NC图片复现：多组学散点图（象限）

采用的是MASS包中的mvrnorm函数生成二元正态分布的数据，数据的列名分别为"mRNA_FC"和"RPF_FC"。

完整代码:

# 加载必要的库
library(MASS)
library(ggplot2)
library(cowplot)

# 生成数据
set.seed(123) # 设置随机种子以获得可复制的结果
covariance <- matrix(c(1,0.8,0.8,1),nrow=2,byrow=TRUE)
data <- mvrnorm(n=1000, mu=c(0,0), covariance)
data <- as.data.frame(data)
colnames(data) <- c("mRNA_FC", "RPF_FC")

# 分组处理
data$group <- ifelse(abs(data$mRNA_FC) > log10(2) & abs(data$RPF_FC) > log10(2),
                     "mRNA+RPF_both", 
                     ifelse(abs(data$mRNA_FC) > log10(2),
                            "mRNA_only", 
                            ifelse(abs(data$RPF_FC) > log10(2),
                                   "RPF_only", NA)))

# 创建一个函数来绘制自定义的坐标轴
draw_axis_line <- function(length_x, length_y, 
                           tick_step = NULL, lab_step = NULL) {
  axis_x_begin <- -1 * length_x
  axis_x_end <- length_x
  axis_y_begin <- -1 * length_y
  axis_y_end <- length_y
  if (missing(tick_step))
    tick_step <- 1
  if (missing(lab_step))
    lab_step <- 2

  # 创建坐标轴刻度和标签的数据框
  tick_x_frame <- data.frame(ticks = seq(axis_x_begin, axis_x_end, by = tick_step))
  tick_y_frame <- data.frame(ticks = seq(axis_y_begin, axis_y_end, by = tick_step))
  lab_x_frame <- subset(data.frame(lab = seq(axis_x_begin, axis_x_end, by = lab_step), zero = 0), lab != 0)
  lab_y_frame <- subset(data.frame(lab = seq(axis_y_begin, axis_y_end, by = lab_step), zero = 0), lab != 0)

  tick_x_length = 0.05
  tick_y_length = 0.05

  # 开始绘制图形
  p <- ggplot() +
    # 绘制坐标轴
    geom_segment(aes(x = axis_x_begin, xend = axis_x_end, y = 0, yend = 0), size = 0.5) +
    geom_segment(aes(x = 0, xend = 0, y = axis_y_begin, yend = axis_y_end), size = 0.5) +
    # 添加刻度
    geom_segment(data = tick_x_frame, aes(x = ticks, xend = ticks, y = 0, yend = -tick_x_length)) +
    geom_segment(data = tick_y_frame, aes(x = 0, xend = -tick_y_length, y = ticks, yend = ticks)) +
    # 添加刻度标签
    geom_text(data = lab_x_frame, aes(x = lab, y = zero, label = lab), vjust = 1.5) +
    geom_text(data = lab_y_frame, aes(x = zero, y = lab, label = lab), hjust = 1.5) +
    theme_minimal() +
    theme(panel.grid = element_blank(),
          axis.text = element_blank())
  
  return(p)
}

# 生成九宫格图
p <- draw_axis_line(4, 4)
p1 <- p + 
  geom_point(data = data, aes(mRNA_FC, RPF_FC, color = group))+
  scale_color_manual(values = c("mRNA+RPF_both" = "#dd8653", "mRNA_only" = "#59a5d7", "RPF_only" = "#aa65a4", "#878787")) +
  labs(x = "mRNA:FC(P42/E15.5)", y = "RPF:FC(P42/E15.5)") +
  theme(legend.position = "bottom") +
  annotate("text", label = "bolditalic(Brain)", parse = TRUE, x = -2, y = 2, size = 4, colour = "black") +
  guides(color = guide_legend(title = "", ncol = 1, byrow = TRUE))

# 保存图形为PDF
ggsave("plot.pdf", plot = p1, height = 6, width = 5)

# 拼接多个图形
p_list <- list(p1=p1, p2=p1, p3=p1)
p_all <- plot_grid(plotlist = p_list, ncol = 3)

# 保存拼接后的图形为PDF
ggsave("plot2.pdf", plot = p_all, height = 4, width = 9)

# 保存数据为CSV格式
write.csv(data, file = "data.csv", row.names = FALSE)

对于使用自己的数据，数据应该是一个csv文件，包含两列，列名分别为"mRNA_FC"和"RPF_FC"。其中每一行代表一个观测，"mRNA_FC"和"RPF_FC"是这个观测的两个特征值。