如何在一张图中按两个不同的组制作多个箱线图？

Question

dataset部分是这样的：

  Treatment   Status     gene1    gene2
1      Both Deceased  3.1934860 63.8697194   
2      Both Deceased  0.0000000 11.3436426   
3     Chemo Deceased  7.2186817 35.0621681   
4      Both Deceased  7.2186817 23.7185255   
5     Chemo Deceased  0.8049256 17.7083638   
6     Chemo Censored  0.8250437  0.8250437   
7     Chemo Censored  3.4136505 23.895533   
8     Radio Censored  0.9428735  4.7143673   
9      None Censored  3.3001750 10.7255686   

我想比较每种治疗的“已故”与“审查”中的每个基因表达。我现在只能做一个基因表达，是这样的：

ggboxplot(df, x="Treatment", y= "gene1", fill = "Status")

有什么方法可以将两个基因的箱线图组合在一张图中？或者有什么其他更好的方法来显示每组中死亡与审查之间这些基因的表达水平差异？

Answer 1

我们可以在base R中使用boxplot()，这里我们需要先使用reshape()来获得长格式。

boxplot(gene ~ Status + time + Treatment, 
        reshape(cbind(id=rownames(dat), dat), 4:5, sep="", direction="long"), 
        border=1:2)

但是，这会产生一个非常拥挤的情节。我们可以做单独的箱线图，例如每个治疗组使用sapply()。

par(mfrow=c(2, 2))
sapply(unique(dat$Treatment), function(x) {
  boxplot(value ~ Status + gene, 
          reshape(cbind(id=rownames(dat[dat$Treatment == x, ]), dat[dat$Treatment == x, ]), 
                  4:5, sep="", direction="long", v.names="value", timevar="gene"), 
          at=c(1:2, 4:5),
          main=x,
          border=1:2)
})

结果

数据

dat <- structure(list(Treatment = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 
3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 
4L, 4L), .Label = c("Both", "Chemo", "None", "Radio"), class = "factor"), 
    Status = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
    ), .Label = c("Censored", "Deceased"), class = "factor"), 
    gene1 = c(2.83185327992901, 5.21658677992433, 9.36719279899948, 
    1.77809421116808, 6.39453760571561, 3.08376117126782, -1.99524072673447, 
    0.380722587753265, -0.947148460332481, 1.73014054712629, 
    0.855919162512028, 0.501667581598007, 0.0638735169737497, 
    10.1712355237258, 5.34317645471502, -7.96626158445742, -0.0781613844302278, 
    5.59930916967042, -0.725717330717595, 0.492793009977729, 
    -0.546677404630108, 0.290301979542245, 2.83540215865274, 
    -1.25738031049913), gene2 = c(6.97361394841868, -6.86012827859373, 
    -0.193731972798249, -5.64669185350061, -20.6664537342379, 
    32.5477488386544, 12.6210452154023, 6.56845245925654, 13.5491140544121, 
    -2.9113829554538, 2.90958200298303, -6.56806056188421, 50.2577234864485, 
    17.0734922804668, 49.0769939658538, -2.0186433516603, 32.3823429023035, 
    17.7654319738005, 12.2884241568455, 21.7600566866782, 19.68978862329, 
    -12.6277420840716, 27.555120882401, 17.5164450232983)), row.names = c(3L, 
23L, 13L, 44L, 34L, 50L, 90L, 67L, 62L, 100L, 95L, 96L, 132L, 
144L, 124L, 174L, 171L, 168L, 196L, 205L, 207L, 233L, 229L, 212L
), class = "data.frame")

Answer 2

使用来自 jay.sf 的数据，您可以尝试“ggplot”。我正在使用tidyverse，但这不是必需的。

library(tidyverse)
dat %>% 
  as_tibble() %>%
  gather(gene, mRNA, -Treatment, -Status) %>% 
  ggplot(aes(Status, mRNA, fill =gene)) + 
   geom_boxplot() +
   facet_wrap(~Treatment, ncol = 2, scales = "free_y")

使用facet_grid，您可以自动添加显着性水平

dat %>% 
  as_tibble() %>%
  gather(gene, mRNA, -Treatment, -Status) %>% 
  ggplot(aes(gene, mRNA, fill =gene)) + 
   geom_boxplot(show.legend = F) +
   ggbeeswarm::geom_beeswarm(show.legend = F) +
   ggsignif::geom_signif(comparisons = list(c("gene1", "gene2"))) +
   facet_grid(Status~Treatment, scales = "free_y")