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单细胞高分图片复现(独家代码附上)之系列(九): 高分主Fig-花瓣韦恩图

2025-06-18

在单细胞测序的数据挖掘中,如何从海量数据中精准捕捉关键信息,揭示细胞异质性与功能差异?大家首先想到的可能是venn图,在集合不超过5个的时候,venn图的可视化结果非常直观,但是一旦数据集增加,很难从图中解读出想要的信息。这时候,我们可以应用外形美观、展示信息又很直观的花瓣图。

每一片花瓣都代表一个组/一个细胞类型的差异基因,数字则表示差异基因数量,更直观地观察到不同细胞类型或不同处理下差异基因的表达模式与分布特征,从而快速锁定关键基因,深入挖掘其背后的生物学机制。

下面我们来学习花瓣图的绘制方法:

主要需要ggVennDiagram和ggplot2两个包

一、数据准备,我们需要差异基因列表和对应的cluster名字:

library(ggplot2)

library("VennDiagram")

> str(sub_gene_list)

List of 7

$ BCells : chr [1:3487] "Rps29" "Rpl37a" "Rps27" "Sub1" ...

$ DCs : chr [1:3427] "Rpl37a" "Rps29" "Rbm3" "Pfn1" ...

$ ECs : chr [1:4803] "Rps29" "Rpl37a" "Rplp1" "Rps15" ...

$ EpithelialCells: chr [1:9566] "Trp63" "Serpinb5" "Krt6b" "Krt6a" ...

$ Macrophages : chr [1:3523] "Rps29" "Pfn1" "Rpl37a" "Rbm3" ...

$ Neutrophils : chr [1:2298] "Pfn1" "Col1a1" "Cd52" "S100a8" ...

$ TCells : chr [1:2972] "Rpl37a" "Rps29" "S100a9" "Rpl38" ...

二、画图

all_diff_genes = read.table("/PERSONALBIO/work/All_diff_gene.xls",sep="\t",header = T)

sub_diff_genes = all_diff_genes[all_diff_genes$cluster %in%c("BCells","TCells","Neutrophils"),]

unique(sub_diff_genes$cluster)

sub_gene_list <- split(sub_diff_genes$gene, sub_diff_genes$cluster)

p1=ggVennDiagram(sub_gene_list, label_alpha=0)+ scale_fill_distiller(palette = "RdBu")

sub_diff_genes = all_diff_genes[all_diff_genes$cluster %in%c("BCells","TCells","Neutrophils","DCs","Macrophages"),]

unique(sub_diff_genes$cluster)

sub_gene_list <- split(sub_diff_genes$gene, sub_diff_genes$cluster)

p1=ggVennDiagram(sub_gene_list, label_alpha=0)+ scale_fill_distiller(palette = "RdBu")

sub_diff_genes = all_diff_genes[all_diff_genes$cluster %in%c("BCells","TCells","Neutrophils","DCs","Macrophages","EpithelialCells","ECs"),]

unique(sub_diff_genes$cluster)

sub_gene_list <- split(sub_diff_genes$gene, sub_diff_genes$cluster)

p = ggVennDiagram(sub_gene_list,label_size= 5,label= "count",label_geom = "text",

set_color = CustomCol2(1:7))+scale_fill_gradient(low="#222F75",high = "firebrick")

花瓣图以更加直观、生动的方式呈现数据,能够迅速捕捉到差异基因的关键信息;花瓣的大小、颜色、等属性可自定义,以展示差异基因的多种生物学特征,如表达水平、差异显著性、功能注释等。这种多维度的展示方式,有助于全面、深入地理解数据。

大家都动手试试吧!绘制图片或者复现代码过程中,如果老师遇到疑惑,欢迎拨打我们的热线电话或者联系我们的驻地销售。