这个图中的“饼图矩阵”展示的是survival-related基因在不同的癌型和通路中所占的比例，填充颜色深浅和扇形大小代表所占比例的高低，可以说是一个非常直观的展示方法，经过小编一番研究，终于确定了这个绘图R包是scatterpie。那么今天小编就向大家介绍一下这个图的绘制方法吧~
1. 使用文章图复现
小编没能下载源文件，只好自己编一些数字了~
在EXCEL中使用=RANDBETWEEN(0,100)生成0至100间的随机矩阵。生成4个矩阵，读入数据分别命名为M1,M2,M3和M4.
整理成这种格式
x为CANCER
Y为PATHWAY
A,B,C,D为四维数据
install.packages("scatterpie")library(ggplot2)library(scatterpie)library(RColorBrewer)Color<-brewer.pal(4, "Set2")ggplot() +geom_scatterpie(data = DATA,aes(x=x, y=y,r=0.4),cols=colnames(DATA)[3:6]) +scale_fill_manual(name = "DATA", values = Color)+theme(panel.background = element_blank())
2. 气泡饼图
#构建数据set.seed(123)long <- rnorm(50, sd=100)lat <- rnorm(50, sd=50)d <- data.frame(long=long, lat=lat)d <- with(d, d[abs(long) < 150 & abs(lat) < 70,])n <- nrow(d)d$region <- factor(1:n)d$A <- abs(rnorm(n, sd=1))d$B <- abs(rnorm(n, sd=2))d$C <- abs(rnorm(n, sd=3))d$D <- abs(rnorm(n, sd=4))d[1, 4:7] <- d[1, 4:7] * 3head(d)
d$radius <- 6 * abs(rnorm(n))p <- ggplot() + geom_scatterpie(aes(x=long, y=lat, group=region, r=radius), data=d,cols=LETTERS[1:4], color=NA) + coord_equal()p + geom_scatterpie_legend(d$radius, x=-140, y=-70)
corrplot的功能是对相关矩阵或一般矩阵的图形展示。它还包含一些算法对矩阵重新排序。此外，corrplot可以选择颜色、文本标签、颜色标签、布局等。
小编想尝试是否可用corrplot中的饼图绘制来复现上图。首先我们来介绍一下这个R包。
install.packages("corrplot")library(corrplot)data(mtcars)
M <- cor(mtcars) #计算相关性系数
set.seed(0) #设定生成随机数的种子##定义几组颜色col1 <- colorRampPalette(c("#7F0000", "red", "#FF7F00", "yellow", "white","cyan", "#007FFF", "blue","#00007F"))par(ask = FALSE)#将绘图区分割成规则的几个部分,ask在新图绘制前是否进行提示
1. 不同排序方式展示图
corrplot(M, order = "AOE", col = col1(200), cl.length = 21, addCoef.col = "grey")corrplot(M, order = "FPC", col = col1(200), cl.length = 21, addCoef.col = "grey")corrplot(M, order = "hclust", col = col1(200), cl.length = 21, addCoef.col = "grey")corrplot(M, order = "alphabet", col = col1(200), cl.length = 21, addCoef.col = "grey")#order，相关矩阵字符的排序方法,可选c("AOE", "FPC", "hclust", "alphabet")#“AOE”表示特征向量的角度顺序。它是根据角度的顺序来计算的#“FPC”为第一个主成分排序#“hclust”，层次聚类顺序#“alphabet”按字母顺序排列#ddCoef.col，在图上添加系数的颜色#cl.length，在彩色标签中的数字文本的数目
2. 不同方法展示
corrplot(M, method = "circle", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "square", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "ellipse", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "number", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "shade", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "color", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")corrplot(M, method = "pie", col = col1(200), cl.length = 21, order = "AOE",addCoef.col = "grey",number.cex=0.7,tl.col="black")#method，相关性矩阵的可视化方法。#method = c("circle", "square", "ellipse", "number", "shade","color", "pie")#number.cex为系数标签大小#tl.col是坐标标签颜色
3. 设置成黑白点
wb <- c("white", "black")corrplot(M, col = wb, order = "AOE", bg = "pink",outline = TRUE, cl.pos = "r",tl.col="black")#outline是否绘制圆形、正方形和椭圆形的轮廓#cl.pos，颜色标签的位置，"n"是不要绘制标签，"r"颜色标签在右侧，"n"颜色标签在下方#bg背景色corrplot(M, col = wb,bg = "pink", order = "AOE",cl.pos = "r",tl.col="black")
4. 混合方法
(1)corrplot
## circle + ellipsecorrplot(M, order = "AOE", type = "upper", tl.pos = "d",tl.col="black",tl.cex=0.7)#type,展示矩阵类型，"upper"展示上部分，"lower"是下部分，"full"是全部#tl.pos，颜色标签位置corrplot(M, add = TRUE, type = "lower", method = "ellipse", order = "AOE",diag = FALSE, tl.pos = "n", cl.pos = "n")#add = TRUE，该图被添加到现有的图中#diag，是否在主对角线上显示相关系数## circle + squarecorrplot(M, order = "AOE",type = "upper", tl.pos = "d",tl.col="black",tl.cex=0.7)corrplot(M, add = TRUE, type = "lower", method = "square", order = "AOE",diag = FALSE, tl.pos = "n", cl.pos = "n")## circle + colorful numbercorrplot(M, order = "AOE", type = "upper", tl.pos = "d",tl.col="black",tl.cex=0.7)corrplot(M, add = TRUE, type = "lower", method = "number", order = "AOE",diag = FALSE, tl.pos = "n", cl.pos = "n",number.cex=0.8)## circle + black numbercorrplot(M, order = "AOE", type = "upper", tl.pos = "tp",tl.col="black",tl.cex=0.7)corrplot(M, add = TRUE, type = "lower", method = "number", order = "AOE",col = "black", diag = FALSE, tl.pos = "n", cl.pos = "n",number.cex=0.8)
(2) corrplot.mixed
M <- cor(mtcars)ord <- corrMatOrder(M, order = "AOE")M2 <- M[ord,ord]corrplot.mixed(M2)corrplot.mixed(M2, lower = "ellipse", upper = "circle")corrplot.mixed(M2, lower = "square", upper = "circle")corrplot.mixed(M2, lower = "shade", upper = "circle")corrplot.mixed(M2, tl.pos = "lt")corrplot.mixed(M2, tl.pos = "lt", diag = "u")corrplot.mixed(M2, tl.pos = "lt", diag = "l")corrplot.mixed(M2, tl.pos = "n")
5. 添加聚类
corrplot(M, order = "hclust", hclust.method = "ward.D2",addrect = 4,rect.col = "blue",tl.col="black")#addrect根据层次聚类绘制在图上矩形的数量#rect.col矩形边框颜色#hclust.method，当order为hclust时采用的聚类方法#hclust.method = c("complete", "ward", "ward.D", "ward.D2", "single",#"average", "mcquitty", "median", "centroid")
6. 其他范围的系数
（1）[0, 1]
corrplot(abs(M), order = "AOE", col = col1(200),cl.lim = c(0,1),tl.col="black")
（2）[-100, 100]
ran <- round(matrix(runif(225, -100,100), 15))corrplot(ran, is.corr = FALSE, cl.lim = c(-100, 100),tl.col="black")
7. 缺失值
M2 <- Mdiag(M2) = NAcorrplot(M2)corrplot(M2, na.label = "o",tl.col="black")corrplot(M2, na.label = "NA",tl.col="black")
8. 输入矩阵非正方形
corrplot(M[1:8,],tl.col="black")
9. 标记显著性值
corrplot(M, p.mat = res1$p, sig.level =0.05,insig = "blank",tl.col="black")corrplot(M, p.mat = res1$p, sig.level =0.05,insig = "p-value",tl.col="black")corrplot(M, p.mat = res1$p, sig.level =0.05,insig = "pch",tl.col="black")corrplot(M, p.mat = res1$p, sig.level =0.05,insig = "label_sig",tl.col="black")#sig.level大于0.05的值认为非显著的，标记为×#insig，特殊标记不显著相关系数#"blank"将不显著的标记为空白#"p-value"标记不显著的P值#"pch"不显著的标记为×#"label_sig"将显著的值标记
10. 标记置信区间，默认0.95
（1）彩色区间
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,plotCI = "circle", addg = "grey20", cl.pos = "n",tl.col="black")corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,plotCI = "rect", addg = "grey20", cl.pos = "n",tl.col="black")#p.mat矩阵的P值#low置信区间下界的矩阵#upp置信区间上界的矩阵#plotCI，绘制置信区间的方法。"n"不绘制置信区间；#plotCI ="rect"绘制矩形，矩形的上半部分表示上界，下半部分表示下界，同时在矩形上加入相关系数#plotCI = "circle",首先画一个绝对边界较大的圆，然后画一个绝对边界较小的圆。#plotCI = "circle",如果这两个界限是相同的符号，较小的圆圈将被擦除，从而形成一个环。#plotCI = "square"与"circle"相似
（2）黑白区间
corrplot(M, p.mat = res1$p, low = res1$lowCI, upp = res1$uppCI,col = c("white", "black"), bg = "pink", order = "AOE",plotCI = "circle", cl.pos = "n", pch.col = "red",tl.col="black")
11.图形复现（失败）
M1=as.matrix(read.csv("C:/Users/DELL/Desktop/Test/M1.csv",sep=",",header=T,quote="", check.names=F, row.names=1))corrplot(M1, is.corr = FALSE, method = "pie",col = col1(200),addgrid.col="white", #设置网格为白色outline="white", #边的颜色insig = "blank",tl.col="black",tl.cex=0.6)
今天我们不只介绍了文中使用的散点饼图矩阵的方法，还介绍了绘制相关性图的方法，都是很实用的数据展示方式，大家动手操作一下吧~
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