scvelo

combined

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. 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.

When to use

**Trajectory inference from snapshot data**: Determine which direction cells are differentiating
**Cell fate prediction**: Identify progenitor cells and their downstream fates
**Driver gene identification**: Find genes whose dynamics best explain observed trajectories
**Developmental biology**: Model hematopoiesis, neurogenesis, epithelial-to-mesenchymal transitions
**Latent time estimation**: Order cells along a pseudotime derived from splicing dynamics
**Complement to Scanpy**: Add directional information to UMAP embeddings
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, transitio…

Skill page

Conflict

The two skills describe the same high-level task (RNA velocity with scVelo) but diverge significantly on implementation details, version compatibility, and workflow steps. The K-Dense-AI skill is a highly specific, version-pinned (scVelo 0.3.4, Python 3.13) workflow that explicitly warns against using the stochastic model due to NumPy 2 incompatibility and provides a bundled script. The FreedomIntelligence skill is a general, version-agnostic tutorial that recommends the stochastic model for exploratory analysis and uses older API calls (like `scv.read` and `scv.DataFrame`) that the K-Dense-AI skill notes are removed or deprecated in newer versions. Because they disagree on the recommended model (stochastic vs. dynamical) and the valid API surface, crowning one would hide a real alternative approach or lead to errors if the user's environment matches the other skill's assumptions.