Estimate RNA velocity, the modeled direction of gene expression change, from single-cell spliced and unspliced counts. Fit models, inspect gene-level plots, visualize trajectories, and explore relative timing and associated genes while checking model limitations.
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Performs RNA velocity analysis with scVelo from spliced and unspliced single-cell RNA counts. Fits deterministic or dynamical models, examines gene phase portraits, builds velocity graphs, estimates relative latent time, and ranks velocity-associated genes. Use for directional trajectory hypotheses and kinetic-model diagnostics alongside Scanpy; velocity alone does not establish cell fate or causal drivers.
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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 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.
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:
uv venv --python 3.13 .venv-velocity
uv pip install --python .venv-velocity/bin/python \
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 loompy==3.0.8
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.
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.scv.read and older
preprocessing calls. Check the 0.3.4 source
when a tutorial disagrees with the installed API.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.Read H5AD through AnnData. scVelo 0.3.4 has no scv.read or scv.DataFrame:
import anndata as ad
adata = ad.read_h5ad("velocity_counts.h5ad")
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.
The bundled script accepts local files and never downloads a demonstration dataset on startup:
MPLBACKEND=Agg .venv-velocity/bin/python scripts/rna_velocity_workflow.py \
velocity_counts.h5ad --mode dynamical --groupby clusters \
--n-top-genes 2000 --n-neighbors 30 --n-jobs 1 --output-dir velocity_results
# Import a loom and align its raw counts to an existing annotation file:
MPLBACKEND=Agg .venv-velocity/bin/python scripts/rna_velocity_workflow.py \
counts.loom --processed-h5ad annotated.h5ad --groupby clusters \
--mode deterministic --output-dir velocity_check
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:
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.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.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.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.uns['velocity_workflow']['diagnostics']; a
constant pseudotime does not support a trajectory ordering. Ranking is exploratory.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.
After a dynamical run (dataset-specific gene selection is illustrative):
import pandas as pd
import scvelo as scv
# Candidate kinetic genes, not experimentally established drivers.
candidates = adata.var['fit_likelihood'].dropna().nlargest(6).index.tolist()
scv.pl.velocity(adata, var_names=candidates, basis='umap', show=False)
# This ranks group-associated velocities, not causal influence or condition DE.
scv.tl.rank_velocity_genes(adata, groupby='clusters', min_corr=0.3)
ranked = pd.DataFrame(adata.uns['rank_velocity_genes']['names'])
scv.tl.velocity_confidence(adata)
scv.pl.scatter(adata, color=['velocity_length', 'velocity_confidence'], show=False)
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.
| 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.
| 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 |
Reviewed 2026-10-01 against the released source, API, kinetic-model caveats, and AnnData loom reader. The main paper is Bergen et al., 2020. 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.
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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.
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Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.
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Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
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Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.
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