如何在 R 中对条形图的订单数据进行“分组”？

我正在研究生物信息学，我需要输出一个包含祖先结果的图表（条形图）。通常，这些图表是通过将人群分组在一起来绘制的。完成的方法是，您只需绘制不同假定人群（此处为 4）的 Q 分数（下面的数据）的条形图。

问题是我使用ord = tbl[order(tbl$V1,tbl$V2,tbl$V3),]对我的价值观进行排序。这样我就看到一些条形条没有聚集在正确的群体中（参见图中应与第一组聚集在一起的橙色条形）。因此，我想知道如何按颜色（代表人口）对条形进行聚类。

有办法解决这个问题吗？

barplotgeno <- function(tbl, # To plot the Q scores (ancestry). 
                        col = c("#FF3030", # nice colors
                                "#9ACD31",
                                "#1D90FF",
                                "#FF8001"), 
                        pdf = TRUE,
                        pdf.path.name = "~/Desktop/Stacked_barplot.pdf") {

  ord = tbl[order(tbl$V1,tbl$V2,tbl$V3),]

  if(pdf) {
    pdf(pdf.path.name, width = 11, height = 8.5)
  bp = barplot(t(as.matrix(ord[,1:dim(ord)[2]-1])), 
               space = c(0.2),#space = c(0),# Space between the bars
               col=col, #rainbow(4),
               xlab="Individual #", 
               ylab="Ancestry", xaxt="n",
               border=NA, main = "Stacked barplot from ADMIXTURE analysis")
  labs <- row.names(ord)
  text(cex=0.5, x=bp+1, y=-0.03, labs, xpd=TRUE, srt=90, pos=2)
    dev.off()
  } else {
    bp = barplot(t(as.matrix(ord[,1:dim(ord)[2]])), 
                 space = c(0.2),
                 col=col, 
                 xlab="Individual #", 
                 ylab="Ancestry", xaxt="n",
                 border=NA, 
                 main = "Stacked barplot from ADMIXTURE analysis")
    labs <- row.names(ord)
    text(cex=0.5, x=bp+1, y=-0.03, labs, xpd=TRUE, srt=90, pos=2)
  }
}

# Example 
barplotgeno(tbl = tbl, pdf = FALSE)

以下是数据：

tbl = structure(list(V1 = c(1e-05, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.430202, 0.99997, 
0.801974, 1e-05, 0.99997, 0.99997, 1e-05, 0.999968, 1e-05, 1e-05, 
1.3e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.99997, 1e-05, 1.8e-05, 
1e-05, 1.2e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1.1e-05, 1e-05, 0.642925, 
1e-05, 0.99997, 0.99997, 1e-05, 1e-05, 0.99997, 1e-05, 0.99997, 
0.99997, 0.287976, 1e-05, 0.99997, 0.99997, 0.99997, 1e-05, 0.533994, 
0.99997, 0.99997, 1e-05, 0.99997, 1e-05, 0.99997, 1e-05, 1e-05, 
0.99997, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 0.307669, 0.99997, 
0.604114, 0.604792, 0.29646, 0.514252, 0.99997, 0.798616, 0.516577, 
1e-05, 1e-05, 1e-05, 1e-05, 0.449886, 1e-05, 1e-05, 1e-05, 1e-05, 
0.790272, 1e-05, 0.576786, 0.776731), V2 = c(0.99997, 1e-05, 
1e-05, 1e-05, 0.99997, 0.99997, 1e-05, 1e-05, 1e-05, 0.99997, 
0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1.2e-05, 0.99997, 1e-05, 1e-05, 1e-05, 0.99997, 1e-05, 
0.99997, 0.99997, 1e-05, 1e-05, 0.528138, 0.99997, 1e-05, 1e-05, 
1e-05, 1e-05, 0.99997, 1e-05, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.712004, 0.99997, 
1e-05, 1e-05, 1e-05, 0.99997, 0.465986, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.99997, 0.99997, 
0.99997, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.05777, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.591857, 0.99997, 1e-05, 
0.99997, 0.99997, 0.99997, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05
), V3 = c(1e-05, 1e-05, 1e-05, 0.541112, 1e-05, 1e-05, 0.329922, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.198006, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.999967, 0.451508, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.749225, 
1e-05, 0.99997, 0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 0.442211, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.188, 
1e-05, 0.248756, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 1e-05, 0.395866, 0.395188, 0.293429, 0.427968, 
1e-05, 0.201364, 0.483403, 1e-05, 1e-05, 0.408123, 1e-05, 0.550094, 
1e-05, 1e-05, 1e-05, 1e-05, 0.209708, 0.533729, 0.423194, 0.223249
), V4 = c(1e-05, 1e-05, 0.99997, 0.458868, 1e-05, 1e-05, 0.670058, 
0.99997, 0.99997, 1e-05, 1e-05, 0.99997, 0.99997, 0.569778, 1e-05, 
1e-05, 0.99997, 1e-05, 1e-05, 0.99997, 1e-05, 1e-05, 0.99997, 
1e-05, 0.548472, 1e-05, 0.99997, 1e-05, 1e-05, 1e-05, 0.99997, 
0.471833, 1e-05, 0.250753, 0.99997, 1e-05, 1e-05, 1e-05, 0.999969, 
1e-05, 0.357055, 0.557769, 1e-05, 1e-05, 0.99997, 0.99997, 1e-05, 
0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 
1e-05, 1e-05, 1e-05, 0.81198, 1e-05, 0.751224, 1e-05, 0.99997, 
0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 0.692311, 
1e-05, 1e-05, 1e-05, 0.410101, 1e-05, 1e-05, 1e-05, 1e-05, 0.99997, 
0.99997, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 1e-05, 
0.466251, 1e-05, 1e-05)), .Names = c("V1", "V2", "V3", "V4"), class = "data.frame", row.names = c(NA, 
-93L))

如果您需要一个分组变量，这里是一个（按数据顺序）：

species = c("fuliginosa", "fuliginosa", "fuliginosa", "fuliginosa", "fuliginosa", 
"fuliginosa", "fuliginosa", "fuliginosa", "fuliginosa", "fortis", 
"fuliginosa", "fuliginosa", "fortis", "fortis", "fortis", "fuliginosa", 
"fuliginosa", "fortis", "scandens", "fortis", "fortis", "scandens", 
"scandens", "fuliginosa", "magnirostris", "scandens", "magnirostris", 
"fortis", "fortis", "fortis", "fortis", "fortis", "scandens", 
"fortis", "scandens", "scandens", "magnirostris", "fortis", "fortis", 
"fortis", "scandens", "magnirostris", "magnirostris", "fortis", 
"fortis", "fortis", "fortis", "magnirostris", "fortis", "magnirostris", 
"magnirostris", "magnirostris", "fortis", "fortis", "fortis", 
"magnirostris", "magnirostris", "fortis", "fortis", "fortis", 
"fortis", "fortis", "fortis", "fortis", "fortis", "fortis", "fortis", 
"magnirostris", "fortis", "fortis", "fortis", "fortis", "fortis", 
"fortis", "fortis", "fortis", "fortis", "fortis", "fuliginosa", 
"fortis", "fortis", "fortis", "fortis", "fuliginosa", "fuliginosa", 
"fuliginosa", "fuliginosa", "fortis", "fortis", "fortis", "fortis", 
"fortis", "fortis")

稍微好一点的方法：

library(tidyverse)

plot_data <- tbl %>% 
  mutate(id = row_number()) %>% 
  gather('pop', 'prob', V1:V4) %>% 
  group_by(id) %>% 
  mutate(likely_assignment = pop[which.max(prob)],
         assingment_prob = max(prob)) %>% 
  arrange(likely_assignment, desc(assingment_prob)) %>% 
  ungroup() %>% 
  mutate(id = forcats::fct_inorder(factor(id)))

用 ggplot 绘制的图：

ggplot(plot_data, aes(id, prob, fill = pop)) +
  geom_col() +
  theme_classic()

或者用面：

ggplot(plot_data, aes(id, prob, fill = pop)) +
  geom_col() +
  facet_grid(~likely_assignment, scales = 'free', space = 'free')