单细胞数据处理中，如何将第一张图变成第二张图
P1
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P2
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#---------单细胞教程step7
#1.1 得到top_marker_genes的相关信息，为data.frame
library(dplyr)
top5 <- sce.markers %>% group_by(cluster) %>% top_n(5, avg_log2FC)
DoHeatmap(sce ,top3$gene,size=3)
#1.2 对数据进行标准化
sce.all <- ScaleData(sce,features = top5$gene)
#2.1 将基因按群体分割并排序
ll = split(top5$gene,top5$cluster)
ll = ll[ord]
#2.2 去除在多个群体中重复出现的基因
rmg=names(table(unlist(ll))[table(unlist(ll))>1])
ll = lapply(ll, function(x) x[!x %in% rmg])
#3.1 定义颜色
library(paletteer)
color <- c(paletteer_d("awtools::bpalette"),
paletteer_d("awtools::a_palette"),
paletteer_d("awtools::mpalette"))
myc = c('#d73027','#f46d43','#fdae61','#fee090',
'#ffffbf','#e0f3f8','#abd9e9','#74add1','#4575b4')
#3.2 将细胞类型转换为因子变量并设定顺序
#------绘制热图时更有序及易于解读
unique(sce.all$celltype)
ord = c('Naive CD4 T' ,'Memory CD4 T', 'CD8 T', 'NK',
'CD14+ Mono', 'FCGR3A+ Mono' ,'DC', 'B','Platelet')
sce.all$celltype = factor(sce.all$celltype ,levels = ord)
#4. 可视化
library(ggplot2)
DoHeatmap(sce.all,
features = unlist(ll),
group.by = "celltype",
assay = 'RNA',
group.colors = myc,label = F)+
scale_fill_gradientn(colors = c("white","grey","firebrick3"))
>引自生信技能树
