BioF3 FigCode · SCI 绘图代码集

多组学整合热图（Multi-omics Heatmap）

导出日期：2026年6月27日

分类：聚类可视化 ｜ 依赖包：ComplexHeatmap、circlize

解决的生物学问题

不同组学层在样本水平上的变化是否一致？哪些样本在多个层面同时异常？

应用场景

输入数据格式

三个矩阵：rna_mat、prot_mat、metab_mat（行 = feature，列 = 样本）+ 样本注释

关键参数

快速开始

1. 直接在线运行

打开 FigCode 在线绘图，点击本工具卡片的"在线绘图"按钮即可用内置示例数据出图，零环境配置。

2. 本地复现

下载脚本和示例数据，本地 RStudio 运行：

# 下载
curl -O https://<your-site>/figcode/scripts/multi-omics-heatmap.R
curl -O https://<your-site>/figcode/data/multi-omics-heatmap.csv

3. 安装依赖

# CRAN 包
install.packages(c("ComplexHeatmap", "circlize"))

# Bioconductor 包（如需）
# BiocManager::install(c())

完整代码

library(ComplexHeatmap)
library(circlize)

# --- Demo data: 3 omics layers, same 12 samples ---
set.seed(42)
n_samp <- 12
sample_names <- paste0("S", 1:n_samp)

# Group: 6 tumor + 6 normal
group <- factor(rep(c("Tumor","Normal"), each = 6))

mk_layer <- function(n_feat, label) {
  m <- matrix(rnorm(n_feat * n_samp), nrow = n_feat)
  # Inject group difference
  m[1:floor(n_feat / 2), 1:6] <- m[1:floor(n_feat / 2), 1:6] + 2
  rownames(m) <- paste0(label, "_", 1:n_feat)
  colnames(m) <- sample_names
  m
}
rna   <- mk_layer(20, "Gene")
prot  <- mk_layer(15, "Prot")
metab <- mk_layer(10, "Metab")

# --- Shared column annotation ---
col_anno <- HeatmapAnnotation(
  Group = group,
  col = list(Group = c(Tumor = "#dc2626", Normal = "#2563eb")),
  show_legend = TRUE
)

col_fun <- colorRamp2(c(-2, 0, 2), c("#2563eb", "white", "#dc2626"))

ht1 <- Heatmap(rna, name = "mRNA", col = col_fun,
               top_annotation = col_anno,
               row_title = "mRNA", show_column_names = FALSE,
               show_row_dend = FALSE)
ht2 <- Heatmap(prot, name = "Protein", col = col_fun,
               row_title = "Protein", show_column_names = FALSE,
               show_row_dend = FALSE, show_column_dend = FALSE)
ht3 <- Heatmap(metab, name = "Metabolite", col = col_fun,
               row_title = "Metabolite",
               show_row_dend = FALSE, show_column_dend = FALSE)

draw(ht1 %v% ht2 %v% ht3,
     column_title = "Multi-omics Integration",
     merge_legend = TRUE)

替换为自己的数据

脚本中以 # --- Demo data --- 标注的段落是示例数据生成代码。替换为自己的数据时，保持列名一致即可：

延伸阅读