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相关性计算与可视化画

相关性计算与可视化画

作者: bioYIYI | 来源:发表于2021-11-18 09:51 被阅读0次

用途：

计算组间相关性，并进行可视化

1.一组数据，单个矩阵

使用示例：

Rscript corrplot.r correlation.format_data_rename/SampleID_correlation.format.txt COR/SampleID

输入数据示例：

image.png

代码

1，输出相关性矩阵，并用ggplot2画热图

args = commandArgs(T)
if (length(args) !=2){
    print("Rscript this R <infile> <pre_outfile>")
    q()
}
library(ggplot2)
library(reshape2)
library(pheatmap)

data <- read.table(args[1],header=T,sep="\t",stringsAsFactors=FALSE,row.names =1)
corr <- cor(data)
out <- data.frame(sample=rownames(corr),as.data.frame(corr))
write.table(out,paste(args[2],".correlation.txt",sep=""),row.names =F,col.names =T,quote =F,sep="\t")
corr_met <- melt(corr)
out <- data.frame(sample=rownames(corr_met),as.data.frame(corr_met))
write.table(out,paste(args[2],".correlation2.txt",sep=""),row.names =F,col.names =T,quote =F,sep="\t")
ggplot(corr_met,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE))
ggsave(paste(args[2],".correlation_heatmap.pdf",sep = ""),width = 5,height =5)

2，输出相关性矩阵，并用corrplot画热图

args = commandArgs(T)
if (length(args) !=2){
    print("Rscript this R <infile> <pre_outfile>")
    q()
}
library("corrplot")
library(reshape2)

data <- read.table(args[1],header=T,sep="\t",stringsAsFactors=FALSE,row.names =1)
corr <- cor(data)
out <- data.frame(sample=rownames(corr),as.data.frame(corr))
write.table(out,paste(args[2],".correlation.txt",sep=""),row.names =F,col.names =T,quote =F,sep="\t")
corr_met <- melt(corr)
out <- data.frame(sample=rownames(corr_met),as.data.frame(corr_met))
write.table(out,paste(args[2],".correlation2.txt",sep=""),row.names =F,col.names =T,quote =F,sep="\t")

pdf(paste(args[2],".correlation.pdf",sep = ""),width = 6,height = 6)
color<-colorRampPalette(c("blue", "red"))(200)
corrplot(corr=corr,order = "AOE",type="upper",tl.pos = "d",col=color)
#如果想画饼图用以下代码
#corrplot(corr=corr,order = "AOE",type="upper",tl.pos = "d",col=color,method = "pie")
corrplot(corr = corr,add=TRUE, type="lower", method="number",order="AOE",diag=FALSE,tl.pos="n", cl.pos="n")
dev.off()

结果示例

*.correlation2.txt：


image.png

*.correlation.txt：


image.png

ggplot可视化：


image.png

corrplot可视化：


image.png image.png

2.多组数据，多个矩阵组合

1）组合方式1

ggplot绘制的结果进行组合：

library(ggplot2)
library(reshape2)
library(pheatmap)
library(ggpubr)
data = read.table("/*/COR//Pt17.correlation2.txt",header = T)
p1 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt17.correlation2.txt",header = T)
p2 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt17.correlation2.txt",header = T)
p3 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt75.correlation2.txt",header = T)
p4 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt92.correlation2.txt",header = T)
p5 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt96.correlation2.txt",header = T)
p6 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt107.correlation2.txt",header = T)
p7 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt109.correlation2.txt",header = T)
p8 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt114.correlation2.txt",header = T)
p9 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt116.correlation2.txt",header = T)
p10 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt120.correlation2.txt",header = T)
p11 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
data = read.table("/*/COR//Pt127.correlation2.txt",header = T)
p12 = ggplot(data,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_fill_continuous(low="blue",high="red")+geom_text(aes(label=round(value,3)),angle=0,size=3)+theme_classic()+theme(axis.line = element_blank())+theme(axis.text.x = element_text(size = 10,angle = 90),axis.text.y = element_text(size = 10,angle = 0))+labs(x="",y="",title = "Pearson Correlation Coefficient")+theme(plot.title = element_text(hjust = 0.5))+guides(fill = guide_legend(title = expression("R"),reverse = TRUE)) 
ggarrange(p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11,p12,ncol=4,nrow=3) 
ggsave(paste("../IM.txt.correlation_heatmap.pdf",sep = ""),width = 20,height =15)

结果示例：


image.png

2）组合方式2

ggplot绘制的结果进行组合：

library(ggplot2)
library(reshape2)
library(pheatmap)
library(ggpubr)
IM<-read.table("IM.format",header = T)
MPLC<-read.table("MPLC.format",header = T)
COHORT2<-read.table("COHORT2.format",header = T)


pdf(paste("2/IM.correlation.pdf",sep = ""),width = 30,height =20,onefile=FALSE)
ggplot(IM,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_x_discrete(breaks=NULL)+scale_y_discrete(breaks=NULL)+scale_fill_gradient2(mid="white",high="red",low="blue",midpoint=-0.2)+labs(x="",y="",title = "IM Pearson_Correlation_Coefficient")+theme( plot.title = element_text(size=30,hjust = 0.5),legend.title=element_text(size=30),legend.text=element_text(size=30), panel.background=element_blank(),strip.text=element_text(size=25) )+guides(fill = guide_legend(title = expression("R"),reverse = TRUE))+facet_wrap(~patient, ncol=3,scales="free")
dev.off()

pdf(paste("2/MPLC.correlation.pdf",sep = ""),width = 30,height =20,onefile=FALSE)
ggplot(MPLC,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_x_discrete(breaks=NULL)+scale_y_discrete(breaks=NULL)+scale_fill_gradient2(mid="white",high="red",low="blue",midpoint=-0.2)+labs(x="",y="",title = "MPLC Pearson_Correlation_Coefficient")+theme( plot.title = element_text(size=30,hjust = 0.5),legend.title=element_text(size=30),legend.text=element_text(size=30), panel.background=element_blank(),strip.text=element_text(size=25) )+guides(fill = guide_legend(title = expression("R"),reverse = TRUE))+facet_wrap(~patient, ncol=4,scales="free")
dev.off()


pdf(paste("2/COHORT2.correlation.pdf",sep = ""),width = 30,height =20,onefile=FALSE)
ggplot(COHORT2,aes(x=Var1,y=Var2,fill=value))+geom_tile()+scale_x_discrete(breaks=NULL)+scale_y_discrete(breaks=NULL)+scale_fill_gradient2(mid="white",high="red",low="blue",midpoint=-0.2)+labs(x="",y="",title = "COHORT2 Pearson_Correlation_Coefficient")+theme( plot.title = element_text(size=30,hjust = 0.5),legend.title=element_text(size=30),legend.text=element_text(size=30), panel.background=element_blank(),strip.text=element_text(size=25) )+guides(fill = guide_legend(title = expression("R"),reverse = TRUE))+facet_wrap(~patient, ncol=8,scales="free")
dev.off()

结果示例：


image.png

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