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 = 0.01334

95 percent confidence interval:
0.02600102 0.21984192

cor 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