An automated tool for identifying and labeling significant genes in volcano plots using a force-directed repulsion algorithm to ensure clean, publication-ready visualizations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install volcano-plot-labeler-1
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install volcano-plot-labeler-1 using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Volcano Plot Labeler?
The Volcano Plot Labeler is a specialized tool within the Openclaw Skills ecosystem designed for bioinformatics and data science. It automates the complex task of labeling the top 10 most significant genes in differential expression plots. By utilizing a force-directed repulsion algorithm, it ensures that text labels do not overlap, solving a common pain point in scientific data visualization. This skill provides a reproducible workflow that bridges the gap between raw data and review-ready interpretation.
Built for researchers and developers, it supports explicit validation and structured outputs. Whether used as a CLI tool or a Python library, it integrates seamlessly into existing data pipelines to enhance the clarity of genomic and transcriptomic analyses through professional Openclaw Skills integration.
Volcano Plot Labeler Use Cases
- Automatically label top significant genes in volcano plots with repulsion for academic publications.
- Generate clean differential expression visualizations where gene names do not overlap.
- Automate bioinformatics data analysis tasks requiring reproducible output formats and documented assumptions.
- Process large genomic datasets to identify key biomarkers based on p-value and fold change thresholds using Openclaw Skills.
How Volcano Plot Labeler Works
- Validate the user objective, input data files, and scope limits to ensure compatibility with the analysis engine.
- Rank genes by a combined significance score calculated from absolute log2 fold change and p-values.
- Apply a force-directed repulsion algorithm to calculate optimal label positions, balancing repulsive forces against spring forces pulling labels toward their data points.
- Execute the Python processing script to generate the visualization in the requested format (PNG, PDF, or SVG).
- Return a structured result including the final plot, documented assumptions, and a summary of identified significance.
Volcano Plot Labeler Setup
To get started with this skill, install the necessary dependencies and verify the environment:
pip install pandas matplotlib numpy scipy
# Navigate to the skill directory
cd "scientific-skills/Data Analytics/volcano-plot-labeler"
# Verify the entry point
python -m py_compile scripts/main.py
Volcano Plot Labeler Data Schema & Taxonomy
The skill expects structured tabular data (CSV/TSV) and produces high-resolution visual artifacts. The following schema is required for input data:
| Column | Description |
|---|---|
| log2FoldChange | Log2 fold change values for gene expression |
| padj or pvalue | Adjusted or raw p-values for statistical significance |
| gene_name | Unique gene identifiers used for text labels |
Output files include labeled volcano plots with color-coded points indicating up-regulation, down-regulation, or non-significance, accompanied by leader lines for gene labels.
Volcano Plot Labeler Advanced Features
- Smart Gene Selection: Uses a combined score of |log2FC| * -log10(pvalue) to prioritize labels.
- Custom Repulsion Logic: Configurable iteration counts and force strengths to handle high-density gene plots.
- Multi-format Export: Native support for PNG, PDF, and SVG to support different publication and editing needs.
- CLI Integration: Fully automated command-line interface for integration into larger Openclaw Skills bioinformatics pipelines.
SKILL.md
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