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omicverse-single-cell-cellrank-fate

Maps RNA velocity to CellRank terminal-state probabilities for AnnData workflows.

13|2|Updated Apr 5, 2026
Search Tags:#single-cell#omicverse#anndata#rna-velocity#cellrank#fate-mapping#gpcca
One-click install
npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-cellrank-fate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: omicverse-single-cell-cellrank-fate
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/single-cell-cellrank-fate
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-single-cell-cellrank-fate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill removes the manual work of turning RNA velocity outputs into interpretable cell fate maps, terminal-state calls, and branch-aware marker dynamics for single-cell analysis.

Core Features & Use Cases

  • Mix VelocityKernel and ConnectivityKernel to build a CellRank transition matrix.
  • Run GPCCA to identify macrostates, predict terminal states, and export fate probabilities.
  • Visualize fate probabilities, branch streamplots, dynamic trends, and dynamic heatmaps in OmicVerse.
  • Use it for pancreas or other lineage-trajectory datasets after velocity has already been computed.

Quick Start

Ask the assistant to analyze a velocity-processed AnnData with CellRank, store the Beta fate probability in obs['beta_fate'], and generate branch-aware marker trend plots for the relevant lineage.

Frequently Asked Questions about omicverse-single-cell-cellrank-fate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I predict terminal cell states from RNA velocity data?▼

To map cell fate trajectories from RNA velocity, mix the VelocityKernel and ConnectivityKernel to construct a CellRank transition matrix, then run GPCCA to compute terminal-state probabilities for your single-cell data.

What is the best way to visualize branch-aware gene trends in single-cell lineage data?▼

The best way to visualize branch-aware gene trends in single-cell lineage data is using OmicVerse plotting helpers to generate dynamic trend plots and multi-module heatmaps based on your computed CellRank fate probabilities.

Can I use CellRank GPCCA on a post-velocity AnnData object for fate mapping?▼

Yes, you can use CellRank GPCCA on a post-velocity AnnData object for fate mapping by supplying the required velocity-derived layers and pseudotime to identify macrostates and predict terminal states.

How do I combine VelocityKernel and ConnectivityKernel for single-cell trajectory analysis?▼

You combine the VelocityKernel and ConnectivityKernel by mixing them to build a unified CellRank transition matrix that integrates RNA velocity direction and transcriptomic similarity for single-cell trajectory analysis.

Does this CellRank workflow support pancreas lineage trajectory datasets?▼

Yes, this CellRank workflow supports pancreas lineage trajectory datasets and can be similarly applied to other lineage systems to compute fate probabilities and visualize branch-aware marker dynamics.

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