feat(matplotlib): implement manhattan-gwas

[github-actions]

Implementation: manhattan-gwas - matplotlib

Implements the matplotlib version of manhattan-gwas.

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

Parent Issue: #2925

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The Manhattan plot displays GWAS results across 22 chromosomes on the x-axis with -log₁₀(p-value) on the y-axis. Chromosomes are clearly labeled 1-22 along the bottom. Points alternate between dark blue (#306998) and light blue (#6699CC) for adjacent chromosomes, creating clear visual distinction. A red dashed horizontal line marks the genome-wide significance threshold at -log₁₀(5×10⁻⁸) ≈ 7.3, and a yellow dashed line marks the suggestive threshold at -log₁₀(1×10⁻⁵) = 5. Significant SNPs above the genome-wide threshold are highlighted in red with larger markers and white edges. Several significant peaks are visible on chromosomes 2, 6, 11, and 16, with the highest peak around -log₁₀(p) ≈ 14.5 on chromosome 2. The title "manhattan-gwas · matplotlib · pyplots.ai" is displayed at the top. A legend in the upper right explains the threshold lines. The plot has clean styling with no top/right spines.

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 14-16pt, all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, chromosome labels well-spaced
• VQ-03: Element Visibility (7/8) - Markers appropriately sized for ~110k points (s=15 for regular, s=50 for significant); very dense at bottom but that's expected for GWAS data
• VQ-04: Color Accessibility (5/5) - Blue alternating colors are colorblind-safe, red for significant hits provides clear distinction
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins, legend well-positioned
• VQ-06: Axis Labels (1/2) - Y-axis has proper subscript notation (-log₁₀(p-value)), but no units needed for this context; X-axis says "Chromosome" which is descriptive
• VQ-07: Grid & Legend (0/2) - No grid present (acceptable for Manhattan plots to reduce visual clutter), legend is well-placed but partially redundant since threshold lines are self-explanatory

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct Manhattan plot visualization
• SC-02: Data Mapping (5/5) - Cumulative genomic position on X, -log₁₀(p-value) on Y, correctly assigned
• SC-03: Required Features (5/5) - All spec features present: alternating chromosome colors, genome-wide significance threshold line at 7.3, suggestive threshold at 5, significant SNPs highlighted in different color
• SC-04: Data Range (3/3) - Axes show all data with appropriate padding (1.1× on Y, 1.01× on X)
• SC-05: Legend Accuracy (2/2) - Legend correctly describes threshold lines
• SC-06: Title Format (2/2) - Correct format: "manhattan-gwas · matplotlib · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows full range of GWAS data: non-significant baseline, suggestive hits, and highly significant peaks across multiple chromosomes
• DQ-02: Realistic Context (7/7) - Simulated GWAS data with realistic chromosome sizes, appropriate number of SNPs (~40 per Mb), and significant peaks in specific chromosomes mimicking real genetic signals
• DQ-03: Appropriate Scale (5/5) - Realistic p-value ranges, appropriate SNP density, chromosome sizes based on actual human genome

Code Quality (8/10 pts)

• CQ-01: KISS Structure (3/3) - Clean linear structure: imports → data generation → plot → save
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - Only matplotlib, numpy, pandas used - all necessary
• CQ-04: No Deprecated API (0/1) - Uses deprecated ".loc" assignment pattern with chained indexing warning potential (should use .copy() or different approach)
• CQ-05: Output Correct (1/1) - Saves as plot.png with correct parameters

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Good use of matplotlib features: rasterized=True for performance with large datasets, spine removal, proper layering with zorder. Could use more advanced features like PathCollection for better performance or custom tick formatting.

Strengths

• Excellent chromosome visualization with alternating blue colors providing clear visual distinction between chromosomes
• Proper highlighting of significant SNPs with larger red markers and white edges that stand out clearly
• Realistic GWAS data simulation with appropriate chromosome sizes, SNP densities, and significant peaks on specific chromosomes
• Clean code structure with good use of rasterized=True for performance optimization with 110k+ data points
• Both genome-wide and suggestive threshold lines included with clear legend

Weaknesses

• No grid lines present (minor - acceptable for Manhattan plots to reduce clutter)
• Y-axis label could include clearer notation like "-log₁₀(p)" or add context in legend

Verdict: APPROVED