基因卡方列联表P值检验——相关系数图形

墨蓝 2024-04-18 04:52 72阅读 0赞

### 相关系数图 ###

library(vegan)  
library(dplyr)  
library(corrplot)  
par(omi = c(0.3, 0.3, 0.3, 0.3),  
cex = 1.2,  
family = ‘Times New Roman’) \# windows系统可能需要安装其他字体包  
M <- cor(decostand(mtcars,method=“hellinger”,na.rm=T))\#计算相关系数矩阵  
corrplot(M, method = “circle”, type = ‘upper’)  
head(mtcars)  
mpg cyl disp hp drat wt qsec vs am gear carb  
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4  
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4  
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1  
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1  
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2  
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70]

### 加注标签的相关系数图形 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]

\#准备数据  
set.seed(20190420)  
n <- ncol(mtcars)  
grp <- c(‘Cluster\_1’, ‘Cluster\_2’, ‘Cluster\_3’) \# 分组名称  
sp <- c(rep(0.0008, 6), rep(0.007, 2), rep(0.03, 3), rep(0.13, 22)) \# P值  
gx <- c(-4.5, -2.5, 1) \# 分组的X坐标  
gy <- c(n-1, n-5, 2.5) \# 分组的Y坐标  
df <- data.frame(  
grp = rep(grp, each = n), \# 分组名称，每个重复n次  
gx = rep(gx, each = n), \# 组X坐标，每个重复n次  
gy = rep(gy, each = n), \# 组Y坐标，每个重复n次  
x = rep(0:(n - 1) - 0.5, 3), \# 变量连接点X坐标  
y = rep(n:1, 3), \# 变量连接点Y坐标  
p = sample(sp), \# 对人工生成p值进行随机抽样  
r = sample(c(rep(0.8, 4), rep(0.31, 7), rep(0.12, 22)))  
\#对人工生成r值进行随机抽样  
)

length(rep(grp, each = n))  
length(rep(gx, each = n))  
length(rep(gy, each = n))  
length(rep(0:(n - 1) - 0.5, 3))  
length(rep(n:1, 3))  
length(sample(sp))  
length(sample(c(rep(0.8, 4), rep(0.31, 7), rep(0.12, 22))) )

\#这一部分代码是按照原图图例说明处理线条宽度和颜色映射  
df <- df %>%  
mutate(  
lcol = ifelse(p <= 0.001, ‘\#1B9E77’, NA),  
\# p值小于0.001时，颜色为绿色，下面依次类推  
lcol = ifelse(p > 0.001 & p <= 0.01, ‘\#88419D’, lcol),  
lcol = ifelse(p > 0.01 & p <= 0.05, ‘\#A6D854’, lcol),  
lcol = ifelse(p > 0.05, ‘\#B3B3B3’, lcol),  
lwd = ifelse(r >= 0.5, 14, NA),  
\# r >= 0.5 时，线性宽度为14，下面依次类推  
lwd = ifelse(r >= 0.25 & r < 0.5, 7, lwd),  
lwd = ifelse(r < 0.25, 1, lwd)  
)

\#核心函数：segments。

segments(df g x , d f gx, df gx,dfgy, df x , d f x, df x,dfy, lty = ‘solid’, lwd = df l w d , c o l = d f lwd, col = df lwd,col=dflcol, xpd = TRUE) \# 绘制连接线

points(gx, gy, pch = 24, col = ‘blue’, bg = ‘blue’, cex = 3, xpd = TRUE)  
\#组标记点  
text(gx - 0.5, gy, labels = grp, adj = c(1, 0.5), cex = 1.5, xpd = TRUE)  
\#组名称

labels01 <- c(’<= 0.001’,‘0.001 < x <= 0.01’,‘0.01 < x <= 0.05’,’> 0.05’)  
labels02 <- c(’>= 0.5’, ‘0.25 - 0.5’, ‘< 0.25’)  
labels\_x <- rep(-6, 4)  
labels\_y <- seq(4.6, 2.6, length.out = 4)  
text(-6.5, 5.2, ‘P-value’, adj = c(0, 0.5), cex = 1.2, font = 2, xpd = TRUE)  
text(labels\_x, labels\_y, labels01, adj = c(0, 0.5), cex = 1.2, xpd = TRUE)  
points(labels\_x - 0.5, labels\_y, pch = 20, col = c(’\#1B9E77’, ‘\#88419D’,’\#A6D854’, ‘\#B3B3B3’),  
cex = 3, xpd = TRUE)  
lines\_x <- c(-6.5, -3, 0.5)  
lines\_y <- rep(1.2, 3)  
text(-6.5, 1.9, “Mantel’s r”, adj = c(0, 0.5), cex = 1.2, font = 2, xpd = TRUE)  
text(lines\_x + 1.5, lines\_y, labels02, adj = c(0, 0.5), cex = 1.2, xpd = TRUE)  
segments(lines\_x, lines\_y, lines\_x + 1, lines\_y, lwd = c(14, 7, 2.5), lty = ‘solid’,  
col = ‘\#B3B3B3’, xpd = TRUE)  
\#\#图例框框  
segments(-6.9, 5.6, -2.8, 5.6, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)  
segments(-2.8, 5.6, -2.8, 1.8, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)  
segments(-2.8, 1.8, 3.6, 1.8, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)  
segments(3.6, 1.8, 3.6, 0.7, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)  
segments(3.6, 0.7, -6.9, 0.7, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)  
segments(-6.9, 0.7, -6.9, 5.6, lty = ‘solid’, lwd = 1.2,  
col = ‘grey50’, xpd = TRUE)

## 相关系数加标签 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]  
install.packages(“ggpubr”)  
library(ggpubr)  
my\_data <- mtcars  
cor(my\_data d r a t , m y d a t a drat,my\_data drat,mydatampg)  
ggscatter(my\_data,  
x = “drat”, \#x变量  
y = “mpg”,\#y变量  
add = “reg.line”,\#\#拟合曲线  
conf.int = TRUE,\#\#置信区间阴影带  
cor.coef = TRUE, \#\#系数  
cor.method = “pearson”,\#方法  
xlab = “drat”, \#\# x轴  
ylab = “mg”)\#\# y轴

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/17/b272e11f15644cd5bd925ebab77b4737.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 1]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/17/e89fd18fd9e8401991e6b0123966c1ea.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3RhbmRlbGlu_size_16_color_FFFFFF_t_70 2]: https://image.dandelioncloud.cn/pgy_files/images/2024/04/17/edb529b3e4464f1ab71ee58ee86b0c0d.png