年过完了，继续好好学学吧！看到一种新的单细胞差异基因的展示方式，相关文章如下：
（Reference：Dissecting the cellular landscape and transcriptome network in viral myocarditis by single-cell RNA sequencing）
（Reference：Single-cell transcriptomics of the human retinal pigment epithelium and choroid in health and macular degeneration）
本来被横坐标的Delta给吓住了，后来仔细阅读了一下图例，发现很简单，其实就是表达基因的两种细胞比例的一个差值，其实类似于ggplot作图的修饰和基因标注，就不再赘述了，我们复现一下效果：
首先做一下差异基因：
library(Seurat)
mouse_data <- readRDS("D:/KS科研分享与服务公众号/mouse_data.rds")
DimPlot(mouse_data, label = T)
PMN0 <- subset(mouse_data, celltype=='PMN(0)')
DEGs_PMN0 <- FindMarkers(PMN0,
ident.1 = '10X_ntph_F',
ident.2 = '10X_ntph_M',
group.by = "orig.ident",
logfc.threshold = 0,
min.pct = 0)
计算下差异比例，和显著基因（用于标记）。
DEGs_PMN0$difference <- DEGs_PMN0$pct.1 - DEGs_PMN0$pct.2
DEGs_PMN0_sig <- DEGs_PMN0[which(DEGs_PMN0$p_val_adj<0.05 & abs(DEGs_PMN0$avg_log2FC) >0.25),]
DEGs_PMN0_sig$label <- rownames(DEGs_PMN0_sig)
最后作图即可：
library(ggplot2)
library(ggrepel)
ggplot(DEGs_PMN0, aes(x=difference, y=avg_log2FC)) +
geom_point(size=2, color="grey60") +
geom_text_repel(data=DEGs_PMN0_sig, aes(label=label),
color="black",fontface="italic")+
geom_point(data=DEGs_PMN0[which(DEGs_PMN0$p_val_adj<0.05 & DEGs_PMN0$avg_log2FC>0.1),],
aes(x=difference, y=avg_log2FC),
size=2, color="red")+
geom_point(data=DEGs_PMN0[which(DEGs_PMN0$p_val_adj<0.05 & DEGs_PMN0$avg_log2FC< -0.1),],
aes(x=difference, y=avg_log2FC),
size=2, color="blue")+
theme_classic()+
theme(axis.text.x = element_text(colour = 'black',size = 12),
axis.text.y = element_text(colour = 'black',size = 12),
axis.title = element_text(colour = 'black',size = 15),
axis.line = element_line(color = 'black', size = 1))+
geom_hline(yintercept = 0,lty=2,lwd = 1)+
geom_vline(xintercept = 0,lty=2,lwd = 1)+
ylab("Log-fold Change")+
xlab("Delta Percent")
转自：“KS科研分享与服务”微信公众号
如有侵权，请联系本站删除！
