Guide scVelo RNA velocity workflows from data loading to visualization.
npx skills add https://github.com/Ketomihine/my_skills --skill scvelo-complete
Please help me install this Agent Skill. Skill: scvelo-complete Source: https://github.com/Ketomihine/my_skills/tree/main/scvelo-complete Command: npx skills add https://github.com/Ketomihine/my_skills --skill scvelo-complete
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.
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)
High-intent search queries and answers about installing and using this skill.
RNA velocity analysis estimates splicing kinetics to infer developmental trajectories. You can achieve this by preprocessing data, calculating moments, estimating velocities, and visualizing the results using the scVelo API workflow.
Stochastic and dynamical velocity estimation are two modes within the scVelo workflow. Dynamical modeling infers full kinetics and latent time, whereas stochastic mode estimates velocities based on steady-state assumptions without learning dynamic parameters.
To visualize RNA velocity results, use scVelo's plotting API to generate velocity embedding graphs and latent time visualizations. This allows you to interpret single-cell developmental trajectories and lineage relationships directly from your dataset.
Yes, you can use scVelo to analyze developing cortex cells by preprocessing the dataset, estimating velocities, and visualizing the velocity embedding. This workflow infers lineage relationships by modeling splicing kinetics and latent time.
The minimal scVelo workflow involves loading a dataset, filtering and normalizing the data, computing moments, running velocity estimation, and plotting the velocity embedding using commands like scv.tl.velocity and scv.pl.velocity_embedding.
OwnLabAICompute RNA velocity and latent time from scRNA-seq data.
StarlitnightlyInterpolate missing single-cell trajectory states using matched bulk RNA-seq data.
omicverseAnalyze single-cell AnnData RNA velocity workflows and refine velocity outputs.
StarlitnightlyReproduce and extend single-trajectory analysis workflows across PAGA, Palantir, VIA, and scVelo.
