目录
R语言之可视化①误差棒
R语言之可视化②点图
R语言之可视化③点图续
R语言之可视化④点韦恩图upsetR
R语言之可视化⑤R图形系统
R语言之可视化⑥R图形系统续
R语言之可视化⑦easyGgplot2散点图
R语言之可视化⑧easyGgplot2散点图续
R语言之可视化⑨火山图
R语言之可视化⑩坐标系统
R语言之可视化①①热图绘制heatmap
R语言之可视化①②热图绘制2
R语言之可视化①③散点图+拟合曲线
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散点图一般用于展示两个变量之间的关系（比如线性相关）例如两个基因表达量的相关性。
cor.test(datagene2)
Pearson's product-moment correlation
data: data gene1 and data$gene2
t = 2.4858, df = 395, p-value = 0.0133495 percent confidence interval:
0.02600102 0.21984192cor 0.1241053
实例：通过以下代码计算两个基因的相关性
-
①使用ggplot2绘制
p1 <- ggplot(data = data, mapping = aes(x = gene1,
y = gene2)) +
geom_point(colour = "#426671", size = 2) + geom_smooth(method = lm,colour='#764C29',fill='#E7E1D7')
p1 <- p1+ stat_cor(method = "pearson",
label.x = 0.15,
label.y = 30)+xlim(0,0.44)
p1
p1 <- p1 + xlab("gene1") +
theme(axis.title.x = element_text(size = 16,
face = "bold",
vjust = 0.5,
hjust = 0.5))+
ylab("gene2") +
theme(axis.title.y = element_text(size = 16,
face = "bold",
vjust = 0.5,
hjust = 0.5))+
theme_bw()
p1
-
②使用ggscatter绘制
ggscatter(data, x = "gene1", y = "gene2",
color = "#426671", size =2, # Points color, shape and size
add = "reg.line", # Add regressin line
add.params = list(color = "#764C29", fill = "#E7E1D7"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+stat_cor(method = "pearson",
label.x = 0.15,
label.y = 30)+xlim(0,0.44)+
xlab("gene1") + ylab('gene2)
theme(axis.title.x = element_text(size = 16,
face = "bold",
vjust = 0.5,
hjust = 0.5))+
ylab("gene2") +
theme(axis.title.y = element_text(size = 16,
face = "bold",
vjust = 0.5,
hjust = 0.5))+
theme_bw()
p1
可以看出两个基因关联性并不高。
一些ggscatter的例子
set.seed(1234)
dat <- data.frame(cond = rep(c("A", "B"), each=10),
xvar = 1:20 + rnorm(20,sd=3),
yvar = 1:20 + rnorm(20,sd=3))
head(dat)
library(ggplot2)
绘制最基本的线性回归图
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) # Use hollow circles
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm) # Add linear regression line
# (by default includes 95% confidence region)
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth(method=lm, # Add linear regression line
se=FALSE) # Don't add shaded confidence region
ggplot(dat, aes(x=xvar, y=yvar)) +
geom_point(shape=1) + # Use hollow circles
geom_smooth() # Add a loess smoothed fit curve with confidence region
#> `geom_smooth()` using method = 'loess'
可以自定义设置点的颜色和大小
# Set color by cond
ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1)
# Same, but with different colors and add regression lines
ggplot(dat, aes(x=xvar, y=yvar, color=cond)) +
geom_point(shape=1) +
scale_colour_hue(l=50) + # Use a slightly darker palette than normal
geom_smooth(method=lm, # Add linear regression lines
se=FALSE) # Don't add shaded confidence region
# Extend the regression lines beyond the domain of the data
ggplot(dat, aes(x=xvar, y=yvar, color=cond)) + geom_point(shape=1) +
scale_colour_hue(l=50) + # Use a slightly darker palette than normal
geom_smooth(method=lm, # Add linear regression lines
se=FALSE, # Don't add shaded confidence region
fullrange=TRUE) # Extend regression lines
# Set shape by cond
ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point()
# Same, but with different shapes
ggplot(dat, aes(x=xvar, y=yvar, shape=cond)) + geom_point() +
scale_shape_manual(values=c(1,2)) # Use a hollow circle and triangle
