【原】复现Nature图表：单细胞UMAP图指示celltype和marker基因

TS的美梦 2024-04-15

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看到了一篇Nature的UMAP图，展示的是单细胞，但是特点是标记了celltype的同时指示了marker基因。显然这个处理很有特色，我们可以用代码指示，但是很难一次性达到这个效果，最后借助AI可能会更加方便，一次性完成这个事情，关于AI的操作，我们是直接整了个视频，希望对你有用！完整版代码以发布微信VIP及QQ群，请自行下载！

（reference：IL-1β+ macrophages fuel pathogenic inflammation in pancreatic cancer)

普通的UMAP是这样的：

setwd('D:\\KS项目\\公众号文章\\复现nature单细胞UMAP降维图修饰')library(Seurat)library(ggplot2)library(ggrastr)library(tidydr)library(dplyr)library(ggrepel)
# load("C:/Users/tq199/Downloads/adj_scRNA.RData")adj_scRNA <- subset(adj_scRNA, celltype=="Other", invert=T)DimPlot(adj_scRNA, label = T)

之后我们提取坐标，用ggplot修饰一下：

df <- adj_scRNA@reductions$umap@cell.embeddings%>%   as.data.frame() %>%  cbind(cell_type = adj_scRNA@meta.data$celltype)
label <- df %>%group_by(cell_type) %>%  summarise(UMAP_1 = median(UMAP_1),            UMAP_2 = median(UMAP_2))%>%  as.data.frame()rownames(label) <- label$cell_type
label$number <- seq(1:9)
cols= c('#7F3C8D' ,'#11A579', '#3969AC',        '#E73F74', '#80BA5A', '#E68310',        '#008695', '#CF1C90', '#f97b72')
#ggplot作图p = ggplot()+  geom_point_rast(data=df, aes(x= UMAP_1 , y = UMAP_2 ,color = cell_type),size = 1,shape=16) +  scale_color_manual(values = alpha(cols,0.3))+ #设置下透明度  theme_classic()+  theme(panel.grid.major = element_blank(),        panel.grid.minor = element_blank(),        axis.ticks = element_blank(),        axis.line = element_blank(),        axis.title = element_blank(),        axis.text = element_blank(),        legend.position = 'none')+  geom_point(data = label, aes(x= UMAP_1 , y = UMAP_2), size=6, color='white', alpha=0.9)+  geom_point(data = label, aes(x= UMAP_1 , y = UMAP_2), size=6, color='black', shape=21)+  geom_text(data = label,            mapping = aes(x= UMAP_1 , y = UMAP_2, label = number),            color='black')

然后标记基因即可：

marker_gene <- FindAllMarkers(adj_scRNA, only.pos = T,                              logfc.threshold = 0.8, min.pct = 0.8)
#每种celltype挑选5个展示marker_genes <- marker_gene %>%   group_by(cluster) %>%   top_n(n = 5, wt = avg_log2FC) 
write.csv(marker_genes, file = 'marker_genes.csv')
p + geom_label_repel(  data=label[which(label$UMAP_1>0 &label$UMAP_2>0),],   aes(x= UMAP_1, y = UMAP_2,label=labels),   size=3,  nudge_x = 4,  box.padding = 0.5,  nudge_y = 8,  segment.curvature = -0.1,  segment.ncp = 3,  segment.angle = 20,  direction = "y", hjust = "left")+  geom_label_repel(    data=label[which(label$UMAP_1>0 &label$UMAP_2<0),],     aes(x= UMAP_1, y = UMAP_2,label=labels),     size=3,    nudge_x = 4,    box.padding = 0.5,    nudge_y = -8,    segment.curvature = -0.1,    segment.ncp = 3,    segment.angle = 20,    direction = "y", hjust = "left")+  geom_label_repel(    data=label[which(label$UMAP_1<0 &label$UMAP_2>3),],     aes(x= UMAP_1, y = UMAP_2,label=labels),     size=3,    nudge_x = -6,    box.padding = 0.5,    nudge_y = 15,    segment.curvature = -0.1,    segment.ncp = 3,    segment.angle = 20,    direction = "y", hjust = "left")+  geom_label_repel(    data=label[which(label$UMAP_1<0 &label$UMAP_2<3),],     aes(x= UMAP_1, y = UMAP_2,label=labels),     size=3,    nudge_x = -3,    box.padding = 0.5,    nudge_y = -12,    segment.curvature = -0.1,    segment.ncp = 3,    segment.angle = 20,    direction = "y", hjust = "left")