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:

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"

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')

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)

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

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:

Reduce point size (60 -> 25)
Thin line widths (2.5 -> 1.5)
Increase transparency (alpha=0.8 -> 0.5)
Reduce font sizes (10 -> 8)
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 &#x3C; 0.001:
    text = 'p &#x3C; 0.001***'
elif pval &#x3C; 0.01:
    text = 'p &#x3C; 0.01**'
elif pval &#x3C; 0.05:
    text = 'p &#x3C; 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

Initial version: 4 categories, violin plots on log scale

Issue: Violin distortion, too complex

V1 refinement: Remove violin plots, keep boxplots

Better, but still issues

V2 refinement: Simplify to 3 categories

Clearer interpretation

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

Less clutter

V4 refinement: Test vertical vs horizontal layout

Horizontal clearer for this case

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

All elements now visible

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

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

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')

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_min0.95, y_max1.05)
Ensures all annotations visible within plot bounds """

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

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:

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