scvelo-complete

What problem does it solve?

This Skill provides a complete, ready-to-use guide for the scVelo RNA velocity toolkit, enabling researchers to perform RNA velocity analysis, learn the full workflow, and adopt best practices from official documentation.

Core Features & Use Cases

• Comprehensive coverage of the scVelo toolkit including preprocessing, velocity estimation (stochastic and dynamical), latent time inference, visualization, and diagnostics.
• Educational and development use: ideal for new users learning RNA velocity workflows or teams adopting scVelocity for single-cell trajectory analyses.
• Use case: a researcher analyzing developing cortex cells can follow the skill to preprocess data, estimate velocities, visualize velocity embedding, and interpret latent time to infer lineage relationships.

Quick Start

Install scVelo and load a dataset, then run a minimal velocity workflow: preprocess, moments, velocity, and visualization. Example commands: import scvelo as scv; adata = scv.datasets.pancreas(); scv.pp.filter_and_normalize(adata); scv.tl.velocity(adata); scv.pl.velocity_embedding(adata)