spatial-trajectory
OfficialMap cell fate and differentiation pathways.
Data & Analytics#bioinformatics#pseudotime#single-cell#cell differentiation#rna velocity#trajectory inference
AuthorTianGzlab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill automates the complex process of understanding cellular differentiation and developmental trajectories from single-cell RNA sequencing data, transforming raw data into clear biological insights.
Core Features & Use Cases
- Trajectory Inference: Utilizes multiple state-of-the-art algorithms (DPT, PAGA, Monocle3, Slingshot) to model cell lineage.
- RNA Velocity Analysis: Predicts future cell states and differentiation dynamics using scVelo.
- Pseudotime Ordering: Assigns a developmental time to each cell along inferred trajectories.
- Use Case: Analyze a dataset of developing immune cells to identify distinct differentiation paths, predict progenitor cell populations, and visualize the flow of cells towards mature states.
Quick Start
Infer cellular trajectories and pseudotime using the DPT method on the provided AnnData file.
Dependency Matrix
Required Modules
scanpyscvelorpy2
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: spatial-trajectory Download link: https://github.com/TianGzlab/OmicsClaw/archive/main.zip#spatial-trajectory Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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