RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference.

scVelo RNA Velocity When to use Use for kinetic analysis of aligned spliced/unspliced RNA counts, directional trajectory hypotheses, gene level phase portraits, and dynamical latent time. Use the [model reference](references/velocity models.md) for assumptions, transition probabilities, and an optional CellRank handoff. RNA velocity estimates an expression derivative under a model. Smooth arrows are not observed cell movement, lineage tracing, causal drivers, or proof of future fate. The dynamical estimator is not automatically more accurate than a steady state estimator on every dataset. Tested environment and compatibility The maintained workflow targets scVelo 0.3.4 , Scanpy 1.12.4 , AnnData 0.13.4 , NumPy 2.5.3 , pandas 2.3.3 , SciPy 1.18.1 , Matplotlib 3.11.2 , and loompy 3.0.8 on Python 3.13. Use an isolated environment: The packages above were resolved from cache and tested in an isolated uv environment; shell paths above use POSIX syntax. Installation requires network access unless packages are cached. No API key is required. Deterministic and dynamical models: native synthetic fitting, graphs, relative times, plots and H5AD round trips are tested on this stack. Stochastic model: scVelo 0.3.4's default GLS scalar assignment fails with NumPy 2. Scanpy 1.12 requires NumPy 2, so installing numpy<2 beside current Scanpy is not a solution. A separately validated legacy Scanpy/NumPy environment is required; that legacy stack was not executed in this review. The helper rejects this combination before modifying data, without changing the model. pandas 3: scVelo 0.3.4 dynamical fitting and some plots use operations no longer supported by pandas 3. Keep the explicit pandas pin. The upstream stable tutorial still contains removed scv.read and older preprocessing calls. Check the [0.3.4 source](https://github.com/theislab/scvelo/tree/v0.3.4) when a tutorial disagrees with the installed API. Input contract 1. Obtain spliced and unspliced counts from a velocity aware quantifier. Keep quantifier/version, reference annotation, counting mode, sample IDs and cell barcode mapping. Upstream quantification is outside the bundled script. 2. Supply cells by genes matrices in adata.layers['spliced'] and adata.layers['unspliced'] , with identical cell/gene ordering and unique IDs. Counts must be finite and nonnegative; fractional count estimates are allowed. Never use logged, scaled, residualized, or batch corrected values as counts. Numerical inspection alone cannot establish that data are raw. 3. Inspect per library/cluster coverage, doublets, ambient RNA, zero count cells, and annotation compatibility before fitting. Preserve the original full gene count file. Retained gene backups do not preserve filtered out genes. 4. Resolve barcode prefixes and gene identifiers explicitly when combining files. Do not silently strip library IDs, intersect away most cells, or make duplicated biological IDs unique without understanding why they repeat. Read H5AD through AnnData. scVelo 0.3.4 has no scv.read or scv.DataFrame : For legacy loom input use ad.io.read loom('counts.loom', X name='spliced', sparse=True) . AnnData 0.13 deprecates loom; convert to H5AD for further work. Its layers[None] aliases X : do not delete it or iterate all layers as if every key were a string. The helper accesses only named velocity layers. Native loom reading and exact, reordered metadata alignment are covered by the tests; AnnData 0.13 write loom is not used. Run the maintained workflow The [bundled script](scripts/rna velocity workflow.py) accepts local files and never downloads a demonstration dataset on startup: Run these from the skill directory. Input filenames and biological annotations are illustrative; the same CLI and functions are tested on small generated kinetic fixtures. Omit groupby when annotations are unavailable. Use no plots for analysis without computing UMAP. The helper performs these steps: 1. Validate layers, IDs, model compatibility and grouping; refuse previously generated moments/velocity so preprocessing cannot silently run twice. 2. Back up retained raw layers as spliced counts and unspliced counts and rebuild X from raw spliced counts. Filter genes and normalize X and the two count layers on a linear scale. 3. Apply sc.pp.log1p only to X , select highly variable genes with Scanpy, and rebuild PCA and neighbors after subsetting. Reusing stale PCA/neighbors from an unrelated feature set can silently change the velocity model. 4. Compute Ms and Mu with scv.pp.moments(adata, n neighbors=None) from the explicit Scanpy graph. Moments are dense: budget memory for multiple cells by genes arrays, not only the sparse input. 5. For dynamical mode, call recover dynamics(var names='all') on the selected genes before velocity(mode='dynamical') ; otherwise fit the selected model. Require usable genes and a nonempty velocity graph. 6. Compute velocity coherence and velocity pseudotime; add latent time only for dynamical fits. Rank velocity associated genes only with at least two groups and at least two cells per group. Record/warn about nonfinite or constant time/coherence outputs in uns['velocity workflow']['diagnostics'] ; a constant pseudotime does not support a trajectory ordering. Ranking is exploratory. 7. For plots, recompute UMAP from the rebuilt graph, project velocities, save PNGs directly to the requested directory, then save H5AD with package versions and selected parameters. Existing labels remain annotations, not validated cell identities. The Python function mutates its argument in place; pass adata.copy() to keep the original object. The CLI writes to output dir/adata velocity.h5ad . Version 2 changes the former helper's behavior deliberately: raw layer geometry is rebuilt, missing requested labels are errors, and plots/parallelism can be controlled explicitly. Runtime depends on cells, genes and fit difficulty; there is no universal 10–30 minute expectation. Inspect the evidence before interpretation After a dynamical run (dataset specific gene selection is illustrative): Inspect spliced versus unspliced phase portraits, coverage across induction and repression, fitted parameters, failed/NaN fits, and branch specific kinetics. Check sensitivity to gene set, neighbors, subsampling and model assumptions. Use time course labels, perturbations, lineage tracing or labeling experiments as independent directional evidence where available. A plausible UMAP alone cannot validate a fit, and tuning until arrows match a desired story is not a validation strategy. There is no general minimum of 2,000 cells, universal unspliced fraction, or rule that root cells must have the highest unspliced/spliced ratio. Coverage of relevant kinetic states and measurement quality matter. Negative velocity can represent repression or model misspecification; it is not by itself evidence that layers were swapped. Output interpretation Field Interpretation layers['velocity'] Model estimated derivative in processed gene expression space var['velocity genes'] Genes selected for the velocity graph; distinct from all HVGs layers['Ms'] , layers['Mu'] Neighbor averaged linear scale spliced/unspliced expression layers['fit t'] Gene specific fitted time coordinates, dynamical model only var['fit alpha/beta/gamma'] Fitted rates on the model's inferred scale; not calibrated physical rates var['fit likelihood'] Relative model fit diagnostic; not a posterior probability of biological truth uns['velocity graph'] Sparse positive cosine correlations for candidate transitions; not row stochastic obsm['velocity umap'] Projected vectors, only after embedding computation/plotting obs['velocity pseudotime'] Graph based relative ordering obs['latent time'] Coupled dynamical ordering; normally scaled 0–1, not elapsed hours obs['velocity length'] Processed space vector magnitude; not physical cell speed obs['velocity confidence'] Neighbor velocity coherence, not calibrated uncertainty For fate probabilities use an explicitly normalized transition kernel and a validated terminal state definition; see the optional CellRank example in the reference. PAGA requires optional igraph and compatible Scanpy internals; it is not part of the tested core workflow or a substitute for fate inference. Troubleshooting Problem Action Missing or mismatched layers Revisit quantification and explicit ID alignment; X cannot substitute for unspliced counts Very few velocity genes Inspect depth, state coverage and phase portraits before altering thresholds Smooth but implausible arrows Check model assumptions, batch geometry and individual genes; compare independent evidence Nonfinite fit or empty graph Stop interpretation and investigate degenerate features/coverage Excessive memory or fitting time Use a justified gene set and n jobs=1 ; account for dense moments and fit arrays Stochastic failure on NumPy 2 Use a separately validated compatible legacy stack or explicitly reconsider the model pandas unique error in fit/plot Use the tested pandas 2.3.3 pin Sources and verification scope Reviewed 2026 10 01 against the [released source](https://github.com/theislab/scvelo/tree/v0.3.4), [API](https://scvelo.readthedocs.io/en/latest/api.html), [kinetic model caveats](https://scvelo.readthedocs.io/en/latest/perspectives/Perspectives.html), and [AnnData loom reader](https://anndata.readthedocs.io/en/stable/generated/anndata.io.read loom.html). The main paper is [Bergen et al., 2020](https://doi.org/10.1038/s41587 020 0591 3). Synthetic execution tests verify software contracts and file/figure production; they do not establish biological accuracy, identifiability or dataset specific parameter recovery. Optional CellRank/PAGA analyses remain source reviewed, illustrative extensions. No authenticated remote service is used.