Volcano Plot Labeler

Analyze data with `volcano-plot-labeler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.

Security Scan

The skill appears to implement a self-contained volcano-plot labeling script consistent with its description and requests no credentials or unusual privileges, though there are small documentation/package inconsistencies to verify before running.

Purpose & Capability

Name/description match the included artifact: scripts/main.py implements label_volcano_plot and a CLI for generating labeled volcano plots. Required libraries (numpy, pandas, matplotlib) are appropriate for the stated purpose.

Instruction Scope

SKILL.md stays within the stated scope (validate inputs, run the packaged script, return artifacts). Minor inconsistencies: SKILL.md's installation block lists 'scipy' in pip install but requirements.txt lacks scipy; SKILL.md shows example 'from volcano_plot_labeler import label_volcano_plot' even though the repository includes only scripts/main.py (not an installed package). These are quality/documentation issues but not direct security concerns.

Install Mechanism

No install spec — instruction-only with a packaged script. No remote downloads or extraction. Dependencies are standard Python packages declared locally (requirements.txt).

Credentials

The skill requests no environment variables, credentials, or config paths. The script operates on user-provided CSVs and writes standard image outputs — this is proportionate to its purpose.

Persistence & Privilege

always:false and no modifications to other skills or system-wide configs. The skill does not request persistent presence or elevated privileges.

Assessment

This skill appears coherent and self-contained, but check a few things before running: 1) Run the recommended smoke check: python -m py_compile scripts/main.py and inspect scripts/main.py yourself to confirm behavior. 2) Install dependencies in a sandboxed venv: pip install -r requirements.txt (note SKILL.md also mentions scipy; install only what you need). 3) Be aware the examples assume importable package semantics but only scripts/main.py is provided — run the script directly or adapt for import. 4) Provide only non-sensitive sample data for initial runs (the script reads CSVs and writes image files). If you need higher assurance, paste the full remainder of scripts/main.py for a complete review (the provided file preview was truncated).

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

• Use this skill when the task needs Automatically label top significant genes in volcano plots with repulsion.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Analyze data with volcano-plot-labeler using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

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.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

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

• Smart Gene Selection: Automatically identifies the top 10 most significant genes based on p-value and fold change
• Repulsion Algorithm: Uses force-directed positioning to prevent text label overlap
• Customizable: Configurable thresholds, label styling, and positioning options
• Multiple Output Formats: PNG, PDF, SVG support

Installation

pip install pandas matplotlib numpy scipy

Usage

Basic Usage

from volcano_plot_labeler import label_volcano_plot
import pandas as pd

# Load 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_plot

fig = 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 values
• padj or pvalue: Adjusted p-values or raw p-values
• gene_name: Gene identifiers

Algorithm

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 significance

Repulsion 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 points

Parameters

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

• Labeled volcano plot with:
  • Color-coded points (up/down/not significant)
  • Top 10 gene labels with leader lines
  • No overlapping text labels

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

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

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

• Required inputs: the user goal, the primary data or source file, and the requested output format.
• Optional inputs: output directory, formatting preferences, and validation constraints.
• If a required input is unavailable, return a short clarification request before continuing.

Output Contract

• Return a short summary, the main deliverables, and any assumptions that materially affect interpretation.
• If execution is partial, label what succeeded, what failed, and the next safe recovery step.
• Keep the final answer within the documented scope of the skill.

Validation and Safety Rules

• Validate identifiers, file paths, and user-provided parameters before execution.
• Do not fabricate results, metrics, citations, or downstream conclusions.
• Use safe fallback behavior when dependencies, credentials, or required inputs are missing.
• Surface any execution failure with a concise diagnosis and recovery path.