美文网首页生物信息学
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

相关文章

  • 相关性计算与可视化画

    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: 输入数据示例: 代码 1,输出相关性矩阵...

  • 技能树直播课程学习-WGCNA-3-模块与临床性状关联

    1. 计算 ME 值 2. 计算模块与性状的相关性并可视化 3. 计算各基因表达量与模块 ME 和性状的关系(MM...

  • 相关性计算与检验

    成对数据进行相关性分析可使用可视化方法及相关性检验方法:可视化方法主要通过散点图观察数据的线性关系; 而相关性检验...

  • numpy必知必会-第八天

    36 计算两列数据间的相关性 皮尔逊相关系数计算公式如下: 例如: 计算iris_2d第一列与第三列的相关性。 解...

  • R语言-相关系数计算(一)

    应用R语言完成相关性检验,相关性矩阵及相关性可视化首先安装相应的R包 相关性分析的方法Pearson correl...

  • ggcorrplot|相关性矩阵可视化神器完整教程

    ggcorrplot可视化相关性矩阵 ggcorrplot(): A graphical display of a...

  • R: 相关系数

    ref:R画月亮阴晴圆缺:corrplot绘图相关系数矩阵 ref:R语言学习笔记之相关性矩阵分析及其可视化 - ...

  • 信息检索 - BM25

    简介 BM25用于计算Query与Doc相关性得分:首先对Query进行分词得到,然后计算Query中的每个词与D...

  • 盘一盘画相关性热图的几种方式

    R可视化相关性矩阵的几种方案 R中相关性矩阵的可视化解决方法在已经有很多了,我们在这里总结一些常用的都有哪些: g...

  • ggplot2绘制相关性热图(heatmap)

    你只是想画个相关性热图!!!! 生信分析中经常要做基因与代谢物之间的相关性热图,R中号称绘制相关性热图的包有许多,...

网友评论

    本文标题:相关性计算与可视化画

    本文链接:https://www.haomeiwen.com/subject/axtrtrtx.html