feat(bokeh): implement volcano-basic

[github-actions]

Implementation: volcano-basic - bokeh

Implements the bokeh version of volcano-basic.

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

Parent Issue: #2924

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The volcano plot displays 2000 simulated gene expression data points. The x-axis shows "Log₂ Fold Change" ranging from approximately -4 to 6, and the y-axis shows "-Log₁₀ (P-value)" ranging from 0 to about 2.6. Three categories of points are color-coded: gray points (Not significant) form a dense cloud in the center/lower portion, blue points (Down-regulated) appear in the upper-left quadrant representing genes with negative fold changes and high significance, and red points (Up-regulated) appear in the upper-right quadrant representing genes with positive fold changes and high significance. Three dashed threshold lines are present: a horizontal line at approximately y=1.3 (p-value threshold of 0.05) and vertical lines at x=±1 (fold change thresholds). The legend is positioned in the top-right corner with a light background. The overall background is a subtle light gray (#FAFAFA).

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 36pt, axis labels at 28pt, tick labels at 22pt - all clearly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements
• VQ-03: Element Visibility (7/8) - Markers well-sized for 2000 points (size 18-25 with alpha 0.5-0.7), slight deduction as non-significant points could use slightly more alpha contrast
• VQ-04: Color Accessibility (3/5) - Uses red/blue for up/down which is acceptable but not optimal for colorblind users; gray for non-significant is good
• VQ-05: Layout Balance (5/5) - Good canvas utilization, plot fills adequate space
• VQ-06: Axis Labels (1/2) - Descriptive labels but no units (though log values are unitless)
• VQ-07: Grid & Legend (2/2) - Subtle dotted grid with alpha 0.3, legend well-placed with good styling

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct volcano plot implementation
• 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 line at ~1.3, vertical lines at ±1, color coding for significance, alpha transparency
• SC-04: Data Range (3/3) - All data visible, axes auto-scaled appropriately
• SC-05: Legend Accuracy (2/2) - Legend labels correctly identify all three categories
• SC-06: Title Format (2/2) - Uses exact format "volcano-basic · bokeh · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows up-regulated, down-regulated, and non-significant genes with realistic correlation between fold change magnitude and significance; missing top gene labels as suggested in spec notes
• DQ-02: Realistic Context (7/7) - Simulated differential gene expression results - appropriate scientific context
• DQ-03: Appropriate Scale (4/5) - 2000 genes with reasonable fold change (-4 to 6) and p-value ranges; slightly wide range on positive side

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear script: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (0/1) - Minor: could use more modern bokeh patterns
• CQ-05: Output Correct (1/1) - Saves as plot.png

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ColumnDataSource, Span for threshold lines, figure with scatter - standard Bokeh usage but no advanced features like hover tooltips or interactive callbacks

Strengths

• Excellent implementation of volcano plot with all required threshold lines and color coding
• Well-scaled for 4800x2700 canvas with appropriately sized text and markers
• Clean separation of data into three ColumnDataSources enables proper legend handling
• Realistic simulated gene expression data with correlation between fold change magnitude and significance
• Proper use of alpha transparency for dense data visualization
• Clean, readable code following KISS principles

Weaknesses

• Red/blue color scheme not optimal for colorblind accessibility - consider using colorblind-safe palette (e.g., orange/blue or viridis-derived colors)
• Missing optional feature: labeling of top significant genes by name as suggested in spec notes
• No hover tooltips to identify individual points - a key Bokeh interactive feature that would enhance the visualization

Verdict: APPROVED