Scientific Publication

Scientific Publication Figure Refinement

Expert guidance for systematically improving scientific figures through iterative refinement based on user feedback and publication requirements.

Supporting files in this directory:

• publication-standards.md - DPI, file formats, size specs, color accessibility
• multi-study-results.md - Writing integrated results from multi-study analyses and practical recommendations from complex trade-offs
• methodological-transparency.md - Dual approach pattern for figures vs statistics, outlier handling
• overleaf-packages.md - Creating production-ready Overleaf packages with templates and checklists

When to Use This Skill

• Improving figures based on reviewer or collaborator feedback
• Optimizing figure clarity and readability
• Ensuring all figure elements fit within bounds
• Deciding between layout alternatives (horizontal vs vertical panels)
• Preparing figures for high-impact publications

Iterative Figure Refinement Workflow

Standard Refinement Sequence

When improving a publication figure, follow this systematic approach:

1. Identify the Core Issue

Examples:
- "Violin plots look distorted on log scale"
- "P-values are cut off at the top"
- "Too much visual clutter, hard to see the data"
- "Text overlaps with data points"

2. Fix the Visualization Type/Method

# Example: Replace inappropriate plot type
# Before: Violin plot on log scale (distorted)
ax.violinplot(data)
ax.set_yscale('log')

# After: Boxplot on log scale (accurate)
ax.boxplot(data)
ax.set_yscale('log')

3. Improve Visual Clarity Systematically adjust element sizes:

# Point sizes: Reduce for dense data
# Start: s=60 (exploratory)
# End: s=25 (publication)
ax.scatter(..., s=25, alpha=0.5)

# Line widths: Thinner reduces clutter
# Start: linewidth=2.5
# End: linewidth=1.5
ax.plot(..., linewidth=1.5)

# Text sizes: Prevent overlap
# Start: fontsize=10-12
# End: fontsize=8-9
ax.text(..., fontsize=8)

# Error bar caps: Keep readable
ax.errorbar(..., capsize=5)

4. Test Layout Alternatives

# Option A: Side-by-side panels
fig, axes = plt.subplots(1, 2, figsize=(16, 7))
# Pros: Direct left-right comparison
# Cons: Smaller individual panels

# Option B: Stacked vertically
fig, axes = plt.subplots(2, 1, figsize=(10, 14))
# Pros: Larger individual panels, easier to read details
# Cons: Harder to compare across panels

# Decision: Let user feedback guide choice
# Generate both, ask which is clearer

5. Optimize Element Positioning Ensure all annotations fit within plot bounds:

# Calculate safe positioning
y_max = max([d.max() for d in data_list])
y_min = min([d.min() for d in data_list])

# Position annotations WITHIN bounds
y_pos = y_max * 0.92  # 92%, not 105% (which goes outside)

# Set explicit limits with headroom
ax.set_ylim(y_min * 0.95 if y_min > 0 else y_min - 5,
            y_max * 1.05)

Checklist for Publication Figures

Use this checklist before finalizing figures:

• Plot type appropriate for data distribution (no violin on log scale)
• All text readable at publication size (8-10 pt minimum)
• Statistical annotations visible and within plot bounds
• Legend clear and doesn't obscure data
• Axis labels descriptive with units
• Color scheme colorblind-friendly
• Line weights balanced (not too thick or thin)
• Point sizes optimized (visible but not overlapping)
• DPI adequate for publication (300 minimum)
• Layout tested (try both horizontal and vertical if applicable)
• File format publication-ready (PNG, PDF, or SVG)

Common Refinement Patterns

Pattern 1: Decluttering Dense Plots

Problem: Too many visual elements competing for attention

Solution sequence:

1. Reduce point size (60 -> 25)
2. Thin line widths (2.5 -> 1.5)
3. Increase transparency (alpha=0.8 -> 0.5)
4. Reduce font sizes (10 -> 8)
5. Remove grid or make it lighter (alpha=0.3)

Before/After test: Generate both versions, compare

Pattern 2: Fixing Overflow Issues

Problem: Annotations, legends, or labels cut off

Solutions:

# 1. Adjust annotation positions
y_pos = y_max * 0.92  # Within bounds

# 2. Use bbox_inches='tight' when saving
plt.savefig('figure.png', dpi=300, bbox_inches='tight')

# 3. Explicitly set limits
ax.set_ylim(min_val * 0.95, max_val * 1.05)

# 4. Move legend outside plot area
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

# 5. Reduce text size
ax.text(..., fontsize=8)  # Down from 10

Pattern 3: Multi-Panel Layout Optimization

Try both orientations:

# Version 1: Horizontal (side-by-side)
fig, axes = plt.subplots(1, 2, figsize=(16, 7))
plt.savefig('fig_horizontal.png', dpi=300, bbox_inches='tight')

# Version 2: Vertical (stacked)
fig, axes = plt.subplots(2, 1, figsize=(10, 14))
plt.savefig('fig_vertical.png', dpi=300, bbox_inches='tight')

# Present both to user, ask which is clearer

Decision criteria:

• Horizontal: Better for direct comparison between panels
• Vertical: Better when each panel needs more space
• User context: Journal column width, presentation slides, etc.

Pattern 4: Iterative Statistical Annotation

Common issue: P-values positioned outside plot or overlapping with data

Solution:

# Calculate data range first
all_data = [data_dual, data_prialt]  # All datasets in plot
y_max = max([d.max() for d in all_data if len(d) > 0])

# Position relative to actual data, not theoretical maximum
for i, (x_pos, comparison) in enumerate(comparisons):
    stat, pval = stats.mannwhitneyu(...)

    # Safe positioning
    y_annotation = y_max * 0.92  # Below the top

    # Format text
    if pval < 0.001:
        text = 'p < 0.001***'
    elif pval < 0.01:
        text = 'p < 0.01**'
    elif pval < 0.05:
        text = 'p < 0.05*'
    else:
        text = f'p = {pval:.3f} ns'

    ax.text(x_pos, y_annotation, text, ha='center', fontsize=9)

# Set explicit limits to ensure annotations fit
ax.set_ylim(0, y_max * 1.05)

Refinement Workflow Example

Real case: VGP Figure 5 improvement sequence

1. Initial version: 4 categories, violin plots on log scale

   • Issue: Violin distortion, too complex

2. V1 refinement: Remove violin plots, keep boxplots

   • Better, but still issues

3. V2 refinement: Simplify to 3 categories

   • Clearer interpretation

4. V3 refinement: Reduce point sizes (60->25), thin lines (2.5->1.5)

   • Less clutter

5. V4 refinement: Test vertical vs horizontal layout

   • Horizontal clearer for this case

6. V5 refinement: Fix p-value positioning (105%->92% of y_max)

   • All elements now visible

7. Final: Smaller text in statistics box (10->8)

   • Publication ready

Total iterations: 7 versions over refinement process Result: Clear, accurate, publication-quality figure

Best Practices

1. Version Your Refinements

Keep working versions during major changes:

scripts/
  plot_figure.py          # Original
  plot_figure_v2.py       # After major change (layout)
  plot_figure_final.py    # Publication version

2. Generate Alternatives in Parallel

When testing layout options:

# Save both versions
layouts = [
    ((1, 2), (16, 7), 'horizontal'),
    ((2, 1), (10, 14), 'vertical')
]

for (nrows, ncols), figsize, name in layouts:
    fig, axes = plt.subplots(nrows, ncols, figsize=figsize)
    # ... plot data ...
    plt.savefig(f'figure_{name}.png', dpi=300, bbox_inches='tight')

3. Document Each Refinement

"""
Figure 5 - Terminal Telomere Presence

Version history:
- v1: Initial 4-category version with violin plots
- v2: Removed violin plots (distortion on log scale)
- v3: Simplified to 3 categories (terminal only)
- v4: Reduced point/line sizes for clarity
- v5: Fixed p-value positioning
- final: Publication ready

Changes from v4 -> v5:
- P-value y-position: 1.05 * y_max -> 0.92 * y_max
- Added explicit y-axis limits: (y_min*0.95, y_max*1.05)
- Ensures all annotations visible within plot bounds
"""

4. Get Feedback at Key Milestones

Don't over-iterate without input:

• After fixing major issues (wrong plot type): Show user
• After layout changes (horizontal vs vertical): Show user
• After final polish: Show user

5. Maintain Consistency Across Figure Set

If refining one figure, check if same improvements apply to others:

# Applied violin->boxplot fix to Figures 2, 7, 10, 11
# Applied size reductions consistently across all figures
# Used same color scheme throughout

Summary

Systematic refinement workflow:

1. Identify issue -> 2. Fix visualization -> 3. Improve clarity -> 4. Test layouts -> 5. Optimize positioning

Key principles:

• Iterate based on user feedback
• Test alternatives (show options)
• Document changes
• Apply lessons across figure set
• Meet publication standards

Common adjustments:

• Point sizes: 60 -> 25
• Line widths: 2.5 -> 1.5
• Font sizes: 10 -> 8
• Annotation positions: 105% -> 92% of max
• Always set explicit axis limits

For additional guidance, see the supporting files:

• Publication standards (DPI, formats, sizes): publication-standards.md
• Multi-study result writing: multi-study-results.md
• Methodological transparency: methodological-transparency.md
• Overleaf package creation: overleaf-packages.md