An automated visualization tool that identifies and labels significant genes in volcano plots using a repulsion algorithm to prevent text overlap.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install volcano-plot-labeler
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 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 bioinformaticians and data scientists. It solves the common problem of cluttered genomic visualizations by automatically identifying the most significant genes based on p-values and fold changes. By leveraging a force-directed repulsion algorithm, this skill ensures that all labels are legible and clearly connected to their data points, making it an essential Openclaw Skills utility for producing publication-ready figures.
Volcano Plot Labeler Use Cases
- Highlighting top differentially expressed genes in genomic research papers.
- Automating quality control visualizations for RNA-seq and proteomics pipelines.
- Generating clean, professional volcano plots for technical presentations without manual label adjustment.
- Integrating automated plotting into large-scale bioinformatics workflows using Openclaw Skills.
How Volcano Plot Labeler Works
- The skill ingests differential expression data and calculates a significance score using the product of absolute log2 Fold Change and negative log10 p-value.
- It selects the top N genes (defaulting to 10) that meet the significance criteria.
- A repulsion algorithm is initialized, placing labels at gene coordinates and then applying simulated forces to push overlapping labels apart.
- The algorithm iteratively optimizes label positions while maintaining a spring force that keeps labels near their source points.
- The final output is rendered with color-coded significance zones and leader lines connecting labels to their specific coordinates.
Volcano Plot Labeler Setup
To get started with this skill, install the required Python dependencies in your environment:
pip install pandas matplotlib numpy scipy
You can then utilize the tool via the command line:
python scripts/main.py --input data/results.csv --output plot.png --gene-col gene_name
Volcano Plot Labeler Data Schema & Taxonomy
The Volcano Plot Labeler processes structured genomic data in CSV or TSV formats. The data taxonomy includes:
| Field | Requirement | Description |
|---|---|---|
| log2FoldChange | Required | Numeric log-ratio of expression changes |
| padj / pvalue | Required | Statistical significance values |
| gene_name | Required | Identifiers used for plot labels |
Generated outputs include high-resolution image files (PNG, PDF, SVG) with structured metadata regarding the significance thresholds applied during generation.
Volcano Plot Labeler Advanced Features
- Force-directed repulsion algorithm for overlap-free labeling.
- Customizable significance ranking logic using combined score metrics.
- Support for multiple export formats including vector-based SVG and PDF.
- Advanced CLI parameters for repulsion force, iterations, and label styling.
- Seamless integration with broader Openclaw Skills automated data pipelines.
SKILL.md
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