feat(seaborn): implement volcano-basic

[github-actions]

Implementation: volcano-basic - seaborn

Implements the seaborn version of volcano-basic.

File: plots/volcano-basic/implementations/seaborn.py

Parent Issue: #2924

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a volcano plot with 500 data points representing simulated differential gene expression data. The x-axis shows "Log2 Fold Change" ranging from approximately -3.5 to 4.5, and the y-axis shows "-Log10(p-value)" ranging from 0 to about 7. Three categories of points are shown: gray points for "Not Significant" genes clustered around the center with low p-values, blue points for "Down-regulated" genes on the left side (negative fold change, high significance), and red/coral points for "Up-regulated" genes on the right side (positive fold change, high significance). Horizontal dashed threshold line at p=0.05 (y≈1.3) and vertical dashed lines at FC=0.5 and FC=2 (log2FC=±1) are clearly visible. The legend is positioned in the upper right corner. The title follows the correct format: "volcano-basic · seaborn · pyplots.ai".

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Markers well-sized (s=100) with alpha=0.7, appropriate for 500 points. Slight deduction as some gray points overlap heavily in the center
• VQ-04: Color Accessibility (4/5) - Blue and red/coral colors are distinguishable, though red-blue isn't the most colorblind-safe combination (could use viridis-based colors)
• VQ-05: Layout Balance (5/5) - Plot fills canvas well, balanced margins
• VQ-06: Axis Labels (1/2) - Descriptive labels but no units (though technically log values are unitless)
• VQ-07: Grid & Legend (1/2) - Grid is subtle (alpha=0.3), legend well-placed but slightly overlaps plot area

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct volcano plot with scatter points
• SC-02: Data Mapping (5/5) - X=log2 fold change, Y=-log10(p-value) correctly mapped
• SC-03: Required Features (5/5) - All spec features present: horizontal threshold at p=0.05, vertical thresholds at ±1, three-color significance coding
• SC-04: Data Range (3/3) - Axes show all data appropriately
• SC-05: Legend Accuracy (2/2) - Legend labels correct and meaningful
• SC-06: Title Format (2/2) - Correctly uses "volcano-basic · seaborn · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows up-regulated, down-regulated, and non-significant genes well. Missing gene labels for top hits as suggested in spec (optional feature)
• DQ-02: Realistic Context (7/7) - Simulated differential expression data with realistic distribution pattern (volcano shape)
• DQ-03: Appropriate Scale (4/5) - Values are realistic for omics data, though -log10(p-value) reaching ~6 suggests some very low p-values

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean flow: imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports used
• CQ-04: No Deprecated API (1/1) - Modern seaborn API with hue parameter
• CQ-05: Output Correct (0/1) - Saves as plot.png but minor: could use plt.show() commented for interactive use

Library Features (3/5 pts)

• LF-01: Uses seaborn's scatterplot with hue categorization (3/5) - Good use of seaborn's hue and palette features, but doesn't leverage more advanced seaborn features like FacetGrid or statistical annotations

Strengths

• Excellent implementation of the classic volcano plot pattern with clear visual separation of significance categories
• Proper threshold annotations (p=0.05, FC=2, FC=0.5) provide context for the significance cutoffs
• Clean data generation with realistic bimodal distribution for differentially expressed genes
• Good use of seaborn's hue parameter with custom palette matching the specification colors
• Sorting mechanism ensures significant points are plotted on top and remain visible

Weaknesses

• Color palette could be more colorblind-friendly (consider using blue-orange instead of blue-red)
• Missing optional gene labels for top significant features mentioned in the spec notes

Verdict: APPROVED