问题描述
我正在尝试对分组数据进行成对比较。我以前使用 stat_compare_means 成功地做到了这一点，但由于某种原因，这次它只显示了其中一个面板中的比较条。我试过了，但似乎无法让它工作。我在下面提供了一个简化的工作示例，下面只有两个条件。真实数据的集合数相同，但条件更多。非常感谢任何帮助！
data <- data.frame(sample = seq(1:83),condition = c("KO","KO","WT","WT"),set = c("di","tet","mono","di","mono"),score = c(0,-5.95940584704586,10.0932562947815,-3.16676604569923,4.46133814098881,-1.9888611720281,-7.08194974795108,3.17097628218171,-1.83986980857496,-0.843268716519414,1.40178526106758,-0.340339302553342,-3.76913603338144,4.28943971347741,-3.20197704274428,-3.54168755452774,15.414510676737,-5.85333426479177,-1.87949902971026,14.1424002410594,-2.14726619139082,-5.01378228438499,11.1131227496058,0.097013584879446,-6.55527134311774,12.2234175105232,-2.55259067519978,-6.68392512983342,15.8358484731832,-2.27825891764331,-5.40451835097939,11.2240240941934,-1.53075128507785,-4.80008896082703,15.3667539728667,-4.81370852797055,-2.69976280917806,21.9926791189896,0.61798090190648,-8.68663007652496,13.8852585926079,-0.80329005076484,-16.8459570277756,13.3500356549569,-5.59531483186873,-3.33602772657725,11.338954967882,-2.12614700145763,-3.37418493461362,11.5903340330526,-2.87224785160433,-14.7792521265679,7.7542705233175,-2.06876649679246,-1.31032740187699,17.6666627835987,-0.824420163207606,-4.82659116096503,-0.0626028094479509,-1.90431942338018,-20.3180652520285,6.83632028067972,-1.58570945276274,-2.77261532150261,2.98168865160908,-1.89922364414076,-5.80584721371712,7.13599592922731,-1.23964885854847,-5.1236580504364,14.5532078112838,-3.82017971494402,-4.39741499313133,11.7342969461195,-2.5852194454582,-3.18458897513284,12.7762556862722,1.1245622869403,-2.98430760077172,13.981837061262,-1.19532384849617))
ggplot(data aes(x=condition,y=score,fill=set)) +
stat_summary(fun=mean,geom="bar",colour="black",alpha=1) +
stat_summary(fun.data = mean_se,geom = "errorbar",width=0.25) +
geom_point(color="black",shape=21,show.legend=TRUE,size=3,stroke=1) +
facet_wrap(~set,scales="free") +
stat_compare_means(method = "t.test",comparisons=list(c("KO","WT")),hide.ns=F) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
scale_y_continuous(expand = expansion(mult = c(0.2,0.2)))
解决方法
我认为您应该将 group = condition 添加到 aes 中的 ggplot。
ggplot(temp,aes(x=condition,y=score,color = set,fill=set,group = condition)) +
stat_summary(fun=mean,geom="bar",colour="black",alpha=1) +
stat_summary(fun.data = mean_se,geom = "errorbar",width=0.25) +
geom_point(color="black",shape=21,show.legend=TRUE,size=3,stroke=1) +
facet_wrap(~set,scales="free") +
stat_compare_means(method = "t.test",comparisons=list(c("KO","WT")),hide.ns=F) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45,hjust = 1)) +
scale_y_continuous(expand = expansion(mult = c(0.2,0.2)))
您也可以手动计算 p 值，然后使用 ggsignif package 将它们放置在图上。这是更多的工作，但您可以调整多重比较的 p 值和/或指定要使用的符号，因此它可能会派上用场用于更复杂的绘图，例如
library(tidyverse)
library(ggsignif)
temp <- data.frame(sample = seq(1:83),condition = c("KO","KO","WT","WT"),set = c("di","tet","mono","di","mono"),score = c(0,-5.95940584704586,10.0932562947815,-3.16676604569923,4.46133814098881,-1.9888611720281,-7.08194974795108,3.17097628218171,-1.83986980857496,-0.843268716519414,1.40178526106758,-0.340339302553342,-3.76913603338144,4.28943971347741,-3.20197704274428,-3.54168755452774,15.414510676737,-5.85333426479177,-1.87949902971026,14.1424002410594,-2.14726619139082,-5.01378228438499,11.1131227496058,0.097013584879446,-6.55527134311774,12.2234175105232,-2.55259067519978,-6.68392512983342,15.8358484731832,-2.27825891764331,-5.40451835097939,11.2240240941934,-1.53075128507785,-4.80008896082703,15.3667539728667,-4.81370852797055,-2.69976280917806,21.9926791189896,0.61798090190648,-8.68663007652496,13.8852585926079,-0.80329005076484,-16.8459570277756,13.3500356549569,-5.59531483186873,-3.33602772657725,11.338954967882,-2.12614700145763,-3.37418493461362,11.5903340330526,-2.87224785160433,-14.7792521265679,7.7542705233175,-2.06876649679246,-1.31032740187699,17.6666627835987,-0.824420163207606,-4.82659116096503,-0.0626028094479509,-1.90431942338018,-20.3180652520285,6.83632028067972,-1.58570945276274,-2.77261532150261,2.98168865160908,-1.89922364414076,-5.80584721371712,7.13599592922731,-1.23964885854847,-5.1236580504364,14.5532078112838,-3.82017971494402,-4.39741499313133,11.7342969461195,-2.5852194454582,-3.18458897513284,12.7762556862722,1.1245622869403,-2.98430760077172,13.981837061262,-1.19532384849617))
anno_df = compare_means(score ~ condition,group.by = "set",data = temp) %>%
mutate(y_pos = c(3,24,2.5))
ggplot(temp,fill=set)) +
stat_summary(fun=mean,scales="free") +
geom_signif(data = anno_df,aes(xmin=group1,xmax=group2,annotations=p.signif,y_position = y_pos),manual = TRUE) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45,0.2)))
@ricoderks 的回答显然是更简单/更好的选择！