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单细胞通路评分（GSVA）

作者

[编辑] ZCode (GLM) — AI 生成，独立 AI 复核

修改于

2026-11-05

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单细胞亚群注释完成后，反复出现的问题是：某条通路在每个细胞或每个群里活性如何、两个群之间是否有差异 [1]。GSVA 为每个细胞对基因集打分；随后用热图和箱线图在群间比较。本教程在模拟的”细胞 × 基因”矩阵上跑该工作流——参考流程（matrix → 分组 → GSVA → 出图）的绘图半程。

Example

ScGsvaPlot DEMO

Setup

  • 系统要求：跨平台（Linux/MacOS/Windows）

  • 编程语言：R

  • 依赖包： ComplexHeatmap; ggplot2; dplyr; tidyr; circlize; ggpubr

# 安装必要的包
if (!requireNamespace("ComplexHeatmap", quietly = TRUE)) {
  BiocManager::install("ComplexHeatmap")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("dplyr", quietly = TRUE)) {
  install.packages("dplyr")
}
if (!requireNamespace("tidyr", quietly = TRUE)) {
  install.packages("tidyr")
}
if (!requireNamespace("circlize", quietly = TRUE)) {
  install.packages("circlize")
}
if (!requireNamespace("ggpubr", quietly = TRUE)) {
  install.packages("ggpubr")
}

# 加载包
library(ComplexHeatmap)
library(ggplot2)
library(dplyr)
library(tidyr)
library(circlize)
library(ggpubr)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.6.2 (2026-10-29)
 os       Ubuntu 26.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-11-05
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.11.9 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 ! package        * version    date (UTC) lib source
 P circlize       * 0.4.18     2026-04-04 [?] RSPM
 P ComplexHeatmap * 2.28.0     2026-04-28 [?] Bioconductor 3.23 (R 4.6.2)
 P dplyr          * 1.2.1      2026-04-03 [?] RSPM
 P ggplot2        * 4.0.3.9000 2026-11-03 [?] Github (tidyverse/ggplot2@b638d65)
 P ggpubr         * 1.0.0      2026-07-06 [?] RSPM
 P tidyr          * 1.3.2      2025-12-19 [?] RSPM

 [1] /home/runner/.cache/R/renv/library/Bizard-5140bdc6/linux-ubuntu-resolute/R-4.6/x86_64-pc-linux-gnu
 [2] /home/runner/work/_temp/Library
 [3] /opt/R/4.6.2/lib/R/library

 * ── Packages attached to the search path.
 P ── Loaded and on-disk path mismatch.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

模拟两个内皮样亚群（EC1、EC2）共 400 个细胞的 log 归一化表达，以及六条植入群特异活性的 hallmark 式通路——即参考工作流中 GSVA 之前的状态。

set.seed(1001)
n_cell <- 200                                     # per cluster
genes <- paste0("Gene", sprintf("%04d", 1:300))
cells <- c(paste0("EC1_", sprintf("%03d", 1:n_cell)),
           paste0("EC2_", sprintf("%03d", 1:n_cell)))
cluster <- factor(rep(c("EC1", "EC2"), each = n_cell), levels = c("EC1", "EC2"))

expr <- matrix(rnorm(300 * 2 * n_cell, sd = 0.5), nrow = 300,
               dimnames = list(genes, cells))
pathways <- list(
  "Angiogenesis"       = 1:40,    "EC activation"     = 41:80,
  "Hypoxia"            = 81:110,  "Leukocyte migration" = 111:150,
  "Coagulation"        = 151:180, "Antigen processing"  = 181:210
)
signal <- c(1.4, 1.0, 0.6, -0.3, 0, 0)             # EC1 > EC2 for the first three
for (k in seq_along(pathways)) {
  pg <- names(pathways)[k]
  expr[pathways[[pg]], cluster == "EC1"] <-
    expr[pathways[[pg]], cluster == "EC1"] + signal[k]
}

Visualization

1. 逐细胞通路评分（GSVA 热图）

## 免依赖的逐细胞集合评分（集合基因均值）；参考流程在归一化矩阵上
## 用 GSVA::gsva()——输入与版式一致
score_mat <- t(sapply(pathways, function(idx) colMeans(expr[idx, , drop = FALSE])))
score_z <- t(scale(t(score_mat)))

ha <- HeatmapAnnotation(Cluster = cluster,
                        col = list(Cluster = c(EC1 = "#B31E22", EC2 = "#529B40")),
                        show_legend = FALSE)
col_fun <- circlize::colorRamp2(c(-2, 0, 2), c("#2166AC", "white", "#B2182B"))
Heatmap(score_z, name = "Score (z)",
        top_annotation = ha, col = col_fun,
        cluster_columns = FALSE,
        column_split = cluster, column_gap = unit(1.5, "mm"),
        row_names_gp = grid::gpar(fontsize = 9),
        show_column_names = FALSE, border = TRUE)
图 1: 每个细胞（列）的通路评分，按亚群分块；EC1 携带血管生成/激活/缺氧程序。

2. 群间比较某条通路

box_df <- data.frame(cluster = cluster,
                     score = score_mat["Angiogenesis", ])
p_kw <- wilcox.test(score ~ cluster, data = box_df)$p.value

ggplot(box_df, aes(cluster, score, fill = cluster)) +
  geom_boxplot(outlier.shape = NA, width = 0.55) +
  geom_jitter(width = 0.15, size = 0.5, alpha = 0.4, show.legend = FALSE) +
  scale_fill_manual(values = c(EC1 = "#B31E22", EC2 = "#529B40")) +
  labs(title = sprintf("Angiogenesis (Wilcoxon %s)",
       ifelse(p_kw < 0.001, "p < 0.001", sprintf("p = %.3f", p_kw))),
       x = NULL, y = "Pathway score", fill = NULL) +
  theme_classic(base_size = 11) +
  theme(legend.position = "none")
图 2: EC1 与 EC2 中每个细胞的血管生成评分；标注 Wilcoxon p 值。
提示

Practice tips

  • 在归一化数据上为细胞打分（Seurat 中为 GetAssayData(slot = "data")），切勿用 raw counts。
  • 真实数据在”细胞 × 基因”矩阵上运行 GSVA::gsva(expr, pathways)——热图与箱线两半与此完全一致。
  • 两个群对应一个 Wilcoxon 检验；更多群用逐通路 Kruskal-Wallis（ggpubr::stat_compare_means(method = "kruskal.test")）。

Application

逐细胞通路评分是亚群注释的标准读出（内皮尖端 vs 主干细胞、肿瘤 vs 基质成纤维细胞）。热图揭示哪些程序定义哪个群；箱线图量化感兴趣的某条程序。

Reference

[1] Xiaofan Lu, et al. FigureYa: A Standardized Visualization Framework for Enhancing Biomedical Data Interpretation and Research Efficiency. iMetaMed. 2025. doi:10.1002/imm3.70005