Volcano Plot Labeler
by @aipoch-ai
Analyze data with `volcano-plot-labeler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
clawhub install volcano-plot-labeler-1π About This Skill
name: volcano-plot-labeler description: Analyze data with
volcano-plot-labeler using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
license: MIT
skill-author: AIPOCH
Volcano Plot Labeler (ID: 148)
Automatically identify and label the Top 10 most significant genes in volcano plots using a repulsion algorithm to prevent label overlap.
When to Use
Key Features
See ## Features above for related details.
volcano-plot-labeler using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.scripts/main.py.references/ for task-specific guidance.Dependencies
See ## Prerequisites above for related details.
Python: 3.10+. Repository baseline for current packaged skills.matplotlib: unspecified. Declared in requirements.txt.numpy: unspecified. Declared in requirements.txt.pandas: unspecified. Declared in requirements.txt.Example Usage
See ## Usage above for related details.
cd "20260318/scientific-skills/Data Analytics/volcano-plot-labeler"
python -m py_compile scripts/main.py
python scripts/main.py --help
Example run plan:
1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.
Implementation Details
See ## Workflow above for related details.
scripts/main.py.references/ contains supporting rules, prompts, or checklists.Quick Check
Use this command to verify that the packaged script entry point can be parsed before deeper execution.
python -m py_compile scripts/main.py
Audit-Ready Commands
Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.
python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan."
Workflow
1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work. 2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions. 3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available. 4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items. 5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.
Features
Installation
pip install pandas matplotlib numpy scipy
Usage
Basic Usage
from volcano_plot_labeler import label_volcano_plot
import pandas as pdLoad your data
df = pd.read_csv('differential_expression_results.csv')Generate labeled volcano plot
fig = label_volcano_plot(
df,
log2fc_col='log2FoldChange',
pvalue_col='padj',
gene_col='gene_name',
top_n=10
)
fig.savefig('volcano_plot_labeled.png', dpi=300, bbox_inches='tight')
Advanced Usage
from volcano_plot_labeler import label_volcano_plotfig = label_volcano_plot(
df,
log2fc_col='log2FoldChange',
pvalue_col='padj',
gene_col='gene_name',
top_n=10,
pvalue_threshold=0.05,
log2fc_threshold=1.0,
figsize=(12, 10),
repulsion_iterations=100,
repulsion_force=0.05,
label_fontsize=10,
label_color='black',
arrow_color='gray',
save_path='output.png'
)
Command Line Usage
python scripts/main.py \
--input data/deseq2_results.csv \
--output volcano_labeled.png \
--log2fc-col log2FoldChange \
--pvalue-col padj \
--gene-col gene_name \
--top-n 10
Input Format
Expected CSV/TSV columns:
log2FoldChange: Log2 fold change valuespadj or pvalue: Adjusted p-values or raw p-valuesgene_name: Gene identifiersAlgorithm
Significance Calculation
1. Calculate-log10(pvalue) for all genes
2. Rank genes by combined score: |log2FC| * -log10(pvalue)
3. Select top N genes with highest significanceRepulsion Algorithm
1. Initial Placement: Place labels at gene coordinates 2. Force Calculation: - Repulsive force between overlapping labels - Spring force pulling label toward its gene point - Boundary forces to keep labels within plot area 3. Iterative Optimization: Update positions for N iterations until convergence 4. Arrow Drawing: Draw connecting lines from labels to gene pointsParameters
| Parameter | Type | Default | Description |
|-----------|------|---------|-------------|
| df | DataFrame | - | Input data |
| log2fc_col | str | 'log2FoldChange' | Column name for log2 fold change |
| pvalue_col | str | 'padj' | Column name for p-value |
| gene_col | str | 'gene_name' | Column name for gene names |
| top_n | int | 10 | Number of top genes to label |
| pvalue_threshold | float | 0.05 | P-value cutoff for coloring |
| log2fc_threshold | float | 1.0 | Log2FC cutoff for coloring |
| repulsion_iterations | int | 100 | Iterations for repulsion algorithm |
| repulsion_force | float | 0.05 | Strength of repulsion force |
| label_fontsize | int | 10 | Font size for labels |
| figsize | tuple | (10, 10) | Figure size |
Output
License
MIT
Risk Assessment
| Risk Indicator | Assessment | Level | |----------------|------------|-------| | Code Execution | Python/R scripts executed locally | Medium | | Network Access | No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |
Security Checklist
Prerequisites
Python dependencies
pip install -r requirements.txt
Evaluation Criteria
Success Metrics
Test Cases
1. Basic Functionality: Standard input β Expected output 2. Edge Case: Invalid input β Graceful error handling 3. Performance: Large dataset β Acceptable processing timeLifecycle Status
Output Requirements
Every final response should make these items explicit when they are relevant:
Error Handling
scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.Input Validation
This skill accepts requests that match the documented purpose of volcano-plot-labeler and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
> volcano-plot-labeler only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
Response Template
Use the following fixed structure for non-trivial requests:
1. Objective 2. Inputs Received 3. Assumptions 4. Workflow 5. Deliverable 6. Risks and Limits 7. Next Checks
If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.
Inputs to Collect
Output Contract
Validation and Safety Rules
β‘ When to Use
π‘ Examples
Basic Usage
from volcano_plot_labeler import label_volcano_plot
import pandas as pdLoad your data
df = pd.read_csv('differential_expression_results.csv')Generate labeled volcano plot
fig = label_volcano_plot(
df,
log2fc_col='log2FoldChange',
pvalue_col='padj',
gene_col='gene_name',
top_n=10
)
fig.savefig('volcano_plot_labeled.png', dpi=300, bbox_inches='tight')
Advanced Usage
from volcano_plot_labeler import label_volcano_plotfig = label_volcano_plot(
df,
log2fc_col='log2FoldChange',
pvalue_col='padj',
gene_col='gene_name',
top_n=10,
pvalue_threshold=0.05,
log2fc_threshold=1.0,
figsize=(12, 10),
repulsion_iterations=100,
repulsion_force=0.05,
label_fontsize=10,
label_color='black',
arrow_color='gray',
save_path='output.png'
)
Command Line Usage
python scripts/main.py \
--input data/deseq2_results.csv \
--output volcano_labeled.png \
--log2fc-col log2FoldChange \
--pvalue-col padj \
--gene-col gene_name \
--top-n 10
βοΈ Configuration
Python dependencies
pip install -r requirements.txt