展示你的特征基因：带"辣椒粉"的markers基因umap图

生信技能树

发布于 2025-03-11 21:26:26

10500

代码可运行

运行总次数：0

代码可运行

今天来看看如何展示你的特征基因，这个需求在单细胞分析中非常常见。图来自文献《The aged tumor microenvironment limits T cell control of cancer》，于2024年6月25日发表在Nat Immunol杂志上（IF27.8）。如下：

图注：

Fig. 4. Cell-extrinsic signals from the aged TME drive the TTAD subset state, which is distinct from CD8+ T cell exhaustion. j, scRNA-seq UMAP projection of 7,242 tumor-infiltrating CD8+ T cells from human pan-cancer aggregated tumor biopsies (n = 57) colored by cluster.

文章中还有很多其他的这种UMAP展示，换一个色系的：

示例数据

使用的数据还是自 GSE128531 数据注释后的seurat对象，你自己用的时候可以使用任何一个经过了注释后的seurat对象。或者在这里下载链接: https://pan.baidu.com/s/16ZzlZBp2_grEXEpX-ggPnw?pwd=2jdp 提取码: 2jdp：

###
### Create: Jianming Zeng
### Date:  2023-12-31  
### Email: jmzeng1314@163.com
### Blog: http://www.bio-info-trainee.com/
### Forum:  http://www.biotrainee.com/thread-1376-1-1.html
### CAFS/SUSTC/Eli Lilly/University of Macau
### Update Log: 2023-12-31   First version 
### Update Log: 2024-12-09   by juan zhang (492482942@qq.com)
### 

rm(list=ls())
library(COSG)
library(harmony)
library(ggsci)
library(dplyr) 
library(future)
library(Seurat)
library(clustree)
library(cowplot)
library(data.table)
library(dplyr)
library(ggplot2)
library(patchwork)
library(stringr)
library(qs)

# 导入数据
sce.all.int <- readRDS('2-harmony/sce.all_int.rds')
head(sce.all.int@meta.data)
load("phe.Rdata")
head(phe)
sce.all.int <- AddMetaData(sce.all.int, metadata = phe)

Idents(sce.all.int) <- "celltype"

确定展示的特征基因

我这里挑了12个基因，为每种细胞的已知marker基因，如下：

macrophages: AIF1
keratinocytes: KRT1
T lymphocytes: CD3D
fibroblasts: COL1A1
endothelial cells: VWF
mast cells: TPSAB1
secretory (glandular) cells: SCGB1B2P
pericytes: RGS5
melanocytes: PMEL
B cells: MS4A1
smooth muscle cells: DES
dendritic cells: CD1C

先绘制一个基因看看

使用 scCustomize 这个包：

################## umap4：scCustomize
library(viridis)
library(Seurat)
library(scCustomize)

# 绘图
p <- FeaturePlot_scCustom(seurat_object = sce.all.int, features = "CD3E", order = T,pt.size = 0.4) + 
  scale_color_gradient(low = "grey", high = "#f32a1f")  + # 更改填充颜色 
  theme_void() +  # 使用空白主题
  theme( plot.title = element_text(hjust = 0.5, size = 16, face = "italic"), # 标题居中
         legend.position = "none", # 去除图例
         panel.border = element_blank(),
         panel.grid.major = element_blank(),
         panel.grid.minor = element_blank(),
         panel.background = element_blank(),
         plot.background = element_blank())
p

结果如下：

多个基因绘制在一个面板

接下来将多个基因绘制在一个图中，这里总共有12个基因，可以选择4X3的排列方式，比较美观，拼图使用patchwork包：

# 确定特征基因
selected_genes <- list("macrophages"="AIF1",
                       "keratinocytes"="KRT1",
                       "T lymphocytes"="CD3D",
                       "fibroblasts"="COL1A1",
                       "endothelial cells"="VWF",
                       "mast cells"="TPSAB1",
                       "secretory (glandular) cells"="SCGB1B2P",
                       "pericytes"="RGS5",
                       "melanocytes"="PMEL",
                       "B cells"="MS4A1",
                       "smooth muscle cells"="DES",
                       "dendritic cells"="CD1C")
g <- unlist(selected_genes)
g

# 生成一个存储多个ggplot对象的list
p_merge <- list()

for(i in 1:length(g)) {
# 打印动态
print(g[i])
# 绘图
  p_merge[[i]] <- FeaturePlot_scCustom(seurat_object = sce.all.int, features = g[i], order = T,pt.size = 0.6) + 
    scale_color_gradient(low = "grey", high = "#f32a1f")  + # 更改填充颜色 
    theme_void() +  # 使用空白主题
    theme( plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), # 标题居中
      legend.position = "none", # 去除图例
      panel.border = element_blank(),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      panel.background = element_blank(),
      plot.background = element_blank())
}

# 拼图
library(patchwork)
# 有多少个基因，3行4咧
length(g)
# 使用 patchwork 的 wrap_plots() 函数组合图表
combined_plot <- wrap_plots(p_merge, ncol = 4)
combined_plot

# 保存
ggsave(filename = "Salt_UMAP.pdf", width = 11.5, height = 8, plot = combined_plot)
ggsave(filename = "Salt_UMAP.png", width = 11.5, height = 8, plot = combined_plot)

结果如下：

完美~

换一个色系

改一下：scale_color_gradient(low = "grey", high = "#f32a1f")

# 生成一个存储多个ggplot对象的list
p_merge <- list()

for(i in 1:length(g)) {
# 打印动态
print(g[i])
# 绘图
  p_merge[[i]] <- FeaturePlot_scCustom(seurat_object = sce.all.int, features = g[i], order = T,pt.size = 0.6) + 
    scale_color_gradient(low = "grey", high = "#44579a")  + # 更改填充颜色
    theme_void() +  # 使用空白主题
    theme( plot.title = element_text(hjust = 0.5, size = 16, face = "bold"), # 标题居中
      legend.position = "none", # 去除图例
      panel.border = element_blank(),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      panel.background = element_blank(),
      plot.background = element_blank())
}

# 拼图
library(patchwork)
# 有多少个基因，3行4咧
length(g)
# 使用 patchwork 的 wrap_plots() 函数组合图表
combined_plot <- wrap_plots(p_merge, ncol = 4)
combined_plot

# 保存
ggsave(filename = "Salt_UMAP.pdf", width = 11.5, height = 8, plot = combined_plot)
ggsave(filename = "Salt_UMAP.png", width = 11.5, height = 8, plot = combined_plot)