批量计算基因和基因之间的相关性，也是一项很好的应用。

场景

画出某个基因和免疫细胞的相关性图

数据准备

数据准备

所需要的两项数据，一个是基因表达数据，另一个是免疫细胞浸润的矩阵，行名要一致，都是样本名称

1. 准备画图函数

之前在批量处理——基因与免疫细胞的相关性已经计算过相关性，此次主要作图。
画图之前先对数据进行合并

plot_df <- cbind(immu_data, expr_data)

写一个画图函数

ggcorplot <- function(a,b,method="spearman"){
  corr_eqn <- function(x,y,digits=3) {
    test <- cor.test(x,y,method=method,exact=FALSE)
    paste(paste0("n = ",length(x)),
          paste0("r = ",round(test$estimate,digits),"(",method,")"),
          paste0("p value ",ifelse(test$p.value>0.001,paste0("= ",round(test$p.value,digits)),"< 0.001")),
          sep = ", ")
  }
  
  plot_df <- plot_df[,c(a,b)]
  names(plot_df) <- c("geneA","geneB")
  require(ggplot2)
  ggplot(plot_df,aes(geneA,geneB))+
    geom_point(col="black")+
    geom_smooth(method=lm, se=T,na.rm=T, fullrange=T,size=2,col="red")+
    geom_rug(col="#006fbc")+
    theme_minimal()+
    xlab(paste0(a," relative expression"))+
    ylab(paste0(b," relative score"))+
    ## 依靠函数来生成title
    labs(title = corr_eqn(plot_df$geneA,plot_df$geneB))+
    theme(plot.title = element_text(hjust = 0.5),
          plot.margin = margin(1, 1, 1, 1, "cm"))
}

这个画图函数是借鉴果子学生信课程

2.检验画图函数

yourgene = 'DDR1'
ggcorplot(yourgene, colnames(immu_data)[1], method = 'spearman')

效果还可以

3. 批量处理

有了函数，直接套用批量处理——for循环画图的步骤就可以得到图

picDir <- './cor_picture/'
if (!dir.exists(picDir)) {
  dir.create(picDir)
}

yourgene = 'DDR1'

for (i in colnames(immu_data)) {
  print(i)
  inputgene = yourgene
  p = ggcorplot(yourgene, i, method = 'spearman')
  ggsave(p, filename = paste0(picDir, inputgene, '_',i,'_correlation', '.pdf'), width = 7, height = 5)
}

批量出图效果还是不错的。

4. 完整代码

plot_df <- cbind(immu_data, expr_data)
ggcorplot <- function(a,b,method="spearman"){
  corr_eqn <- function(x,y,digits=3) {
    test <- cor.test(x,y,method=method,exact=FALSE)
    paste(paste0("n = ",length(x)),
          paste0("r = ",round(test$estimate,digits),"(",method,")"),
          paste0("p.value= ",round(test$p.value,digits)),
          sep = ", ")
  }
  
  plot_df <- plot_df[,c(a,b)]
  names(plot_df) <- c("geneA","geneB")
  require(ggplot2)
  ggplot(plot_df,aes(geneA,geneB))+
    geom_point(col="black")+
    geom_smooth(method=lm, se=T,na.rm=T, fullrange=T,size=2,col="red")+
    geom_rug(col="#006fbc")+
    theme_minimal()+
    xlab(paste0(a," relative expression"))+
    ylab(paste0(b," relative score"))+
    ## 依靠函数来生成title
    labs(title = corr_eqn(plot_df$geneA,plot_df$geneB))+
    theme(plot.title = element_text(hjust = 0.5),
          plot.margin = margin(1, 1, 1, 1, "cm"))
}


ggcorplot(yourgene, colnames(immu_data)[1], method = 'spearman')


picDir <- './cor_picture/'
if (!dir.exists(picDir)) {
  dir.create(picDir)
}

for (i in colnames(immu_data)) {
  print(i)
  inputgene = yourgene
  p = ggcorplot(yourgene, i, method = 'spearman')
  ggsave(p, filename = paste0(picDir, inputgene, '_',i,'_correlation', '.pdf'), width = 7, height = 5)
}