R语言|在图形上标注P值的R包

遇到一个记录一个。

ggsignif: 多组之间的比较 #BiocManager::install('ggsignif') library("ggsignif") library('ggplot2') # geom_signif() # compare_means(value ~ group, data = exprSet_L,method="wilcox.test", paired=FALSE) exprSet_L=mydata gene_name='geneXX' my_comparisons <- list(c("T1","T2"), c("T1","T3"),c("T1", "T4")) ggplot(exprSet_L,aes(group,value))+ geom_boxplot(width=0.5)+ geom_jitter(aes(color=group))+ theme(plot.title=element_text(size = 25), axis.text.x=element_text(size=25,angle=0), axis.text.y=element_text(size=25), axis.title.x=element_text(size = 25), axis.title.y=element_text(size = 25))+ labs(title=paste0('Title:',gene_name),x=gene_name, y= 'Expression')+ geom_signif(comparisons = my_comparisons, step_increase = 0.1, map_signif_level = T, #T显示星号，F显示p值 test = t.test, size=1,textsize = 6)+ theme_set(theme_set(theme_bw(base_size=22))) iris 实例 library(ggplot2) library("ggsignif") #' Boxplot with p value #' #' v0.1 #' v0.2 自定义设置颜色、统计学方法等 #' #' @param dat 输入数据框：前几列是数据，比如身高、体重、肺活量; 最后一列是分组（如小学、初中、高中） #' @param i 数据第i列，除最后一行外的列编号 #' @param my_comparisons 两两比较 #' @param test.method 两两比较的统计学方法，默认是 wilcox.test。还可以是 t.test #' @param title 图标题 #' @param ylab y轴标题 #' @param cols 分组颜色，要和分组一致 #' @param legend.name 图例标题 //为啥不起作用？ #' @param alpha 小提琴图的不透明度,[0,1]。越小越透明 #' @param border.color 小提琴图 边框颜色，默认为黑色，我设置为无色 #' @param seed 随机数种子，用于生成 jitter 点的抖动位置 #' #' @return #' @export #' #' @examples drawBoxPlot=function(dat, i, my_comparisons, test.method=c("wilcox.test", "t.test")[2], cols=c("#307EC1", "orange", "deeppink"), title="", ylab="value", legend.name="", alpha=0.5, border.color="#11223300", seed=2025){ #i=1 message("> test.method:", test.method) message("> my_comparisons:", my_comparisons) # 取第i列和最后一列，给列名 d1=dat[,c(i, ncol(dat))] colnames(d1)=c("value", "group") # 绘图 set.seed(seed) p2=ggplot(d1, aes(group, value))+ geom_violin( aes(fill=group), scale="width", color=border.color, show.legend = F, alpha=alpha)+ #不透明度[0,1] geom_jitter(aes(color=group), width=0.2, size=0.5)+ geom_boxplot(width=0.15, outliers = F, fill="white", alpha=0.7)+ theme_classic(base_size = 12)+ theme( axis.text.x = element_text(angle=45, hjust=1, size=12), axis.text.y = element_text(size=10), plot.title=element_text(size = 10), )+ guides(color = guide_legend(override.aes = list(size = 3)))+ scale_color_manual(name=legend.name, values=cols)+ scale_fill_manual(values=cols)+ #labs(title=paste0('Title:',gene_name),x=gene_name, y= 'Expression')+ labs(x="", y=ylab, title=title )+ geom_signif(comparisons = my_comparisons, step_increase = 0.18, #y_position=max(d1[,1])+0.02, #test = t.test, test = test.method, #"wilcox.test", "t.test" map_signif_level = F, #T显示星号，F显示p值 size=0.5, textsize = 3)#+ #ylim(y=c(min(d1[,1]), max(d1[,1])+0.1) ) #theme_set(theme_set(theme_bw(base_size=22))) return(p2) } if(0){ # How to use # 1.准备数据：前几列是数据，最后一列是分组。 # 每次取一列数据(第i列)和最后的分组，用于绘图 dat=iris table(dat[, ncol(dat)]) # setosa versicolor virginica # 50 50 50 # 2. 手动设置分组: 用于两两t-检验 my_comparisons <- list(c("versicolor","setosa"), c("virginica","setosa"), c("virginica", "versicolor")) # 3. 保存每列的小图到list中 plots=list() for(i in 1:(ncol(dat)-1)){ #i=1 p1=drawBoxPlot(dat, i, my_comparisons, title=colnames(dat)[i], ylab = paste0("Length of ", colnames(dat)[i] ), #cols=c("#307EC1", "orange", "deeppink"), alpha=0.4 ) plots[[i]]=p1 } # 4.写到文件：设置小图列数，共用一个图例 outputRoot="D://" pdf(paste0(outputRoot, "_Boxplot.pdf"), width=5, height=7) patchwork::wrap_plots(plots, ncol = 2, guides = "collect") dev.off() }

右图是全貌，左图是其第一个小图。

ggpubr::stat_compare_means() 两组的比较 #install.packages("ggpubr") library(ggplot2) library(ggpubr) stat_compare_means(aes(group = location), ref.group="nLung", #label = "p.signif", label = "p.format", method = "t.test", paired=F, #label.y =5, show.legend = F) Ref: blog.csdn.net/ZaoJewin/article/details/135150515 blog.csdn.net/jiangshandaiyou/article/details/135956610 blog.csdn.net/weixin_65744007/article/details/127381702 .datanovia /en/blog/how-to-add-p-values-to-ggplot-facets/ stackoverflow /questions/70883042/adding-p-values-to-ggplot-facets-with-different-scales