利用ggcor包绘制相关性图

今天小果利用ggcor包绘制好看的相关性图，代码如下：

1. 安装需要的R包

install.packages(“ggplot2”)

install.packages(“GSVA”)

install.packages(“data.table”)

install.packages(“ggnewscale”)

install.packafes(“ade4”)

#直接下载安装包安装

install.packages(“ggcor-master.tar.gz”, repos = NULL, type = “source”)

1. 导入需要的R包

library(ggplot2)

library(ggcor)

library(ggplot2)

library(data.table)

library(GSVA)

1. 代码展示

#导入需要的数据

#加载TCGA-BLCA表达谱FPKM

expr<-read.table(“tcga_blca_fpkm.txt”,sep = “\t”,row.names = 1,check.names = F,stringsAsFactors = F,header = T)

tumsam <- colnames(expr)[substr(colnames(expr),11,13) == “01A”] # 取出肿瘤样本

# 取出SIGLEC15表达量

siglec15 <- log2(expr[“SIGLEC15”,tumsam] + 1)

write.csv(siglec15, “gene.csv”)

# 加载immunotherapy-predicted pathways

immPath <- read.table(“immunotherapy predicted pathways.txt”,sep = “\t”,row.names = 1,check.names = F,stringsAsFactors = F,header = T)

immPath.list <- list()

for (i in rownames(immPath)) {

tmp <- immPath[i,”Genes”]

tmp <- toupper(unlist(strsplit(tmp,”, “,fixed = T)))

tmp <- gsub(” “,””,tmp)

immPath.list[[i]] <- tmp

}

# 计算immunotherapy-predicted pathways的富集得分

immPath.score <- gsva(expr = as.matrix(log2(expr[,tumsam] + 1)),

immPath.list,

method = “ssgsea”)

write.table(immPath.score, “immPath.txt”,sep = “\t”,row.names = T,col.names = NA,quote = F)

# 加载单个基因的表达量，也就是“1对多”的“1”

siglec15 <- read.csv(“gene.csv”, row.names = 1, check.names = F)

# 加载富集得分

immPath.score <- read.table(“immPath.txt”, check.names = F)

# 跟目标基因SIGLEC15的表达量合并

immPath.score <- rbind.data.frame(immPath.score,

siglec15)

# 循环计算相关性并绘制左下角

immCorSiglec15 <- NULL

for (i in rownames(immPath.score)) {

cr <- cor.test(as.numeric(immPath.score[i,]),

as.numeric(siglec15),

method = “pearson”)

immCorSiglec15 <- rbind.data.frame(immCorSiglec15,

data.frame(gene = “Siglec15″,

path = i,

r = cr$estimate,

p = cr$p.value,

stringsAsFactors = F),

stringsAsFactors = F)

}

immCorSiglec15$sign <- ifelse(immCorSiglec15$r > 0,”pos”,”neg”)

immCorSiglec15$absR <- abs(immCorSiglec15$r)

immCorSiglec15$rSeg <- as.character(cut(immCorSiglec15$absR,c(0,0.25,0.5,0.75,1),labels = c(“0.25″,”0.50″,”0.75″,”1.00”),include.lowest = T))

immCorSiglec15$pSeg <- as.character(cut(immCorSiglec15$p,c(0,0.001,0.01,0.05,1),labels = c(“<0.001″,”<0.01″,”<0.05″,”ns”),include.lowest = T))

immCorSiglec15[nrow(immCorSiglec15),”pSeg”] <- “Not Applicable”

immCorSiglec15$rSeg <- factor(immCorSiglec15$rSeg, levels = c(“0.25″,”0.50″,”0.75″,”1.00”))

immCorSiglec15$pSeg <- factor(immCorSiglec15$pSeg, levels = c(“<0.001″,”<0.01″,”<0.05″,”Not Applicable”,”ns”))

immCorSiglec15$sign <- factor(immCorSiglec15$sign, levels = c(“pos”,”neg”))

p1 <- quickcor(t(immPath.score),

type = “lower”,

show.diag = TRUE) +

geom_colour() +

add_link(df = immCorSiglec15,

mapping = aes(colour = pSeg, size = rSeg, linetype = sign),

spec.key = “gene”,

env.key = “path”,

diag.label = FALSE) +

scale_size_manual(values = c(0.5, 1, 1.5, 2)) +

scale_color_manual(values = c(“#19A078″,”#DA6003″,”#7570B4″,”#E8288E”,”#65A818″)) +

scale_fill_gradient2(low = “#9483E1”,mid = “white”,high = “#E11953”,midpoint=0) +

remove_axis(“x”)

ggsave(filename = “ggcor.pdf”, width = 10,height = 8)

#immPath.txt-富集得分文件

最终小果成功的画出了相关性网络图，看起来小果非常不错，有需要的可以借鉴学习奥。