菜单
Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- --skill bio-data-visualization-multipanel-figures命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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基于 SOC 职业分类
正在显示 SKILL.md
| name | bio-data-visualization-multipanel-figures |
| description | Combine multiple plots into publication-ready multi-panel figures using patchwork, cowplot, or matplotlib GridSpec with shared legends and panel labels. Use when combining multiple plots into publication figures. |
| tool_type | mixed |
| primary_tool | patchwork |
library(patchwork)
p1 <- ggplot(df, aes(x, y)) + geom_point()
p2 <- ggplot(df, aes(group, value)) + geom_boxplot()
p3 <- ggplot(df, aes(x)) + geom_histogram()
# Horizontal
p1 + p2 + p3
# Vertical
p1 / p2 / p3
# Mixed layouts
(p1 | p2) / p3
(p1 + p2) / (p3 + p4)
# Automatic labels
(p1 + p2 + p3) + plot_annotation(tag_levels = 'A')
# Custom labels
(p1 + p2 + p3) + plot_annotation(tag_levels = list(c('A', 'B', 'C')))
# Label styling
(p1 + p2) + plot_annotation(
tag_levels = 'A',
tag_prefix = '(',
tag_suffix = ')',
theme = theme(plot.tag = element_text(face = 'bold', size = 14))
)
# Width ratios
p1 + p2 + plot_layout(widths = c(2, 1))
# Height ratios
p1 / p2 + plot_layout(heights = c(1, 2))
# Complex grid
layout <- "
AAB
AAB
CCC
"
p1 + p2 + p3 + plot_layout(design = layout)
# Fixed dimensions
p1 + p2 + plot_layout(widths = unit(c(5, 3), 'cm'))
# Collect legends
(p1 + p2 + p3) + plot_layout(guides = 'collect')
# Position at bottom
(p1 + p2) + plot_layout(guides = 'collect') &
theme(legend.position = 'bottom')
# Keep individual legends
(p1 + p2) + plot_layout(guides = 'keep')
# Add inset
p1 + inset_element(p2, left = 0.6, bottom = 0.6, right = 1, top = 1)
# Multiple insets
p1 +
inset_element(p2, 0.6, 0.6, 1, 1) +
inset_element(p3, 0.02, 0.6, 0.4, 1)
library(cowplot)
# Simple grid
plot_grid(p1, p2, p3, ncol = 3, labels = 'AUTO')
# With labels
plot_grid(p1, p2, labels = c('A', 'B'), label_size = 14)
# Relative widths
plot_grid(p1, p2, rel_widths = c(2, 1))
# Nested grids
top_row <- plot_grid(p1, p2, ncol = 2)
bottom_row <- p3
plot_grid(top_row, bottom_row, nrow = 2, labels = c
library(patchwork)
# Same axis limits
(p1 + p2) & xlim(0, 10) & ylim(0, 100)
# Same theme
(p1 + p2 + p3) & theme_minimal()
# Same color scale
(p1 + p2) & scale_color_viridis_d()
# Add blank panel
p1 + plot_spacer() + p2
# With layout
layout <- "
AB#
CCC
"
p1 + p2 + p3 + plot_layout(design = layout)
(p1 + p2 + p3) +
plot_annotation(
title = 'Main Title',
subtitle = 'Subtitle text',
caption = 'Data source: ...',
theme = theme(
plot.title = element_text(face = 'bold', size = 16),
plot.subtitle = element_text(size = 12, color = 'grey40')
)
)
# Combine and save
combined <- (p1 | p2) / (p3 | p4) +
plot_annotation(tag_levels = 'A') &
theme(plot.tag = element_text(face = 'bold'))
ggsave('figure.pdf', combined, width = 10, height = 8)
ggsave('figure.png', combined, width = 10, height = 8, dpi = 300)
# For specific journal dimensions
ggsave('figure.pdf', combined, width = 180, height = 150, units =
# Create themed plots
theme_pub <- theme_bw(base_size = 10) +
theme(
panel.grid = element_blank(),
legend.position = 'none'
)
p_volcano <- create_volcano(res) + theme_pub + ggtitle('Volcano Plot')
p_pca <- create_pca(vsd) + theme_pub + ggtitle('PCA')
p_heatmap <- wrap_elements(pheatmap_grob)
p_boxplot <- create_boxplot(expr_df) + theme_pub + ggtitle('Expression')
# Assemble
figure <- (p_volcano | p_pca) / (p_heatmap | p_boxplot) +
plot_annotation(tag_levels =
plot_layoutguides
theme
plot.tag element_textface size
legend.position
ggsave figure width height units
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0]) # Top left
ax2 = fig.add_subplot(gs[0, 1:]) # Top right, spans 2 columns
ax3 = fig.add_subplot(gs[1, :]) # Bottom, spans all columns
# Add plots to each axis
ax1.plot(x, y)
ax2.scatter(x, y)
ax3.bar(x, y)
plt.tight_layout()
# 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=14, fontweight='bold', va='top')
# matplotlib 3.4+ subfigures for complex layouts
fig = plt.figure(figsize=(12, 8))
subfigs = fig.subfigures(1, 2, width_ratios=[2, 1])
axs_left = subfigs[0].subplots(2, 1)
ax_right = subfigs[1].subplots(1, 1)
# Python
fig.savefig('figure1.pdf', bbox_inches='tight')
fig.savefig('figure1.png', dpi=300, bbox_inches='tight')