BioSkills bio-reporting-figure-export

Exports publication-ready figures in various formats with proper resolution, sizing, and typography. Use when preparing figures for journal submission, creating vector graphics for presentations, or ensuring consistent figure styling across analyses.

install

source · Clone the upstream repo

git clone https://github.com/GPTomics/bioSkills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/GPTomics/bioSkills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/reporting/figure-export" ~/.claude/skills/gptomics-bioskills-bio-reporting-figure-export && rm -rf "$T"

manifest: reporting/figure-export/SKILL.md

source content

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+

Before using code patterns, verify installed versions match. If versions differ:

Python:
```
pip show <package>
```
then
```
help(module.function)
```
to check signatures
R:
```
packageVersion('<pkg>')
```
then
```
?function_name
```
to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Publication-Ready Figure Export

"Export figures for publication" → Save plots as high-resolution PDF/SVG/TIFF with journal-required DPI and dimensions.

Python:

fig.savefig('fig.pdf', dpi=300, bbox_inches='tight')

ggsave('fig.pdf', width=7, height=5, units='in')

Python (matplotlib)

import matplotlib.pyplot as plt

# Set publication defaults
plt.rcParams.update({
    'font.size': 8,
    'font.family': 'Arial',
    'axes.linewidth': 0.5,
    'lines.linewidth': 1,
    'figure.dpi': 300
})

fig, ax = plt.subplots(figsize=(3.5, 3))  # Single column width
# ... create plot ...

# Save in multiple formats
fig.savefig('figure1.pdf', bbox_inches='tight', dpi=300)
fig.savefig('figure1.png', bbox_inches='tight', dpi=300)
fig.savefig('figure1.svg', bbox_inches='tight')

R (ggplot2)

library(ggplot2)

p <- ggplot(data, aes(x, y)) + geom_point() +
  theme_classic(base_size = 8) +
  theme(text = element_text(family = 'Arial'))

# PDF for vector graphics
ggsave('figure1.pdf', p, width = 3.5, height = 3, units = 'in')

# High-res PNG
ggsave('figure1.png', p, width = 3.5, height = 3, units = 'in', dpi = 300)

# TIFF (some journals require)
ggsave('figure1.tiff', p, width = 3.5, height = 3, units = 'in',
       dpi = 300, compression = 'lzw')

Journal Requirements

Journal Type	Format	Resolution	Width
Most journals	PDF/EPS	Vector	3.5" (1-col), 7" (2-col)
Online-only	PNG	300 DPI	Variable
Print	TIFF	300-600 DPI	Column width

Multi-panel Figures

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(7, 5))  # Two-column width
gs = GridSpec(2, 3, figure=fig)

ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, :])

# Add panel labels
for ax, label in zip([ax1, ax2, ax3], ['A', 'B', 'C']):
    ax.text(-0.1, 1.1, label, transform=ax.transAxes,
            fontsize=10, fontweight='bold')

fig.savefig('figure_multipanel.pdf', bbox_inches='tight')

Color Considerations

Use colorblind-friendly palettes (viridis, cividis)
Ensure sufficient contrast for grayscale printing
Maintain consistency across all figures

Related Skills

data-visualization/ggplot2-fundamentals - Creating plots in R
data-visualization/heatmaps-clustering - Complex visualizations
data-visualization/multipanel-figures - Figure composition