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Forest Plot Styler

Beautify meta-analysis forest plots with customizable odds ratio points and confidence intervals

personAuthor: aipoch-aihubclawhub
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Forest Plot Styler

ID: 157

Beautifies Meta-analysis or subgroup analysis forest plots, customizes Odds Ratio point sizes and confidence interval line styles.

Features

  • Reads Meta-analysis data (CSV/Excel format)
  • Draws high-quality forest plots
  • Customizes Odds Ratio point sizes, colors, and shapes
  • Customizes confidence interval line styles (color, thickness, endpoint style)
  • Supports subgroup analysis display
  • Automatically calculates and displays pooled effect values
  • Outputs to PNG, PDF, or SVG format

Usage

python scripts/main.py --input <data.csv> [options]

Parameters

| Parameter | Type | Default | Required | Description | |-----------|------|---------|----------|-------------| | --input, -i | string | - | Yes | Input data file (CSV or Excel) | | --output, -o | string | forest_plot.png | No | Output file path | | --format, -f | string | png | No | Output format (png/pdf/svg) | | --point-size | int | 8 | No | OR point size | | --point-color | string | #2E86AB | No | OR point color | | --ci-color | string | #2E86AB | No | Confidence interval line color | | --ci-linewidth | int | 2 | No | Confidence interval line thickness | | --ci-capwidth | int | 5 | No | Confidence interval endpoint width | | --summary-color | string | #A23B72 | No | Pooled effect point color | | --summary-shape | string | diamond | No | Pooled effect point shape | | --subgroup | string | - | No | Subgroup analysis column name | | --title, -t | string | Forest Plot | No | Chart title | | --xlabel, -x | string | Odds Ratio (95% CI) | No | X-axis label | | --reference-line | float | 1.0 | No | Reference line position | | --width, -W | int | 12 | No | Image width (inches) | | --height, -H | int | auto | No | Image height (inches) | | --dpi | int | 300 | No | Image resolution | | --font-size | int | 10 | No | Font size | | --style, -s | string | default | No | Preset style (default/minimal/dark) |

Input Data Format

CSV/Excel files must contain the following columns:

| Column Name | Description | Type | |------|------|------| | study | Study name | Text | | or | Odds Ratio value | Numeric | | ci_lower | Confidence interval lower bound | Numeric | | ci_upper | Confidence interval upper bound | Numeric | | weight | Weight (optional, for point size) | Numeric | | subgroup | Subgroup label (optional) | Text |

Sample Data

study,or,ci_lower,ci_upper,weight,subgroup
Study A,0.85,0.65,1.12,15.2,Drug A
Study B,0.72,0.55,0.94,18.5,Drug A
Study C,1.15,0.88,1.50,12.3,Drug B
Study D,0.95,0.75,1.20,14.8,Drug B

Examples

Basic Usage

python scripts/main.py -i meta_data.csv

Custom Style

python scripts/main.py -i meta_data.csv \
    --point-color="#E63946" \
    --ci-color="#457B9D" \
    --point-size=10 \
    --ci-linewidth=3 \
    -t "Meta-Analysis of Treatment Effects"

Subgroup Analysis

python scripts/main.py -i meta_data.csv \
    --subgroup subgroup_column \
    --summary-color="#F4A261" \
    -o subgroup_forest.png

Output PDF Vector Graphic

python scripts/main.py -i meta_data.csv \
    -f pdf \
    -o forest_plot.pdf

Preset Styles

default

  • Blue color scheme
  • Standard font size
  • White background

minimal

  • Clean lines
  • Grayscale color scheme
  • No grid lines

dark

  • Dark background
  • Bright data points
  • Suitable for dark theme presentations

Dependencies

  • Python >= 3.8
  • matplotlib >= 3.5.0
  • pandas >= 1.3.0
  • numpy >= 1.20.0
  • openpyxl >= 3.0.0 (for reading Excel)

Output Example

Generated forest plot contains:

  • Left side: Study name list
  • Middle: OR values and confidence intervals
  • Right side: Weight percentage (if available)
  • Bottom: Pooled effect value (diamond marker)
  • Reference line (OR=1)

Notes

  1. Ensure input file encoding is UTF-8
  2. OR values are automatically converted when log scale is suggested
  3. Studies with confidence intervals crossing 1 are not statistically significant
  4. Weight values are used to adjust point size, reflecting study contribution

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