Forest Plot Styler#

ID: 157

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

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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

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Usage#

bash

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

Parameters#

ParameterTypeDefaultRequiredDescription--input, -istring-YesInput data file (CSV or Excel)--output, -ostringforest_plot.pngNoOutput file path--format, -fstringpngNoOutput format (png/pdf/svg)--point-sizeint8NoOR point size--point-colorstring#2E86ABNoOR point color--ci-colorstring#2E86ABNoConfidence interval line color--ci-linewidthint2NoConfidence interval line thickness--ci-capwidthint5NoConfidence interval endpoint width--summary-colorstring#A23B72NoPooled effect point color--summary-shapestringdiamondNoPooled effect point shape--subgroupstring-NoSubgroup analysis column name--title, -tstringForest PlotNoChart title--xlabel, -xstringOdds Ratio (95% CI)NoX-axis label--reference-linefloat1.0NoReference line position--width, -Wint12NoImage width (inches)--height, -HintautoNoImage height (inches)--dpiint300NoImage resolution--font-sizeint10NoFont size--style, -sstringdefaultNoPreset style (default/minimal/dark)

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Input Data Format#

CSV/Excel files must contain the following columns:

Column NameDescriptionTypestudyStudy nameTextorOdds Ratio valueNumericci_lowerConfidence interval lower boundNumericci_upperConfidence interval upper boundNumericweightWeight (optional, for point size)NumericsubgroupSubgroup label (optional)Text

Sample Data#

csv

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

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Examples#

Basic Usage#

bash

python scripts/main.py -i meta_data.csv

Custom Style#

bash

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#

bash

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

Output PDF Vector Graphic#

bash

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

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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

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Dependencies#

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

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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)

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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 IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

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#

bash

# 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