Volcano Plot Labeler

Source: https://github.com/aipoch/medical-research-skills

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

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