Engage to Life Energy
单细胞转录组基础分析八：可视化工具总结
发布日期：2021-12-10浏览：
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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)
VlnPlot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)
