v3.0中的数据可视化新方法

编译：2020-10-02

---

我们将使用2700 PBMC教程中先前计算的Seurat对象，在Seurat中演示可视化技术。您可以从SeuratData下载此数据集

SeuratData::InstallData("pbmc3k")

library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
data("pbmc3k.final")
pbmc3k.final$groups <- sample(c("group1", "group2"), size = ncol(pbmc3k.final), replace = TRUE)
features <- c("LYZ", "CCL5", "IL32", "PTPRCAP", "FCGR3A", "PF4")
pbmc3k.final

## An object of class Seurat 
## 13714 features across 2638 samples within 1 assay 
## Active assay: RNA (13714 features, 2000 variable features)
##  2 dimensional reductions calculated: pca, umap

• 标记特征表达的五种可视化
• 的新增加 FeaturePlot
• 更新和扩展了可视化功能
• 将主题应用于情节
• 交互式绘图功能
• 绘图配件

# Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
RidgePlot(pbmc3k.final, features = features, ncol = 2)

image.png

# Violin plot - Visualize single cell expression distributions in each cluster
VlnPlot(pbmc3k.final, features = features)

image.png

# Feature plot - visualize feature expression in low-dimensional space
FeaturePlot(pbmc3k.final, features = features)

image.png

# Dot plots - the size of the dot corresponds to the percentage of cells expressing the feature
# in each cluster. The color represents the average expression level
DotPlot(pbmc3k.final, features = features) + RotatedAxis()

image.png

# Single cell heatmap of feature expression
DoHeatmap(subset(pbmc3k.final, downsample = 100), features = features, size = 3)

image.png