pseudotime-trajectory-viz

What This Skill Does

The pseudotime-trajectory-viz skill is a specialized tool designed for the comprehensive visualization of single-cell developmental trajectories. By leveraging advanced frameworks like scanpy, scvelo, and palantir, this skill maps out complex biological differentiation processes, converting raw high-dimensional gene expression data into intuitive pseudotime manifolds. It ensures scientific rigor through a highly structured, reproducible execution workflow that prioritizes validation and consistency, making it an essential asset for researchers needing to generate review-ready visual artifacts.

Installation

To integrate this skill into your environment, use the OpenClaw command-line interface. Run the following command in your terminal: clawhub install openclaw/skills/skills/aipoch-ai/pseudotime-trajectory-viz Ensure your system meets the prerequisite Python 3.9+ requirements and that you have the necessary biological analysis libraries installed as specified in the dependencies section, including anndata and scikit-learn.

Use Cases

• Developmental Biology: Mapping cell lineage hierarchies from progenitor to mature cell states in developmental organoids.
• Disease Progression: Visualizing the transition of immune cells during inflammatory responses or the differentiation of malignant cell subpopulations in cancer studies.
• Quality Control: Validating experimental clusters by projecting them onto a pseudotime axis to ensure biological consistency before finalizing a manuscript submission.
• Comparison Studies: Contrasting different normalization techniques or clustering outputs by visualizing their respective trajectories side-by-side using a standardized script framework.

Example Prompts

1. "Run the pseudotime-trajectory-viz on my normalized .h5ad dataset located in ./data/raw_experiment_1.h5ad and output the trajectory manifold to ./results/trajectory_viz.png."
2. "Analyze the differentiation lineage of the T-cell population from my single-cell data, using the Palantir method as defined in the scripts/main.py configuration."
3. "Visualize the developmental trajectory of my dataset, assuming the primary root state is identified by cluster 0, and save the resulting pseudotime values in a CSV report."

Tips & Limitations

To maximize the utility of this skill, always verify your input data structure—the script expects standard AnnData objects. Before execution, perform a quick check by running python -m py_compile scripts/main.py to ensure environment compatibility. Note that this skill is optimized for specific analytical pipelines; if your analysis requires custom R-based methods like Slingshot, ensure rpy2 is properly configured in your path. Always review the generated outputs for domain-specific artifacts, and utilize the provided references in references/ for guidance on parameter tuning and threshold selection. Explicitly document any assumptions regarding cell roots or filtering thresholds to ensure your final report is audit-ready.