feat(seaborn): implement manhattan-gwas

[github-actions]

Implementation: manhattan-gwas - python/seaborn

Implements the python/seaborn version of manhattan-gwas.

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

Parent Issue: #2925

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): The plot displays a Manhattan plot on a warm off-white background (#FAF8F1) with the title "manhattan-gwas · seaborn · anyplot.ai" clearly visible in dark text. The x-axis shows chromosome labels (1-22) centered under alternating green (#009E73) and orange (#D55E00) chromosome blocks. The y-axis displays -log₁₀(p-value) values in dark text with readable tick labels. Data is represented by alternating colored scatter points for each chromosome with appropriate alpha transparency (0.7) for density visualization. Significant SNPs above the genome-wide significance threshold (7.3) are highlighted with larger markers (60px) and dark edge highlighting, creating clear visual hierarchy. Two horizontal threshold lines (dashed at 7.3 for genome-wide significance, dotted at 5 for suggestive threshold) are visible with a legend in the upper right. All text elements are clearly readable against the light background with excellent contrast.

Dark render (plot-dark.png): The same Manhattan plot is rendered on a warm near-black background (#1A1A17). The title, axis labels, and tick labels are all clearly visible in light text (#F0EFE8 for the title, #B8B7B0 for secondary elements). Critically, there are no dark-on-dark rendering failures—all text maintains strong contrast against the dark background. The data colors are identical to the light render: the same alternating green and orange markers form distinct chromosome blocks, and the significant SNPs maintain their larger size and edge highlighting. The threshold lines and legend remain clearly visible. The plot demonstrates correct theme adaptation with only chrome (background, text, spines, grid) flipping between themes while data colors remain constant.

Score: 94/100

Category	Score	Max
Visual Quality	30	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	94	100

Visual Quality (30/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set (title 24pt, axis labels 20pt, tick labels 16pt). Readable at full resolution in both light and dark themes with excellent contrast.
• VQ-02: No Overlap (6/6) - Chromosome labels well-spaced, no collisions. Legend properly positioned without interfering with data.
• VQ-03: Element Visibility (6/6) - Markers clearly visible with appropriate alpha. Significant SNPs distinguished through larger size and edge highlighting.
• VQ-04: Color Accessibility (2/2) - Okabe-Ito palette is inherently colorblind-safe. Adequate contrast maintained. No red-green as sole signal.
• VQ-05: Layout & Canvas (4/4) - 16:9 aspect ratio well-proportioned. No cut-off elements. Generous margins around plot.
• VQ-06: Axis Labels & Title (2/2) - Title follows spec format. Axis labels descriptive with units.
• VQ-07: Palette Compliance (2/2) - First series #009E73 (Okabe-Ito position 1), second #D55E00 (position 2) in correct order. Plot backgrounds #FAF8F1 (light) and #1A1A17 (dark). Both renders theme-correct.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) - Demonstrates thoughtful design choices including alpha blending for density visualization (0.7), larger markers with edge highlighting for significant SNPs, and careful color alternation. However, implementation is fairly straightforward without exceptional visual polish. Defaults partially raised with evidence.
• DE-02: Visual Refinement (5/6) - Top and right spines properly removed. Grid is subtle (y-axis only, alpha=0.10). Whitespace generous. Minor room for additional refinement.
• DE-03: Data Storytelling (5/6) - Clear visual hierarchy guides the viewer toward significant peaks. Focal point is obvious through size/color contrast. Could have slightly stronger emphasis.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correctly implements Manhattan plot for GWAS data visualization.
• SC-02: Required Features (4/4) - All specification features present: alternating chromosome colors, cumulative position mapping, -log10 p-value transformation, genome-wide significance threshold, suggestive threshold.
• SC-03: Data Mapping (3/3) - X-axis correctly shows cumulative genomic position with chromosome labels. Y-axis shows -log10(p-value). All data visible and properly mapped.
• SC-04: Title & Legend (3/3) - Title format correct. Legend shows both threshold lines with descriptive labels.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows all aspects of Manhattan plot including multiple chromosomes, distinct peaks, background signal, and significance thresholds.
• DQ-02: Realistic Context (5/5) - Realistic GWAS data structure with simulated peaks on biologically plausible chromosomes (6, 11, 2). Proper p-value distribution. Neutral context.
• DQ-03: Appropriate Scale (4/4) - ~10,000 SNPs appropriate for GWAS. -log10 p-value scale standard. Realistic chromosome sizes and peak magnitudes.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - No unnecessary functions or classes. Linear, straightforward code.
• CQ-02: Reproducibility (2/2) - Random seed set (np.random.seed(42)) for deterministic output.
• CQ-03: Clean Imports (2/2) - Only necessary packages imported (matplotlib, numpy, pandas, seaborn).
• CQ-04: Code Elegance (2/2) - Appropriate complexity. No fake UI or misleading features.
• CQ-05: Output & API (1/1) - Saves as plot-{THEME}.png with correct DPI (300) and facecolor parameters.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) - Uses sns.set_theme() for global styling setup. Proper matplotlib scatter for custom rendering. Theme-adaptive rc parameters. Correct API usage.
• LM-02: Distinctive Features (3/5) - Uses rasterization (rasterized=True) for performance with dense data. Custom iteration-based color grouping. Could leverage more seaborn-specific features.

Score Caps Applied

• None - No score caps triggered. All quality criteria maintained.

Strengths

• Excellent visual quality: both light and dark renders are highly readable with proper contrast and theme adaptation
• Correct Manhattan plot implementation with all GWAS specification features (alternating colors, cumulative positioning, significance thresholds)
• Realistic and well-structured GWAS data generation with meaningful peaks on specific chromosomes and proper p-value distribution
• Professional use of Okabe-Ito palette with first series as brand green (#009E73) and clean color alternation
• Clean, idiomatic seaborn/matplotlib code with proper theme tokens and adaptive styling
• Effective visual hierarchy through alpha blending (0.7) for regular SNPs and larger size/edge highlighting for significant SNPs
• Proper performance optimization using rasterization for dense scatter data

Weaknesses

• Design Excellence could be stronger (16/20) — while solid and professional, could benefit from more intentional visual refinement or emphasis techniques
• Could leverage more distinctive seaborn-specific features beyond basic scatter (e.g., palette objects, advanced seaborn stylistic conventions)

Issues Found

1. DE-01 MEDIUM: Design could be more sophisticated with additional visual polish
   • Fix: Consider adding subtle color gradients on background, more intentional typography variations, or enhanced emphasis on key findings through additional visual techniques
2. LM-02 MEDIUM: Could use more distinctive seaborn features
   • Fix: Explore seaborn-specific palette handling or stylistic conventions that would add distinctive library character

AI Feedback for Next Attempt

The implementation is solid and production-ready. For higher scores, focus on design sophistication: consider additional visual refinement through typography variations, subtle emphasis techniques, or enhanced focal point design. The code is clean and correct; enhancement should be purely visual/design-focused.

Verdict: APPROVED