在进行科研数据绘图时，柱形图是非常常见的一种图形，这里我使用柱形图来展示细胞流式的结果。
这是流式的部分结果：UR代表右上象限，LR代表右下象限
先加载要用的R包：
>library(reshape2)
>library(ggplot2)
>library(ggsignif)
读入数据并整理数据：
>mito48 <- read.csv(file = "clipboard",header = T,sep = "\t") #读入数据
>colnames(mito48) <- c("Well","UR","LR")
>mito48$apop <- mito48$UR + mito48$LR
>mito48 <- mito48[,c(1,4)]
>head(mito48)
>mito48$dose <- rep(c("0","25","50","100","200","500","negtive"),3)
>mito48$dose <- as.factor(mito48$dose)
>mito48$Well <- c(rep(c("A","B","C"),each = 7))
>mito48$Well <- as.factor(mito48$Well)
>head(mito48)
sig <- mito48 # 把这个长数据的mito48赋值给一个新的数据框，因为这个格式在后面画图的时候会用到
准备画图所需要的格式：
>mito48 <- acast(mito48,dose~Well,value.var = "apop") #先把长数据换成宽数据(reshape2包)
>mito48
>mean <- apply(mito48,1,mean) #计算均数
>sd <- apply(mito48,1,sd) #计算标准差
>mito48 <- cbind(mito48,cbind(mean,sd)) #合并
>mito48 <- as.data.frame(mito48)
mito48$dose <- c("control","25","50","100","200","500","negtive")
mito48$order <- c(1:7)
mito48$min <- mito48$mean - mito48$sd #95%下限，用于制作柱形图的误差棒
mito48$max <- mito48$mean + mito48$sd
>mito48 #作图需要这么多列的内容
开始画图：
comparison <- list(c("0","25"),c("0","50"),c("0","100"),c("0","200"),c("0",500),c("0","negtive"))
#先设定要比较的两组，这里我用所有组和对照组（浓度为0）进行比较
ggplot(mito48,mapping = aes(x = reorder(dose,order),mean,fill = dose))+
geom_bar(stat = "identity",color = "black",size = 1.5,alpha = 0.8,width = 0.6)+
coord_cartesian(expand = F,xlim = c(0.3,7.7),ylim = c(0,75))+
geom_errorbar(aes(ymin = min,ymax = max),width = 0.2,size = 1.5)+ #添加误差棒
labs(x = "dose",y = "Double staining rate(%)(%)",title = expression(' '~mito-Ca^"2+"~'(24h)'))+ #~~之间表示要表达的公式
geom_text(mapping = aes(x = 7,y = max[7] + 1.5,size = 10,label = "*"),size = 10)+
geom_signif(data = sig,mapping = aes(dose,apop),stat = "signif",test = "t.test",comparisons = comparison,step_increase = 0.1,y_position = c(43,48,53,58,63,68),size = 1.52)+
theme(panel.background = element_blank(),
axis.line = element_line(colour = "black",size = rel(2)),
axis.title = element_text(size = rel(1.2)),
axis.text = element_text(size = rel(1.2)),
axis.text.y = element_text(hjust = 1),
axis.ticks = element_line(size = rel(1.5)),
legend.position = "none",
plot.title = element_text(size = rel(2),hjust = 0.5))
最终图片：
