t检验的核心思想是通过样本均值与方差的比较，评估两个总体均值是否存在显著差异。当有三个或更多组数据时，单次t检验已不再适用，因此通常的做法是先进行方差齐性检验与单因素方差分析（ANOVA），如果总体差异显著，再进行组间的两两t检验，用于具体比较每对样本组之间的差异。
多组样本执行两两t.test时，当有分组少于2个数据，t.test会报错，当要检测的数据为常量时，t.test会报错，
下面代码将解决以上问题
library(readr) library(dplyr) library(purrr) library(rstatix) # 构建函数进行两两t.test pf_ttest <- function(my_comparison, data) {group1 <- my_comparison[1]group2 <- my_comparison[2]data1 <- filter(data, group == group1) %>% pull(value)data2 <- filter(data, group == group2) %>% pull(value)cat(sprintf("比较 %s (n=%d) vs %s (n=%d)\n", group1, length(data1), group2, length(data2)))tryCatch({test_result <- t.test(data1, data2)tibble(group1 = group1,group2 = group2,p = test_result$p.value,statistic = test_result$statistic,df = test_result$parameter)},error = function(e) {cat(sprintf("错误: %s\n", e$message))}) }# 创建比较组 create_comparisons <- function(groups) {comp <- combn(groups, 2)lapply(1:ncol(comp), function(i) as.character(comp[, i])) }# 准备数据：2列，第1列为分组，第2列为数值 data <- readxl::read_xlsx("OP.xlsx") col_names <- names(data) xlab=col_names[1] ylab=col_names[2] data=dplyr::rename(data, group = 1, value = 2)# 创建比较组 my_comparisons <- create_comparisons(unique(data$group))stat_test <- map_dfr(my_comparisons, pf_ttest, data) %>%mutate(p.adj = p.adjust(p)) %>%add_significance(p.col = "p.adj",cutpoints = c(0, 0.001, 0.01, 0.05, 1),symbols = c("***", "**", "*", "ns"))
比较 Homo sapiens (n=22) vs M. assmensiss (n=4)
比较 Homo sapiens (n=22) vs M.mulatta (n=1)
错误: 'y'观察值数量不足
比较 Homo sapiens (n=22) vs M. arctoides (n=2)
比较 Homo sapiens (n=22) vs M. mulatta (n=15)
比较 Homo sapiens (n=22) vs M. fascicularis (n=1)
错误: 'y'观察值数量不足
比较 0mascus leucogenys (n=25) vs Pongo pygmaeus (n=7)
head(stat_test)
write_tsv(stat_test, "stat_test.tsv")
# 绘制箱形图 sig_comparisons <- stat_test %>%filter(p < 0.05) %>%select(group1, group2) %>%as.data.frame() %>% # 确保是 data.framesplit(1:nrow(.)) %>% # 按行分割lapply(function(x) as.character(x))# 创建基础图形 library(ggpubr) p1 <- ggboxplot(data,x = "group",y = "value",color = "group",alpha = 0.3,xlab = xlab,ylab = ylab,bxp.errorbar = TRUE,bxp.errorbar.width = 0.2,add = "jitter",legend = "none" ) +theme(plot.title = element_text(size = 15, hjust = 0.5),axis.title = element_text(size = 12),axis.text.x = element_text(size = 10, angle = 60, hjust = 1),axis.text.y = element_text(size = 10),axis.ticks = element_line(linewidth = 0.2),)p1
p1 + stat_compare_means(comparisons = sig_comparisons,method = "t.test",label = "p.signif", # 显示显著性符号（*, **, ***）symnum.args = list(cutpoints = c(0, 0.001, 0.01, 0.05, Inf),symbols = c("***", "**", "*", "ns")),hide.ns = TRUE, # 隐藏不显著的标签vjust = 0.75,tip.length = 0,step.increase = 0.05,size = 3)
