🤩 SeuratExtend | 基于Seurat的强大拓展包！~（一）（基础可视化）

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发布于 2025-11-13 18:32:09

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文章被收录于专栏：R语言及实用科研软件R语言及实用科研软件

写在前面

单细胞分析的小伙伴们一定对 Seurat 不陌生，它几乎是所有scRNA-seq入门必备的“瑞士军刀🔪”。

但你是否也遇到过这样的问题：

“想画个更漂亮的气泡图，却要手写半页 ggplot 代码……”

“整合批次数据后，想快速提取 marker、可视化 module score，却找不到方便的函数……”

别急！今天要介绍的是一款让你在 Seurat 世界里“如虎添翼🐯”的神器——

🧬 SeuratExtend：基于Seurat的强大拓展包！

它不仅兼容原生Seurat工作流，还提供了大量一行命令即可实现的高质量可视化、数据统计与特征分析功能📊。

用到的包

rm(list = ls())
library(Seurat)
library(SeuratExtend)

示例数据

pbmc

降维图

基本用法

DimPlot2(pbmc)

可视化不同变量

DimPlot2(pbmc, features = c("cluster", "orig.ident", "CD14", "CD3D"), theme = NoAxes())

DimPlot2(pbmc, features = c("CD14", "CD3D"), cols = "A")

按指定变量分面

DimPlot2(pbmc, features = c("cluster", "CD14"), split.by = "orig.ident", ncol = 1)

高亮显示指定细胞

b_cells <- colnames(pbmc)[pbmc$cluster == "B cell"]
DimPlot2(pbmc, cells.highlight = b_cells)

高级可视化

DimPlot2(
  pbmc,
  features = c("cluster", "orig.ident", "CD14", "CD3D"),
  cols = list(
    "cluster" = "default", # dark theme
    "CD14" = "D",
    "CD3D" = "OrRd"
  ),
  theme = NoAxes())

添加标签和框

DimPlot2(pbmc, label = TRUE, box = TRUE, label.color = "black", repel = TRUE, theme = NoLegend())

使用索引简化标签

DimPlot2(pbmc, index.title = "C", box = TRUE, label.color = "black")

添加UMAP箭头

# Add simplified axis indicators
DimPlot2(
  pbmc, 
  features = c("cluster", "orig.ident", "CD14", "CD3D"),
  theme = NoAxes()
) + theme_umap_arrows()

多特征可视化

# Add arrows to each subplot
DimPlot2(
  pbmc, 
  features = c("cluster", "orig.ident", "CD14", "CD3D"),
  theme = theme_umap_arrows()
)

在降维图上同时显示三个特征

FeaturePlot3(pbmc, color = "ryb", feature.1 = "CD3D", feature.2 = "CD14", feature.3 = "CD79A", pt.size = 0.5)

使用 FeaturePlot3.grid 进行批量可视化

FeaturePlot3.grid(pbmc, features = c("CD3D", "CD14", "CD79A", "FCGR3A", NA, "LYZ"), pt.size = 0.5)

热图

基本用法

# Calculate z-scores for variable features
genes <- VariableFeatures(pbmc)
toplot <- CalcStats(pbmc, features = genes, method = "zscore", order = "p", n = 5)

# Create a basic heatmap
Heatmap(toplot, lab_fill = "zscore")

调整配色方案

# Viridis-A theme
Heatmap(toplot, lab_fill = "zscore", color_scheme = "A")

修改轴和标签

Heatmap(toplot, lab_fill = "zscore", plot.margin = margin(l = 50))

对于具有许多特征的密集矩阵，可以挑选显示基因。🥸

toplot2 <- CalcStats(pbmc, features = genes[1:500], method = "zscore", order = "p")
Heatmap(toplot2, lab_fill = "zscore", feature_text_subset = genes[1:20], expand_limits_x = c(-0.5, 11))

热图分面

gene_groups <- sample(c("group1", "group2", "group3"), nrow(toplot2), replace = TRUE)
Heatmap(toplot2, lab_fill = "zscore", facet_row = gene_groups) +
  theme(axis.text.y = element_blank())

点阵图

基本用法

# Select some variable features
genes <- VariableFeatures(pbmc)[1:10]
DotPlot2(pbmc, features = genes)

分面

# Create grouped features
grouped_features <- list(
  "B_cell_markers" = c("MS4A1", "CD79A"),
  "T_cell_markers" = c("CD3D", "CD8A", "IL7R"),
  "Myeloid_markers" = c("CD14", "FCGR3A", "S100A8")
)

DotPlot2(pbmc, features = grouped_features)

拆开可视化

# Basic split visualization
DotPlot2(pbmc, features = genes, group.by = "cluster", split.by = "orig.ident", show_grid = FALSE)

# Using colors instead of borders for split groups
DotPlot2(pbmc, 
         features = genes, 
         group.by = "cluster", 
         split.by = "orig.ident", 
         split.by.method = "color", 
         show_grid = FALSE)

修改细节！~

DotPlot2(pbmc, 
         features = grouped_features, 
         color_scheme = "BuRd", 
         border = FALSE,        # Remove dot borders
         show_grid = FALSE,     # Remove grid lines
         flip = TRUE)          # Flip coordinates