feat(seaborn): implement manhattan-gwas

[github-actions]

Implementation: manhattan-gwas - seaborn

Implements the seaborn version of manhattan-gwas.

File: plots/manhattan-gwas/implementations/seaborn.py

Parent Issue: #2925

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The Manhattan plot displays simulated GWAS data with ~10,000 SNPs across 22 chromosomes. The x-axis shows chromosome positions with labels 1-22 centered beneath each chromosome's region. The y-axis shows -log₁₀(p-value) ranging from 0 to ~12. Points are colored with alternating blue (#306998) and light blue/gray (#8B9DC3) for adjacent chromosomes. Three distinct significant peaks are visible: a major peak on chromosome 6 reaching -log₁₀(p) ≈ 12 (highlighted in yellow), a moderate peak on chromosome 11 reaching ≈ 9, and a smaller peak on chromosome 2 reaching ≈ 8. Yellow markers with black edges highlight SNPs above the genome-wide significance threshold. Two horizontal threshold lines are shown: a red dashed line at y=7.3 (genome-wide significance, p < 5×10⁻⁸) and a gray dotted line at y=5 (suggestive threshold, p < 1×10⁻⁵). The legend is positioned in the upper right. The plot has a clean appearance with only y-axis gridlines and top/right spines removed.

Quality Score: 92/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 12-14pt, all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text, chromosome labels well-spaced
• VQ-03: Element Visibility (7/8) - Good marker size (s=15) and alpha (0.7) for dense data; significant hits highlighted well with larger yellow markers
• VQ-04: Color Accessibility (5/5) - Blue/light blue alternating scheme is colorblind-safe; yellow highlights provide excellent contrast
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, legend positioned nicely
• VQ-06: Axis Labels (1/2) - Y-axis has proper subscript notation (-log₁₀(p-value)), but x-axis just says "Chromosome" without indicating it's genomic position
• VQ-07: Grid & Legend (0/2) - Legend entries are clear and well-positioned; however, y-axis grid at alpha=0.3 is appropriate

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct Manhattan plot for GWAS data
• SC-02: Data Mapping (5/5) - X = cumulative genomic position, Y = -log10(p-value)
• SC-03: Required Features (5/5) - Alternating chromosome colors, significance threshold at 7.3, suggestive threshold at 5, significant SNPs highlighted
• SC-04: Data Range (3/3) - Full genome displayed, y-axis shows all data points
• SC-05: Legend Accuracy (2/2) - Legend correctly labels both threshold lines
• SC-06: Title Format (2/2) - Correct format: "manhattan-gwas · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Excellent: shows realistic distribution with multiple peaks of varying significance (chr6 major, chr11 moderate, chr2 smaller)
• DQ-02: Realistic Context (7/7) - Simulates real GWAS patterns: chr6 peak mimics MHC region association, realistic p-value distribution using beta distribution
• DQ-03: Appropriate Scale (3/5) - Chromosome sizes are biologically accurate; however, ~10k SNPs is on the lower end for GWAS (spec mentions 100k-1M typical)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Modern seaborn API used correctly
• CQ-05: Output Correct (1/1) - Saves as plot.png with dpi=300

Library Features (3/5 pts)

• LF-01: Uses seaborn scatterplot with hue for alternating colors (3/5) - Good use of seaborn's scatterplot with palette, but falls back to matplotlib for significant point highlighting rather than using seaborn throughout

Strengths

• Excellent biological realism with chromosome-specific significant peaks mimicking real GWAS patterns (MHC region on chr6)
• Clean visual design with alternating chromosome colors and clear significance thresholds
• Proper handling of dense data with appropriate marker size and transparency
• Correct title format following pyplots.ai conventions
• Yellow highlighting of significant SNPs provides excellent visual emphasis

Weaknesses

• Y-axis grid could be styled more subtly (current alpha=0.3 is slightly prominent)
• Significant SNPs highlighting uses raw matplotlib instead of seaborn; could use a second scatterplot call for consistency

Verdict: APPROVED