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单细胞分析实录(8): 展示marker基因的4种图形(一)

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今天的内容讲讲单细胞文章中经常出现的展示细胞marker的图:tsne/umap图、热图、堆叠小提琴图、气泡图,每个图我都会用两种方法绘制。

 

使用的数据来自文献:Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. 去年7月发表在Cell Research上的关于鼻咽癌的文章,数据下载: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150430

 

 

髓系细胞数量相对少一些,为了方便演示,选它作为例子。

 

library(Seurat)
library(tidyverse)
library(harmony)
Myeloid=read.table("Myeloid_mat.txt",header = T,row.names = 1,sep = "\t",stringsAsFactors = F)
Myeloid_anno=read.table("Myeloid_anno.txt",header = T,sep = "\t",stringsAsFactors = F)

 

导入数据的时候需要注意一个地方:从cell ranger得到的矩阵,每一列的列名会在CB后面加上”-1″这个字符串,在R里面导入数据时,会自动转化为”.1″,在做匹配的时候需要注意一下。我已经提前转换为”_1″

 

> summary(as.numeric(Myeloid["CD14",]))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.000   0.000   0.689   1.080   2.111   4.500
> summary(as.numeric(Myeloid["PTPRC",]))
Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.000   1.200   1.681   1.607   2.124   3.520

 

考虑到下载的表达矩阵,表达值都不是整数,且大于0,推测该矩阵已经经过了标准化,因此下面的流程会跳过这一步

 

0. Seurat流程

 

我们直接把注释结果赋值到[email protected]矩阵中,后面省去聚类这一步

 

mye.seu=CreateSeuratObject(Myeloid)
[email protected]$CB=rownames([email protected]) 
[email protected]=merge([email protected],Myeloid_anno,by="CB")
rownames([email protected])[email protected]$CB
#替代LogNormalize这一步
mye.seu[["RNA"]]@data=mye.seu[["RNA"]]@counts
mye.seu <- FindVariableFeatures(mye.seu, selection.method = "vst", nfeatures = 2000)
mye.seu <- ScaleData(mye.seu, features = rownames(mye.seu))
mye.seu <- RunPCA(mye.seu, npcs = 50, verbose = FALSE)
mye.seu=mye.seu %>% RunHarmony("sample", plot_convergence = TRUE)
mye.seu <- RunUMAP(mye.seu, reduction = "harmony", dims = 1:20)
mye.seu <- RunTSNE(mye.seu, reduction = "harmony", dims = 1:20)
#少了聚类
DimPlot(mye.seu, reduction = "tsne", group.by = "celltype", pt.size=1)+theme(
  axis.line = element_blank(),
  axis.ticks = element_blank(),axis.text = element_blank()
)
ggsave("tsne1.pdf",device = "pdf",width = 17,height = 14,units = "cm")

 

 

基本符合原图,三个亚群分得开,三个亚群分不开。

 

接下来,我用4种方式展示marker基因,这些基因可以在文献的补充材料里面找到。

 

1. tsne展示marker基因

 

FeaturePlot(mye.seu,features = "CCR7",reduction = "tsne",pt.size = 1)+
  scale_x_continuous("")+scale_y_continuous("")+
  theme_bw()+ #改变ggplot2的主题
  theme( #进一步修改主题
    panel.grid.major = element_blank(),panel.grid.minor = element_blank(), #去掉背景线
    axis.ticks = element_blank(),axis.text = element_blank(), #去掉坐标轴刻度和数字
    legend.position = "none", #去掉图例
    plot.title = element_text(hjust = 0.5,size=14) #改变标题位置和字体大小
  )
ggsave("CCR7.pdf",device = "pdf",width = 10,height = 10.5,units = "cm")

 

 

另一种方法就是把tsne的坐标和基因的表达值提取出来,用ggplot2画,其实不是很必要,因为FeaturePlot也是基于ggplot2的,我还是演示一下

 

mat1=as.data.frame(mye.seu[["RNA"]]@data["CCR7",])
colnames(mat1)="exp"
mat2=Embeddings(mye.seu,"tsne")
mat3=merge(mat2,mat1,by="row.names")
#数据格式如下:
> head(mat3)
Row.names     tSNE_1     tSNE_2   exp
1 N01_AAACGGGCATTTCAGG_1   5.098727  32.748145 0.000
2 N01_AAAGATGCAATGTAAG_1 -24.394040  26.176422 0.000
3 N01_AACTCAGGTAATAGCA_1  11.856730   8.086553 0.000
4 N01_AACTCAGGTCTTCGTC_1  10.421878  12.660407 0.000
5 N01_AACTTTCAGGCCATAG_1  33.555756 -10.437406 1.606
6 N01_AAGACCTTCGAATGGG_1 -23.976967  11.897753 0.738
mat3%>%ggplot(aes(tSNE_1,tSNE_2))+geom_point(aes(color=exp))+
  scale_color_gradient(low = "grey",high = "purple")+theme_bw()
ggsave("CCR7.2.pdf",device = "pdf",width = 13.5,height = 12,units = "cm")

 

 

用ggplot2的好处就是图形修改很方便,毕竟ggplot2大家都很熟悉

 

2. 热图展示marker基因

 

画图前,需要给每个细胞一个身份,因为我们跳过了聚类这一步,此处需要手动赋值

 

Idents(mye.seu)="celltype"
library(xlsx)
markerdf1=read.xlsx("ref_marker.xlsx",sheetIndex = 1)
markerdf1$gene=as.character(markerdf1$gene)
# 这个表格整理自原文的附表,选了53个基因
#数据格式
# > head(markerdf1)
# gene   celltype
# 1    S100B DC2(CD1C+)
# 2 HLA-DQB2 DC2(CD1C+)
# 3   FCER1A DC2(CD1C+)
# 4     CD1A DC2(CD1C+)
# 5     PKIB DC2(CD1C+)
# 6    NDRG2 DC2(CD1C+)
DoHeatmap(mye.seu,features = markerdf1$gene,label = F,slot = "scale.data")
ggsave("heatmap.pdf",device = "pdf",width = 23,height = 16,units = "cm")

 

label = F不在热图的上方标注细胞类型,

 

slot = “scale.data”使用scale之后的矩阵画图,默认就是这个

 

 

接下来用pheatmap画,在布局上可以自由发挥

 

library(pheatmap)
[email protected][,c("CB","celltype")]
colanno=colanno%>%arrange(celltype)
rownames(colanno)=colanno$CB
colanno$CB=NULL
colanno$celltype=factor(colanno$celltype,levels = unique(colanno$celltype))

 

先对细胞进行排序,按照celltype的顺序,然后对基因排序

 

rowanno=markerdf1
rowanno=rowanno%>%arrange(celltype)

 

提取scale矩阵的行列时,按照上面的顺序

 

mat4=mye.seu[["RNA"]]@scale.data[rowanno$gene,rownames(colanno)]
mat4[mat4>=2.5]=2.5
mat4[mat4 < (-1.5)]= -1.5 #小于负数时,加括号!

 

下面就是绘图代码了,我加了分界线,使其看上去更有区分度

 

pheatmap(mat4,cluster_rows = F,cluster_cols = F,
         show_colnames = F,
         annotation_col = colanno,
         gaps_row=as.numeric(cumsum(table(rowanno$celltype))[-6]),
         gaps_col=as.numeric(cumsum(table(colanno$celltype))[-6]),
         filename="heatmap.2.pdf",width=11,height = 7
         )

 

 

先写到这儿吧(原本以为能写完的),剩下的气泡图、堆叠小提琴图改天再补上。

 

因水平有限,有错误的地方,欢迎批评指正!

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