Upset Plot Converter

Convert complex Venn diagrams (more than 4 sets) to clearer Upset Plots.

Usage

from skills.upset_plot_converter.scripts.main import convert_venn_to_upset

# From set data
sets = {
    'A': {1, 2, 3, 4, 5},
    'B': {4, 5, 6, 7, 8},
    'C': {3, 5, 7, 9, 10},
    'D': {2, 4, 6, 8, 10},
    'E': {1, 3, 5, 7, 9}
}
convert_venn_to_upset(sets, output_path="upset_plot.png")

# From list data
from skills.upset_plot_converter.scripts.main import upset_from_lists
set_names = ['Genes A', 'Genes B', 'Genes C', 'Genes D', 'Genes E']
lists = [
    ['gene1', 'gene2', 'gene3'],
    ['gene2', 'gene4', 'gene5'],
    ['gene3', 'gene5', 'gene6'],
    ['gene7', 'gene8', 'gene9'],
    ['gene1', 'gene10', 'gene11']
]
upset_from_lists(set_names, lists, output_path="gene_upset.png", title="Gene Intersections")

Input

• sets: Dictionary of set names to sets/lists of elements, OR
• set_names: List of set names
• lists: List of lists (each containing elements)
• output_path: Path to save the output figure
• title: Optional title for the plot
• min_subset_size: Minimum subset size to display (default: 1)
• max_intersections: Maximum number of intersections to show (default: 30)

Output

PNG file of the Upset Plot visualization.

Notes

• When Venn diagrams exceed 4 sets, they become difficult to read
• Upset Plots provide a clearer alternative for visualizing set intersections
• The x-axis shows set intersections as dot patterns
• Bar heights represent the size of each intersection
• Automatically sorts intersections by size for better readability

Requirements

• matplotlib
• numpy
• pandas

Risk Assessment

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