r - ggplot2 boxplots - 当没有显着比较时如何避免额外的垂直空间？

Question

在关于如何制作具有方面和显着性水平的箱线图的许多问题之后，特别是 this和 this ，我还有一个小问题。

我设法生成了如下所示的图，这正是我想要的。

我现在面临的问题是当我很少或没有显着的比较时；在这些情况下，专用于显示显着性水平的括号的整个空间仍然保留，但我想摆脱它。

请使用 iris 数据集检查此 MWE:

library(reshape2)
library(ggplot2)
data(iris)
iris$treatment <- rep(c("A","B"), length(iris$Species)/2)
mydf <- melt(iris, measure.vars=names(iris)[1:4])
mydf$treatment <- as.factor(mydf$treatment)
mydf$variable <- factor(mydf$variable, levels=sort(levels(mydf$variable)))
mydf$both <- factor(paste(mydf$treatment, mydf$variable), levels=(unique(paste(mydf$treatment, mydf$variable))))
a <- combn(levels(mydf$both), 2, simplify = FALSE)#this 6 times, for each lipid class
b <- levels(mydf$Species)
CNb <- relist(
    paste(unlist(a), rep(b, each=sum(lengths(a)))), 
    rep.int(a, length(b))
)
CNb
CNb2 <- data.frame(matrix(unlist(CNb), ncol=2, byrow=T))
CNb2
#new p.values
pv.df <- data.frame()
for (gr in unique(mydf$Species)){
    for (i in 1:length(a)){
        tis <- a[[i]] #variable pair to test
        as <- subset(mydf, Species==gr & both %in% tis)
        pv <- wilcox.test(value ~ both, data=as)$p.value
        ddd <- data.table(as)
        asm <- as.data.frame(ddd[, list(value=mean(value)), by=list(both=both)])
        asm2 <- dcast(asm, .~both, value.var="value")[,-1]
        pf <- data.frame(group1=paste(tis[1], gr), group2=paste(tis[2], gr), mean.group1=asm2[,1], mean.group2=asm2[,2], log.FC.1over2=log2(asm2[,1]/asm2[,2]), p.value=pv)
        pv.df <- rbind(pv.df, pf)
    }
}
pv.df$p.adjust <- p.adjust(pv.df$p.value, method="BH")
colnames(CNb2) <- colnames(pv.df)[1:2]
# merge with the CN list
pv.final <- merge(CNb2, pv.df, by.x = c("group1", "group2"), by.y = c("group1", "group2"))
# fix ordering
pv.final <- pv.final[match(paste(CNb2$group1, CNb2$group2), paste(pv.final$group1, pv.final$group2)),]
# set signif level
pv.final$map.signif <- ifelse(pv.final$p.adjust > 0.05, "", ifelse(pv.final$p.adjust > 0.01,"*", "**"))
# subset
G <- pv.final$p.adjust <= 0.05
CNb[G]
P <- ggplot(mydf,aes(x=both, y=value)) +
    geom_boxplot(aes(fill=Species)) +
    facet_grid(~Species, scales="free", space="free_x") +
    theme(axis.text.x = element_text(angle=45, hjust=1)) +
    geom_signif(test="wilcox.test", comparisons = combn(levels(mydf$both),2, simplify = F),
              map_signif_level = F,            
              vjust=0.5,
              textsize=4,
              size=0.5,
              step_increase = 0.06)
P2 <- ggplot_build(P)

#pv.final$map.signif <- "" #UNCOMMENT THIS LINE TO MOCK A CASE WHERE THERE ARE NO SIGNIFICANT COMPARISONS
#pv.final$map.signif[c(1:42,44:80,82:84)] <- "" #UNCOMMENT THIS LINE TO MOCK A CASE WHERE THERE ARE JUST A COUPLE OF SIGNIFICANT COMPARISONS

P2$data[[2]]$annotation <- rep(pv.final$map.signif, each=3)
# remove non significants
P2$data[[2]] <- P2$data[[2]][P2$data[[2]]$annotation != "",]
# and the final plot
png(filename="test.png", height=800, width=800)
  plot(ggplot_gtable(P2))
dev.off()

这会产生这个图:

上面的图正是我想要的......但我面临着没有显着比较或很少比较的情况。在这些情况下，大量垂直空间被留空。

为了举例说明这些场景，我们可以取消注释该行:

pv.final$map.signif <- "" #UNCOMMENT THIS LINE TO MOCK A CASE WHERE THERE ARE NO SIGNIFICANT COMPARISONS

因此，当没有显着的比较时，我得到这个图:

如果我们取消注释另一行:

pv.final$map.signif[c(1:42,44:80,82:84)] <- "" #UNCOMMENT THIS LINE TO MOCK A CASE WHERE THERE ARE JUST A COUPLE OF SIGNIFICANT COMPARISONS

我们所处的情况只有几个显着的比较，并获得此图:

所以我的问题是:

如何将垂直空间调整为重要比较的数量，从而不留任何垂直空间？

我可能可以在geom_signif()内的step_increase或y_position中更改一些内容，所以我只为重要的比较留出空间在CNb[G]...

Answer 1

一种选择是预先计算 both 的每个组合的 p 值水平，然后仅选择重要的水平进行绘图。由于我们事先知道有多少个是重要的，因此我们可以调整图的 y 范围来解释这一点。然而，它看起来并不像 geom_signif只能对 p 值注释进行面内计算(请参阅 manual 参数的帮助)。因此，我们不使用 ggplot 的分面，而是使用 lapply为每个 Species 创建一个单独的图然后使用 grid.arrange来自gridExtra包来布置各个图，就好像它们是多面的一样。

(为了回应评论，我想强调这些图仍然是用 ggplot2 创建的，但是我们将单个图的三个方面面板创建为三个独立的图然后将它们放在一起，就像它们被刻面一样。)

下面的函数是针对OP中的数据框和列名称进行硬编码的，但当然可以概括为采用任何数据框和列名称。

library(gridExtra)
library(tidyverse)

# Change data to reduce number of statistically significant differences
set.seed(2)
df = mydf %>% mutate(value=rnorm(nrow(mydf)))

# Function to generate and lay out the plots
signif_plot = function(signif.cutoff=0.05, height.factor=0.23) {

  # Get full range of y-values
  y_rng = range(df$value)

  # Generate a list of three plots, one for each Species (these are the facets)
  plot_list = lapply(split(df, df$Species), function(d) {

    # Get pairs of x-values for current facet
    pairs = combn(sort(as.character(unique(d$both))), 2, simplify=FALSE)

    # Run wilcox test on every pair
    w.tst =  pairs %>% 
      map_df(function(lv) { 
        p.value = wilcox.test(d$value[d$both==lv[1]], d$value[d$both==lv[2]])$p.value
        data.frame(levs=paste(lv, collapse=" "), p.value)
      })

    # Record number of significant p.values. We'll use this later to adjust the top of the
    # y-range of the plots
    num_signif = sum(w.tst$p.value <= signif.cutoff)

    # Plot significance levels only for combinations with p <= signif.cutoff
    p = ggplot(d, aes(x=both, y=value)) +
      geom_boxplot() +
      facet_grid(~Species, scales="free", space="free_x") +
      geom_signif(test="wilcox.test", comparisons = pairs[which(w.tst$p.value <= signif.cutoff)],
                  map_signif_level = F,            
                  vjust=0,
                  textsize=3,
                  size=0.5,
                  step_increase = 0.08) +
      theme_bw() +
      theme(axis.title=element_blank(),
            axis.text.x = element_text(angle=45, hjust=1))

    # Return the plot and the number of significant p-values
    return(list(num_signif, p))
  })

  # Get the highest number of significant p-values across all three "facets"
  max_signif = max(sapply(plot_list, function(x) x[[1]]))

  # Lay out the three plots as facets (one for each Species), but adjust so that y-range is same
  # for each facet. Top of y-range is adjusted using max_signif.
  grid.arrange(grobs=lapply(plot_list, function(x) x[[2]] + 
                              scale_y_continuous(limits=c(y_rng[1], y_rng[2] + height.factor*max_signif))), 
               ncol=3, left="Value")
}

现在运行具有四个不同显着性截止值的函数:

signif_plot(0.05)

signif_plot(0.01)

signif_plot(0.9)

signif_plot(0.0015)