Question

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

我设法制作了如下所示的情节，这正是我想要的。

我现在面临的问题是，我很少或没有重要的比较;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。

请使用虹膜数据集检查此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()

产生这个情节：

上面的情节正是我想要的......但我面临的情况是没有重要的比较，或者很少。在这些情况下，很多垂直空间都是空的。

为了举例说明这些情况，我们可以取消对该行的注释：

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

所以当没有重要的比较时，我会得到这个情节：

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

我们的情况只有几个重要的比较，并获得这个情节：

所以我的问题是：

如何将垂直空间调整为重要比较的数量，因此没有垂直空间？

我可能会在step_increase或y_position内的geom_signif()中更改某些内容，因此我只为CNb[G]中的重要比较留出空间...

Answer 1

一种选择是预先计算both水平的每个组合的p值，然后仅选择重要的p值用于绘图。由于我们事先知道有多少是重要的，我们可以调整图的y范围来解释这个问题。但是，geom_signif看起来不能仅对p值注释进行内部计算（请参阅manual参数的帮助）。因此，我们不是使用ggplot的分面，而是使用lapply为每个Species创建单独的绘图，然后使用grid.arrange包中的gridExtra来布置各个绘图好像他们是分面的。

（为了回应这些评论，我想强调的是，这些图仍然是用ggplot2创建的，但是我们创建了单个图的三个面板作为三个单独的图然后将它们放在一起就好像它们已被刻面一样。）

下面的函数是针对OP中的数据框和列名进行硬编码的，但当然可以推广为采用任何数据框和列名。

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截止值的函数：

signif_plot(0.05)

signif_plot(0.01)

signif_plot(0.9)

signif_plot(0.0015)

Answer 2

你可以试试。虽然答案类似于我的回答here，但我现在添加了一个函数。

library(tidyverse)
library(ggsignif)
# 1. your data
set.seed(2)
df <- as.tbl(iris) %>% 
  mutate(treatment=rep(c("A","B"), length(iris$Species)/2)) %>% 
  gather(key, value, -Species, -treatment) %>% 
  mutate(value=rnorm(n())) %>% 
  mutate(key=factor(key, levels=unique(key))) %>% 
  mutate(both=interaction(treatment, key, sep = " "))

# 2. pairwise.wilcox.test for 1) validation and 2) to calculate the ylim
Wilcox <- df %>% 
  split(., .$Species) %>% 
  map(~tidy(pairwise.wilcox.test(.$value, .$both, p.adjust.method =  "none"))) %>% 
  map(~filter(.,.$p.value < 0.05)) %>% 
  bind_rows(.id="Species") %>% 
  mutate(padjust=p.adjust(p.value, method = "BH"))

# 3. calculate y range
Ylim <- df %>% 
  summarise(Min=round(min(value)),
            Max=round(max(value))) %>% 
  mutate(Max=Max+0.5*group_by(Wilcox, Species) %>% count() %>% with(.,max(n))) 
  %>% c()

# 4. the plot function 
foo <- function(df, Ylim, Signif=0.05){
P <- df %>% 
ggplot(aes(x=both, y=value)) + 
  geom_boxplot(aes(fill=Species)) + 
  facet_grid(~Species) +
  ylim(Ylim$Min, Ylim$Max)+
  theme(axis.text.x = element_text(angle=45, hjust=1)) +
  geom_signif(comparisons = combn(levels(df$both),2,simplify = F),
              map_signif_level = F, test = "wilcox.test" ) +
  stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
  xlab("") 
# 5. remove not significant values and add step increase
P_new <- ggplot_build(P)
P_new$data[[2]] <- P_new$data[[2]] %>% 
  filter(as.numeric(as.character(annotation)) < 0.05) %>% 
  group_by(PANEL) %>%
  mutate(index=(as.numeric(group[drop=T])-1)*0.5) %>% 
  mutate(y=y+index,
         yend=yend+index) %>% 
  select(-index) %>% 
  as.data.frame()
# the final plot  
plot(ggplot_gtable(P_new))
}

foo(df, Ylim)

尝试其他数据

set.seed(12345)
df <- as.tbl(iris) %>% 
  mutate(treatment=rep(c("A","B"), length(iris$Species)/2)) %>% 
  gather(key, value, -Species, -treatment) %>% 
  mutate(value=rnorm(n())) %>% 
  mutate(key=factor(key, levels=unique(key))) %>% 
  mutate(both=interaction(treatment, key, sep = " "))

foo(df, list(Min=-3,Max=5))

当然，您也可以将Ylim计算添加到函数中。此外，您可以更改或添加ggtitel()，ylab()并更改颜色。

ggplot2 boxplots - 如果没有重要的比较，如何避免额外的垂直空间？

2 个答案: