常规做图收录（ggpubr）

1、成对分析

常规用法
rm(list=ls())
suppressMessages(library(ggpubr))
suppressMessages(library(tidyverse))
suppressMessages(library(vcd))

my_comparisons <- list(c("control", "treatment"))
ggpaired(comb_fre_new, x = "xj", y ="percentage",ylab ="Gene 3' UTR Modification Frequence\n(% of Total Modificantion Events)",
             line.color = "gray", line.size = 0.05,
             palette = "npg"#c("#0A5EB9","#DF3D8C"
            )
p<-p+stat_compare_means(comparisons = my_comparisons,
                        paired = T,method = "t.test",position = "identity",label = "p.signif")
做完是一根线，所以进阶改良（还是ggplot2 靠谱）
主要就是定义一个数xj 让它实现偏移
comb_fre_new$Group <- factor(comb_fre_new$Group, levels=unique(comb_fre_new$Group))
comb_fre_new$"x"<-c(rep(1,nrow(fed_fre)),rep(2,nrow(fasting_fre)))
comb_fre_new$xj <- jitter(comb_fre_new$x, amount=.04)
p<-ggplot(data=comb_fre_new, aes(y=percentage)) +
  geom_boxplot(aes(x=Group, group=Group), width=0.2, outlier.shape = NA) +
  geom_point(aes(x=xj,colour = factor(Group))) + scale_colour_manual(name="",values = c("control"="#0A5EB9", "treatment"="#DF3D8C"))+
  geom_line(aes(x=xj, group=gene_name),size=0.05,color="gray") +theme_bw()

2、相关性分析

suppressMessages(library(ggpubr))
sp<-ggscatter(utr3_mean, x = "control", y = "treatment",
              add.params = list(color =  "black",linetype="dashed"), # Customize reg. line
              add = "reg.line",  # Add regressin line
              conf.int = F,color ="#54C6DC",
              ellipse.alpha=0.5,
              xlab = "control",
              ylab="treatment",
              label = "Gene_symbol",
              label.select = targets,
              repel = T,
              font.label =  c(11, "bold", "#f16446")
)
sp<-sp + stat_cor(method = "pearson")