LLMs-Universal-Life-Science-and-Clinical-Skills- rna-velocity

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name: rna-velocity description: Infer transcriptional dynamics from spliced/unspliced layers using scVelo with latent time, driver gene ranking, and velocity graph exports. measurable_outcome: Deliver annotated .h5ad files containing velocity layers, latent time, confidence metrics, and ranked driver genes with Markdown + PNG diagnostics. allowed-tools:

• read_file
• run_shell_command
• python reliability:
• source: https://github.com/theislab/scvelo score: 0.93 rationale: >- Canonical scVelo implementation maintained by Theis Lab with validated dynamical/stochastic models and active release cadence.
• source: https://github.com/scverse/single-cell-best-practices score: 0.90 rationale: >- Community maintained best-practices playbook from the scverse consortium covering velocity QC, parameter defaults, and benchmarking datasets.

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At-a-Glance

• description (10-20 chars): Velocity mapper
• keywords: scRNAseq, velocity, scVelo, latent-time, kinetics

Workflow

1. Input audit – Accept

   .h5ad

   or

   .loom

   matrices with

   layers['spliced']

   /

   layers['unspliced']

   . Validate counts (non-negative) and gene overlap.
2. Preconditioning – Apply

   scv.pp.filter_and_normalize(min_shared_counts=30, n_top_genes=4000)

   and

   scv.pp.moments(n_pcs=30, n_neighbors=30)

   to stabilize kinetics as recommended by scVelo maintainers.
3. Model selection – Default to

   mode="dynamical"

   ; auto-fallback to

   stochastic

   if latent time convergence stalls (>30 iterations without likelihood improvement).
4. Velocity graph + confidence – Run

   scv.tl.velocity_graph

   and

   scv.tl.velocity_confidence

   , persisting matrices plus summary stats (min/median confidence, percent of cells >0.4).
5. Latent time + drivers – Execute

   scv.tl.recover_dynamics

   and

   scv.tl.latent_time

   , then

   scv.tl.rank_velocity_genes(groupby="leiden")

   with CSV/JSON exports of top regulators.
6. Visualization bundle – Render

   velocity_embedding_stream

   ,

   velocity_embedding_grid

   , and per-gene phase portraits for reviewer traceability.

Guardrails

• Fail fast if

  layers

  missing or if >20% sparsity after normalization indicates invalid loom conversion.
• Never overwrite raw

  .h5ad

  ; write

  *_velocity.h5ad

  plus

  result.json

  capturing parameters + git hash of scVelo commit.
• Document heuristics when trimming divergences (e.g., driver gene count, MT filtering) inside the markdown report for FDA audit trails.

Integration Hooks

• Upstream:

  scrna-qc

  ,

  sc-batch-integration

  , doublet workflows.
• Downstream: trajectory agents, ligand-receptor analysis, spatial velocity coupling.

References

• scVelo GitHub issues/wiki for parameter defaults and troubleshooting.
• scverse best-practices notebook for velocity QC decision trees.

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