tardigrade-gwas 1.0.0

Multi-GWAS gene-overlap analysis, similarity heatmaps, Circos-style visualization, and reporting.

Tardigrade GWAS

A lightweight Python library for multi-GWAS gene-overlap analysis, Jaccard similarity heatmaps, Circos-style visualization, and text/CSV reporting.

Developed by Amir Izadi and Zahra Aghabeygi.

Installation

pip install tardigrade-gwas

Analyze multiple GWAS files

from tardigrade_gwas import analyze_files

result = analyze_files(
    ["disease_a.csv", "disease_b.tsv"],
    names=["Disease A", "Disease B"],
)

print(result.report_text)
print(result.similarity_table)
print(result.consensus_table.head())

Default columns:

• P value: P-VALUE
• Gene: MAPPED_GENE
• Chromosome: CHR_ID
• Position: CHR_POS

The P-value threshold defaults to 5e-8. Common column aliases are detected case-insensitively, and all defaults can be overridden.

Similarity heatmap

from tardigrade_gwas import analyze_files, plot_similarity_heatmap

result = analyze_files(["disease_a.csv", "disease_b.tsv"])
figure = plot_similarity_heatmap(result)
figure.show()

# Save only when you choose to:
figure.savefig("heatmap.png", dpi=300, bbox_inches="tight")

Circos-style plot

from tardigrade_gwas import plot_circos

figure = plot_circos("disease_a.csv")
figure.show()

Supported files

CSV, TSV, TXT, and gzip-compressed CSV/TSV files are supported.

License

MIT License.