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

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

最后编辑于：2021.11.19 18:04:54

人面猴
序言：七十年代末，一起剥皮案震惊了整个滨河市，随后出现的几起案子，更是在滨河造成了极大的恐慌，老刑警刘岩，带你破解...
沈念sama阅读 217,277评论 6赞 503
死咒
序言：滨河连续发生了三起死亡事件，死亡现场离奇诡异，居然都是意外死亡，警方通过查阅死者的电脑和手机，发现死者居然都...
沈念sama阅读 92,689评论 3赞 393
救了他两次的神仙让他今天三更去死
文/潘晓璐我一进店门，熙熙楼的掌柜王于贵愁眉苦脸地迎上来，“玉大人，你说我怎么就摊上这事。” “怎么了？”我有些...
开封第一讲书人阅读 163,624评论 0赞 353
道士缉凶录：失踪的卖姜人
文/不坏的土叔我叫张陵，是天一观的道长。经常有香客问我，道长，这世上最难降的妖魔是什么？我笑而不...
开封第一讲书人阅读 58,356评论 1赞 293
港岛之恋（遗憾婚礼）
正文为了忘掉前任，我火速办了婚礼，结果婚礼上，老公的妹妹穿的比我还像新娘。我一直安慰自己，他们只是感情好，可当我...
茶点故事阅读 67,402评论 6赞 392
恶毒庶女顶嫁案：这布局不是一般人想出来的
文/花漫我一把揭开白布。她就那样静静地躺着，像睡着了一般。火红的嫁衣衬着肌肤如雪。梳的纹丝不乱的头发上，一...
开封第一讲书人阅读 51,292评论 1赞 301
城市分裂传说
那天，我揣着相机与录音，去河边找鬼。笑死，一个胖子当着我的面吹牛，可吹牛的内容都是我干的。我是一名探鬼主播，决...
沈念sama阅读 40,135评论 3赞 418
双鸳鸯连环套：你想象不到人心有多黑
文/苍兰香墨我猛地睁开眼，长吁一口气：“原来是场噩梦啊……” “哼！你这毒妇竟也来了？” 一声冷哼从身侧响起，我...
开封第一讲书人阅读 38,992评论 0赞 275
万荣杀人案实录
序言：老挝万荣一对情侣失踪，失踪者是张志新（化名）和其女友刘颖，没想到半个月后，有当地人在树林里发现了一具尸体，经...
沈念sama阅读 45,429评论 1赞 314
护林员之死
正文独居荒郊野岭守林人离奇死亡，尸身上长有42处带血的脓包…… 初始之章·张勋以下内容为张勋视角年9月15日...
茶点故事阅读 37,636评论 3赞 334
白月光启示录
正文我和宋清朗相恋三年，在试婚纱的时候发现自己被绿了。大学时的朋友给我发了我未婚夫和他白月光在一起吃饭的照片。...
茶点故事阅读 39,785评论 1赞 348
活死人
序言：一个原本活蹦乱跳的男人离奇死亡，死状恐怖，灵堂内的尸体忽然破棺而出，到底是诈尸还是另有隐情，我是刑警宁泽，带...
沈念sama阅读 35,492评论 5赞 345
日本核电站爆炸内幕
正文年R本政府宣布，位于F岛的核电站，受9级特大地震影响，放射性物质发生泄漏。R本人自食恶果不足惜，却给世界环境...
茶点故事阅读 41,092评论 3赞 328
男人毒药：我在死后第九天来索命
文/蒙蒙一、第九天我趴在偏房一处隐蔽的房顶上张望。院中可真热闹，春花似锦、人声如沸。这庄子的主人今日做“春日...
开封第一讲书人阅读 31,723评论 0赞 22
一桩弑父案，背后竟有这般阴谋
文/苍兰香墨我抬头看了看天上的太阳。三九已至，却和暖如春，着一层夹袄步出监牢的瞬间，已是汗流浃背。一阵脚步声响...
开封第一讲书人阅读 32,858评论 1赞 269
情欲美人皮
我被黑心中介骗来泰国打工，没想到刚下飞机就差点儿被人妖公主榨干…… 1. 我叫王不留，地道东北人。一个月前我还...
沈念sama阅读 47,891评论 2赞 370
代替公主和亲
正文我出身青楼，却偏偏与公主长得像，于是被迫代替她去往敌国和亲。传闻我的和亲对象是个残疾皇子，可洞房花烛夜当晚...
茶点故事阅读 44,713评论 2赞 354

相关性计算与可视化画

相关性计算与可视化画

用途：

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，并用ggplot2画热图

2，输出相关性矩阵，并用corrplot画热图

结果示例

2.多组数据，多个矩阵组合

1）组合方式1

2）组合方式2

推荐阅读更多精彩内容