一 .从GEO数据库中下载表达矩阵文件

GSE112996_merged_fpkm_table.txtGSE112996_series_matrix.txt，把GSE112996_series_matrix.txt解压，得到如下两个文件，把这两个文件放到对应的project文件夹内（备注：GSE112996_merged_fpkm_table是这个数据集的补充表达矩阵，不是所有数据集都有这个格式的）

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二 . 读取这两个文件

2.1读取表达矩阵

rm(list=ls())
a <- read.table('GSE112996_merged_fpkm_table.txt.gz',
                header = T,
                row.names=1)#读取已经下载好的补充的表达矩阵压缩文件
a[1:4,1:4]

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可以看到读取的表达矩阵，第一列是基因名

raw_data<- a[,-1]

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去掉基因名，得到纯粹的表达矩阵raw_data

2.2读取表型信息

###读取表型信息
pheno <- read.csv(file = 'GSE112996_series_matrix.txt')

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pheno <- data.frame(num1 = strsplit(as.character(pheno[42,]),split='\t')[[1]][-1],
                    num2 = gsub('patient: No.','P',strsplit(as.character(pheno[51,]),split='\t')[[1]][-1]))

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三处理两个文件

  ####数据过滤，把表达量和为0的基因去掉（去O)
  data<- a[!apply(raw_data,1,sum)==0,]
  ####去除重复基因名的行，归一化
  data$median=apply(data[,-1],1,median)#计算每行的中位数，添加到 data数据中

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  data=data[order(data$GeneName,data$median,decreasing = T),]#排序
  data=data[!duplicated(data$GeneName),]#去除重复的基因名
  rownames(data)=data$GeneName#把基因名变成行名
  uni_matrix <- data[,grep('\\d+',colnames(data))]
#grep('\\d+',colnames(data)）识别有数字的位置，然后挑出来
  uni_matrix <- log2(uni_matrix+1)#把表达矩阵log
  colnames(uni_matrix)<- gsub('X','',gsub('\\.','\\-',colnames(uni_matrix)))#处理行名

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  uni_matrix<- uni_matrix[,order(colnames(uni_matrix))]#根据行名排序表达矩阵
save(uni_matrix,pheno,file = 'uni_matrix.Rdata')##把处理好的数据存好

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四、进行ssGSEA分析

4.1加载包载入数据

rm(list=ls())
加载包
{
  library(genefilter)
  library(GSVA)
  library(Biobase)
  library(stringr)
}
##载入数据
load('uni_matrix.Rdata')

4.2读取基因列表，得到免疫细胞对应的特异的基因

gene_set<- read.csv('geneji.csv',
                    header = T)##读取已经下载好的免疫细胞和对应基因列表，来源见文献附件
gene_set<-gene_set[, 1:2]#选取特异基因和对应的免疫细胞两行
head(gene_set)

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list<- split(as.matrix(gene_set)[,1], gene_set[,2])

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得到每种免疫细胞对应的基因

4.3评估Estimates GSVA enrichment scores.

gsva_matrix<- gsva(as.matrix(uni_matrix), list,method='ssgsea',kcdf='Gaussian',abs.ranking=TRUE)

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4.4把富集分数画热图

library(pheatmap)
gsva_matrix1<- t(scale(t(gsva_matrix)))#归一化
gsva_matrix1[gsva_matrix1< -2] <- -2
gsva_matrix1[gsva_matrix1>2] <- 2
anti_tumor <- c('Activated CD4 T cell', 'Activated CD8 T cell', 'Central memory CD4 T cell', 'Central memory CD8 T cell', 'Effector memeory CD4 T cell', 'Effector memeory CD8 T cell', 'Type 1 T helper cell', 'Type 17 T helper cell', 'Activated dendritic cell', 'CD56bright natural killer cell', 'Natural killer cell', 'Natural killer T cell')
pro_tumor <- c('Regulatory T cell', 'Type 2 T helper cell', 'CD56dim natural killer cell', 'Immature dendritic cell', 'Macrophage', 'MDSC', 'Neutrophil', 'Plasmacytoid dendritic cell')
anti<- gsub('^ ','',rownames(gsva_matrix1))%in%anti_tumor
pro<- gsub('^ ','',rownames(gsva_matrix1))%in%pro_tumor
non <- !(anti|pro)##设定三种基因
gsva_matrix1<- rbind(gsva_matrix1[anti,],gsva_matrix1[pro,],gsva_matrix1[non,])#再结合起来，使图分成三段
normalization<-function(x){
  return((x-min(x))/(max(x)-min(x)))}#设定normalization函数
nor_gsva_matrix1 <- normalization(gsva_matrix1)
annotation_col = data.frame(patient=pheno$num2)#加上病人编号
rownames(annotation_col)<-colnames(uni_matrix)#使编号能互相对应
bk = unique(c(seq(0,1, length=100)))#设定热图参数
pheatmap(nor_gsva_matrix1,
         show_colnames = F,
         cluster_rows = F,cluster_cols = F,
         annotation_col = annotation_col,
         breaks=bk,cellwidth=5,cellheight=5,
         fontsize=5,gaps_row = c(12,20),
         filename = 'ssgsea.pdf',width = 8)#画热图
save(gsva_matrix,gsva_matrix1,pheno,file = 'score.Rdata')

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五ggplot2绘图

rm(list=ls())
anti_tumor <- c('Activated CD4 T cell', 'Activated CD8 T cell', 'Central memory CD4 T cell', 'Central memory CD8 T cell', 'Effector memeory CD4 T cell', 'Effector memeory CD8 T cell', 'Type 1 T helper cell', 'Type 17 T helper cell', 'Activated dendritic cell', 'CD56bright natural killer cell', 'Natural killer cell', 'Natural killer T cell')
pro_tumor <- c('Regulatory T cell', 'Type 2 T helper cell', 'CD56dim natural killer cell', 'Immature dendritic cell', 'Macrophage', 'MDSC', 'Neutrophil', 'Plasmacytoid dendritic cell')
load('score.Rdata')
anti<- as.data.frame(gsva_matrix1[gsub('^ ','',rownames(gsva_matrix1))%in%anti_tumor,])
pro<- as.data.frame(gsva_matrix1[gsub('^ ','',rownames(gsva_matrix1))%in%pro_tumor,])
anti_n<- apply(anti,2,sum)
pro_n<- apply(pro,2,sum)
patient <- pheno$num2[match(colnames(gsva_matrix1),pheno$num1)]
library(ggplot2)
data <- data.frame(anti=anti_n,pro=pro_n,patient=patient)
anti_pro<- cor.test(anti_n,pro_n,method='pearson')
gg<- ggplot(data,aes(x = anti, y = pro),color=patient) + 
  xlim(-20,15)+ylim(-15,10)+
  labs(x="Anti-tumor immunity", y="Pro-tumor suppression") +
  geom_point(aes(color=patient),size=3)+geom_smooth(method='lm')+
  annotate("text", x = -5, y =7.5,label=paste0('R=',round(anti_pro$estimate,4),'\n','p<0.001'))
ggsave(gg,filename = 'cor.pdf', height = 6, width = 8)

画相关图，原理和上面差不多

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作者：mayoneday
链接：https://www.jianshu.com/p/b53fa8b5c253