gene-Shared Latent Time | Root Cell Identification | Model Selection Guidelines | References | Session Information
Rendered fromalgorithm-theory.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25

Performance Optimization Guide
Introduction | Performance Architecture | Memory Optimization | Sparse Matrix Usage | Memory Profiling | Chunked Processing | Parallel Computing | Using the future Package | Platform-Specific Configuration | Parallel Best Practices | Algorithmic Optimizations | Gene Selection | Neighbor Graph Approximation | Benchmark Comparison | EM Algorithm Optimization | Hardware Recommendations | Memory Requirements | Compute Requirements | Profiling Your Analysis | Timing Code Blocks | Identifying Bottlenecks | Practical Workflow for Large Data | Optimized Pipeline | Memory-Efficient Visualization | Troubleshooting Performance Issues | Common Issues and Solutions | Quick Diagnostics | Summary | Session Information
Rendered fromperformance-guide.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25

Velocity Model Comparison
Introduction | Model Overview | 1. Deterministic (Steady-State) Model | 2. Stochastic Model | 3. Dynamical Model | Comparison Table | Visual Comparison | When to Use Each Model | Use Steady-State When: | Use Stochastic When: | Use Dynamical When: | Diagnostic Plots | Phase Portrait Comparison | R-squared Distribution | Computational Benchmarks | Practical Workflow | Recommended Approach | Session Information
Rendered frommodel-comparison.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25

Visualization Gallery
Introduction | Velocity Embedding Plot | Basic Usage | Customization Options | Example Output | Streamline Plot | Grid Plot | Phase Portrait | Latent Time Visualization | Velocity Confidence | Combined Visualizations | Multi-panel Figure | Customizing Colors | Exporting Figures | Session Information
Rendered fromvisualization-gallery.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25

scVeloR: RNA Velocity Analysis in R
Introduction | Installation | Quick Start Workflow | Load packages | Prepare your data | Step 1: Preprocessing | Step 2: Compute Moments | Step 3: Velocity Estimation | Steady-State Model (Fastest) | Stochastic Model (Recommended) | Dynamical Model (Most Accurate) | Step 4: Velocity Graph | Step 5: Project to Embedding | Step 6: Visualization | Velocity Embedding Plot | Stream Plot | Grid Plot | Phase Portrait | Heatmap | Parallel Computing | Terminal States and Pseudotime | Gene Ranking | Quality Metrics | Model Selection Guide | Session Info | References | Support
Rendered fromscVeloR-tutorial.Rmdusingknitr::rmarkdown

Last update: 2026-01-25
Started: 2026-01-25