在高分文章中，单细胞转录组的细胞比例图怎么画最好看？

百沐生物

322人浏览 · 2026-04-20 14:27:36

百沐生物 · 2026-04-20 14:27:36 发布

Nature的经典配色是什么？代码如何运用在科研绘图当中呢？

今天小编就来带你开箱：单细胞绘图的方法。快来mark这篇干货推文吧！

一、为什么但细胞比例图这么重要呢？

在单细胞转录组（scRNA-seq）的分析里面，细胞比例图（Cell Propotion Plot）是每一篇高分文章的加分项。

细胞比例图主要回答了：在不同的样本/条件/时间点之间，各细胞亚群的组成比例有没有变化？

这张图往往出现在：疾病 vs 正常对照的细胞组成差异、治疗前后细胞亚群的重塑等变化。

图画得好不好，直接影响了审稿人对数据和文章的第一印象。

二、搞清楚有哪些画法

1. 堆叠柱状图（Stacked Bar Plot）：最经典，最常见，信息密度高。适合展示多样本整体组成对比。

2. 分组柱状图（Stacked Bar Plot）：适合比较少量细胞类型的精确数值，便于统计检验。

3. 桑基图 / 冲积图（Alluvial / Sankey Plot）：适合展示细胞状态转变，视觉冲击力强，Nature 文章最爱。

4. 气泡比例图（Bubble Proportion Plot）：适合多维度展示，兼顾比例和统计显著性。

5. 小提琴/箱线图叠加比例图：适合多样本统计检验场景，可以直接展示p值。

三、Nature的配色从哪里来的？

高分文章的配色有三个共同特点：饱和度适中（不刺眼，印刷友好）、色相区分度高（色盲友好）、有层次感（主色+辅色搭配）。

NPG 配色（Nature Publishing Group）

#E64B35 #4DBBD5 #00A087 #3C5488 #F39B7F #8491B4 #91D1C2 #DC0000

D3 Category10（单细胞最常用）

#1F77B4 #FF7F0E #2CA02C #D62728 #9467BD #8C564B #E377C2 #7F7F7F

推荐使用 ggsci 包，一行代码 scale_fill_npg() 即可调用 NPG 配色，无需手动输入色值。

四、代码实践

首先，要准备好代码环境：

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div.WordSection1
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install.packages(c("ggplot2",
"dplyr", "tidyr", "ggsci",
"ggalluvial", "RColorBrewer"))
BiocManager::install("scater")

图一：经典堆叠柱状图

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library(ggplot2)
library(dplyr)
library(ggsci)
set.seed(42)
cell_types <- c("T cell","B cell","NK
cell","Monocyte","DC","Macrophage","Neutrophil","Plasma
cell")
samples <- paste0("Sample_", 1:8)
df <- expand.grid(CellType = cell_types, Sample = samples) %>%
  mutate(Proportion = runif(n(), 0.02,
0.35),
         Group =
ifelse(grepl("1|2|3|4", Sample), "Control",
"Disease")) %>%
  group_by(Sample) %>%
  mutate(Proportion = Proportion /
sum(Proportion)) %>%
  ungroup()
p1 <- ggplot(df, aes(x = Sample, y = Proportion, fill = CellType)) +
  geom_bar(stat = "identity",
width = 0.75, color = "white", linewidth = 0.3) +
  scale_fill_npg() +
  scale_y_continuous(labels =
scales::percent_format()) +
  facet_grid(~ Group, scales =
"free_x", space = "free") +
  labs(title = "Cell Type
Composition Across Samples",
       x = NULL, y = "Cell
Proportion", fill = "Cell Type") +
  theme_classic(base_size = 12) +
  theme(axis.text.x = element_text(angle
= 45, hjust = 1, size = 9),
        strip.background =
element_rect(fill = "#F0F0F0", color = NA),
        strip.text = element_text(face =
"bold", size = 11),
        legend.position =
"right",
        legend.key.size = unit(0.4,
"cm"),
        plot.title = element_text(face =
"bold", hjust = 0.5))
ggsave("stacked_bar_NPG.pdf", p1, width = 9, height = 5, useDingbats
= FALSE)

color = "white" 加白色描边让各色块边界清晰；useDingbats = FALSE 保证 PDF 字体在 Illustrator 中可编辑。

图2：冲积图（Alluvial Plot）——这图好啊，这图可太吸睛了（下面代码建议收藏）

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div.WordSection1
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library(ggalluvial)
df_alluvial <- data.frame(
  Cluster   = rep(paste0("C", 1:8), each = 3),
  CellType  = rep(cell_types[1:3], 8),
  Condition = rep(c("Normal",
"Early", "Late"), 8),
  Freq      = sample(50:500, 24, replace = TRUE)
)
p3 <- ggplot(df_alluvial,
       aes(axis1 = Cluster, axis2 =
CellType, axis3 = Condition, y = Freq)) +
  geom_alluvium(aes(fill = CellType),
width = 1/12, alpha = 0.75, knot.pos = 0.4) +
  geom_stratum(width = 1/6, fill =
"grey90", color = "grey50", linewidth = 0.3) +
  geom_text(stat = "stratum",
aes(label = after_stat(stratum)), size = 3, fontface = "bold") +
  scale_fill_d3("category10") +
  scale_x_discrete(limits =
c("Cluster", "Cell Type", "Condition"), expand =
c(0.1, 0.1)) +
  labs(title = "Cell State
Transition Across Conditions",
       y = "Cell Count", fill =
"Cell Type") +
  theme_minimal(base_size = 12) +
  theme(panel.grid = element_blank(),
        axis.text.y = element_blank(),
        plot.title = element_text(face =
"bold", hjust = 0.5, size = 14))
ggsave("alluvial_D3.pdf", p3, width = 10, height = 7, useDingbats =
FALSE)

封装函数：一键出图

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plot_cell_proportion <- function(seurat_obj,
                                 
group_by = "orig.ident",
                                 
cell_type_col = "celltype",
                                  palette
= "npg") {
  df <- seurat_obj@meta.data %>%
    count(.data[[group_by]],
.data[[cell_type_col]]) %>%
    group_by(.data[[group_by]]) %>%
    mutate(prop = n / sum(n)) %>%
    ungroup()
  color_fn <- switch(palette,
    "npg"    = scale_fill_npg(),
    "nejm"   = scale_fill_nejm(),
    "d3"     = scale_fill_d3(),
    "lancet" =
scale_fill_lancet()
  )
  ggplot(df, aes(x = .data[[group_by]], y
= prop, fill = .data[[cell_type_col]])) +
    geom_bar(stat = "identity",
width = 0.75, color = "white", linewidth = 0.3) +
    color_fn +
    scale_y_continuous(labels =
scales::percent_format()) +
    labs(x = NULL, y = "Cell
Proportion", fill = "Cell Type") +
    theme_classic(base_size = 12) +
    theme(axis.text.x =
element_text(angle = 45, hjust = 1))
}
# 使用示例
# plot_cell_proportion(pbmc, group_by = "sample", cell_type_col =
"celltype", palette = "npg")

五、让图拥有“高级感”的5个细节

【字体】 普通：默认 Arial → 高分：Helvetica / Myriad Pro

【描边】 普通：无描边 → 高分：白色细描边 0.3pt

【图例】 普通：右侧大图例 → 高分：精简图例，key.size 缩小

【导出】 普通：PNG 300dpi → 高分：PDF 矢量图

【配色】 普通：默认 ggplot2 彩虹色 → 高分：ggsci NPG / NEJM / D3

六、常见的踩坑！

Q：颜色太多，超过 10 种细胞类型怎么办？
A：用 colorRampPalette 在 NPG 基础上插值扩展，或使用 Polychrome 包的 palette36，专为多类别设计。

Q：PDF 导出后字体变成方块？
A：加 useDingbats = FALSE，或者用 cairo_pdf() 替代 pdf()。

Q：Seurat 对象如何直接提取比例数据？
A：用table(seurat@meta.data$sample, seurat@meta.data$celltype) 提取，再用 prop.table() 转换为比例。

有关图像编辑和图像修改更多的建议，可以也直接在百沐一下中进行提问！

一张好的细胞比例图 = 合适的图形类型 + Nature 级配色 + 精细主题调整 + 矢量格式导出