Can I use this skill with Scanpy processed data?

Yes, scVelo is designed to complement Scanpy and includes utilities to merge processed H5AD files with velocity information.

What data does scVelo require?

It requires count matrices for both spliced and unspliced mRNA, typically generated by tools like STARsolo or velocyto and stored in an AnnData object.

What is latent time in scVelo?

Latent time is a pseudotime metric derived from the dynamical model that represents the internal developmental clock of a cell based on its splicing kinetics.

How does the dynamical model differ from the stochastic model?

The dynamical model fits the full splicing kinetics for higher accuracy and driver gene identification, while the stochastic model is faster for initial exploration.

Which sequencing pipelines generate the required input?

Pipelines such as STARsolo, velocyto, kallisto|bustools, and alevin-fry produce the necessary spliced and unspliced layers.

scVelo RNA Velocity Analysis

Name: scVelo RNA Velocity Analysis
Author: K-Dense-AI

byK-Dense-AI

•

17,327

•

데이터 과학 및 ML

Performs RNA velocity analysis to infer cell state transitions and developmental trajectories in single-cell transcriptomics data.

scVelo is a specialized Claude Code skill designed for bioinformatics and computational biology. It leverages the scVelo Python package to model mRNA splicing kinetics, allowing researchers to determine the direction of cell differentiation from snapshot single-cell RNA-seq data. By analyzing the ratio of spliced to unspliced transcripts, this skill helps identify driver genes, estimate latent time, and visualize complex developmental lineages, making it an essential tool for advanced trajectory inference and cell fate prediction.

주요 기능

01Visualization of cell transitions with streamlines and embeddings

0217,327 GitHub stars

03RNA velocity estimation using stochastic and dynamical models

04Identification of driver genes through velocity-informed ranking

05Latent time and pseudotime calculation for cell ordering

06Integration with Scanpy for comprehensive single-cell workflows

사용 사례

01Predicting future cell states from single-cell snapshot datasets

02Mapping developmental trajectories in hematopoiesis or neurogenesis

03Identifying key driver genes that govern cell fate transitions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k-dense-ai/claude-scientific-skills scvelo

For use in Claude.ai and ChatGPT

주요 기능

01Visualization of cell transitions with streamlines and embeddings

0217,327 GitHub stars

03RNA velocity estimation using stochastic and dynamical models

04Identification of driver genes through velocity-informed ranking

05Latent time and pseudotime calculation for cell ordering

06Integration with Scanpy for comprehensive single-cell workflows

사용 사례

01Predicting future cell states from single-cell snapshot datasets

02Mapping developmental trajectories in hematopoiesis or neurogenesis

03Identifying key driver genes that govern cell fate transitions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add k-dense-ai/claude-scientific-skills scvelo

For use in Claude.ai and ChatGPT