Upset Plot Converter

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

Upset Plot Converter

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

When to Use

• Use this skill when the task is to Convert complex Venn diagrams with more than 4 sets to clearer Upset.
• 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

• Scope-focused workflow aligned to: Convert complex Venn diagrams with more than 4 sets to clearer Upset.
• Packaged executable path(s): scripts/main.py.
• 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.

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/upset-plot-converter"
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.
• 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

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.

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