单细胞转录组基础分析八：可视化工具总结

单细胞天地 2022-06-07

The following article is from 生信会客厅 Author Kinesin

单细胞测序技术是近年最大的生命科学突破之一，相关文章频繁发表于各大顶级期刊，然而单细胞数据的分析依然是大家普遍面临的障碍。本专题将针对10X Genomics单细胞转录组数据演示各种主流分析，包括基于Seurat的基础分析、以及基于clusterProfiler、Monocle、SingleR等R包的延伸分析。不足之处请大家批评指正，欢迎添加Kinesin微信交流探讨！

Seurat自带一些优秀的可视化工具，之前的分析内容陆续展示过一些，本节内容将总结这些可视化函数的使用。

RidgePlot山脊图

library(Seurat)library(tidyverse)library(patchwork)rm(list=ls())dir.create("visual")scRNA <- readRDS("scRNA.rds")p1 = RidgePlot(scRNA, features = "FCN1")p2 = RidgePlot(scRNA, features = "PC_2")plotc = p1/p2 + plot_layout(guides = 'collect')ggsave('visual/ridgeplot_eg.png', plotc, width = 8,height = 8)

VInPlot小提琴图

p1 = VlnPlot(scRNA, features = "nCount_RNA", pt.size = 0)p2 = VlnPlot(scRNA, features = "CD8A", pt.size = 0)plotc = p1/p2 + plot_layout(guides = 'collect')ggsave('visual/vlnplot_eg.png', plotc, width = 8,height = 8)

FeaturePlot特征图

p1 <- FeaturePlot(scRNA,features = "CD8A", reduction = 'umap')p2 <- FeaturePlot(scRNA,features = "CD79A", reduction = 'umap')plotc = p1|p2ggsave('visual/featureplot_eg.png', plotc, width = 10, height = 4)

DotPlot点图

genelist = c('LYZ','CD79A','CD8A','CD8B','GZMB','FCGR3A')p = DotPlot(scRNA, features = genelist)ggsave('visual/dotplot_eg.png', p, width = 7, height = 5)

DoHeatmap热图

genelist = read.csv("cell_identify/top10_diff_genes_wilcox.csv")genelist <- pull(genelist, gene) %>% as.characterp = DoHeatmap(scRNA, features = genelist, group.by = "seurat_clusters")ggsave('visual/heatmap_eg.png', p, width = 12, height = 9)

FeatureScatter散点图

p1 <- FeatureScatter(scRNA, feature1 = 'PC_1', feature2 = 'PC_2')p2 <- FeatureScatter(scRNA, feature1 = 'nCount_RNA', feature2 = 'nFeature_RNA')plotc = p1|p2ggsave('visual/featurescatter_eg.png', plotc, width = 10, height = 4)

DimPlot降维图

p1 <- DimPlot(scRNA, reduction = 'tsne', group.by = "celltype", label=T)p2 <- DimPlot(scRNA, reduction = 'umap', group.by = "Phase", label=T)p3 <- DimPlot(scRNA, reduction = 'pca', group.by = "celltype", label=T)p4 <- DimPlot(scRNA, reduction = 'umap', group.by = "seurat_clusters", label=T)plotc = (p1|p2)/(p3|p4)ggsave('visual/dimplot_eg.png', plotc, width = 10, height = 8)

获取帮助

本教程的目的在于把常用的单细胞分析流程串起来，适合有一定R语言基础的朋友参考。分析原理和代码我没有详细解释，官网有详细的教程和权威的解释，翻译好的中文教程也有多个版本，有兴趣的可以搜索一下。如果您学习本教程有一定困难，可以点击 “阅读原文” 找到作者联系方式，获取帮助。

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