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我已经对许多用树进行层次结构的属进行了统计测试，因此我对树中的每个属都有一个 p 值。

我想在面板图中可视化树和 p 值，因为可以使用ggtree。

包和树数据：

library(ape)
# source("https://bioconductor.org/biocLite.R"); biocLite("ggtree")
library(ggtree)

tree <- structure(list(
  edge = structure(c(102L, 103L, 104L, 105L, 106L, 
                     107L, 103L, 108L, 109L, 110L, 111L, 111L, 109L, 112L, 113L, 109L, 
                     114L, 115L, 115L, 115L, 115L, 115L, 115L, 114L, 116L, 109L, 117L, 
                     118L, 108L, 119L, 120L, 121L, 119L, 122L, 123L, 123L, 108L, 124L, 
                     125L, 126L, 125L, 127L, 127L, 108L, 128L, 129L, 130L, 130L, 129L, 
                     131L, 103L, 132L, 133L, 134L, 135L, 134L, 136L, 136L, 134L, 137L, 
                     137L, 134L, 138L, 138L, 134L, 139L, 139L, 134L, 140L, 134L, 141L, 
                     141L, 103L, 142L, 143L, 144L, 145L, 103L, 146L, 147L, 148L, 149L, 
                     146L, 150L, 151L, 152L, 103L, 153L, 154L, 155L, 156L, 153L, 157L, 
                     158L, 159L, 159L, 159L, 159L, 159L, 157L, 160L, 161L, 160L, 162L, 
                     103L, 163L, 164L, 165L, 166L, 165L, 167L, 167L, 165L, 168L, 168L, 
                     165L, 169L, 164L, 170L, 171L, 170L, 172L, 163L, 173L, 174L, 175L, 
                     175L, 175L, 173L, 176L, 177L, 177L, 177L, 173L, 178L, 179L, 179L, 
                     163L, 180L, 181L, 182L, 181L, 183L, 183L, 183L, 181L, 184L, 184L, 
                     184L, 184L, 184L, 184L, 184L, 184L, 184L, 184L, 181L, 185L, 185L, 
                     185L, 181L, 186L, 181L, 187L, 187L, 181L, 188L, 188L, 188L, 188L, 
                     188L, 163L, 189L, 190L, 191L, 191L, 163L, 192L, 193L, 194L, 194L, 
                     194L, 194L, 194L, 194L, 194L, 103L, 195L, 196L, 197L, 198L, 196L, 
                     199L, 200L, 103L, 201L, 202L, 203L, 204L, 204L, 102L, 205L, 206L, 
                     207L, 208L, 209L, 206L, 210L, 211L, 212L, 206L, 213L, 214L, 215L, 
                     103L, 104L, 105L, 106L, 107L, 1L, 108L, 109L, 110L, 111L, 2L, 
                     3L, 112L, 113L, 4L, 114L, 115L, 5L, 6L, 7L, 8L, 9L, 10L, 116L, 
                     11L, 117L, 118L, 12L, 119L, 120L, 121L, 13L, 122L, 123L, 14L, 
                     15L, 124L, 125L, 126L, 16L, 127L, 17L, 18L, 128L, 129L, 130L, 
                     19L, 20L, 131L, 21L, 132L, 133L, 134L, 135L, 22L, 136L, 23L, 
                     24L, 137L, 25L, 26L, 138L, 27L, 28L, 139L, 29L, 30L, 140L, 31L, 
                     141L, 32L, 33L, 142L, 143L, 144L, 145L, 34L, 146L, 147L, 148L, 
                     149L, 35L, 150L, 151L, 152L, 36L, 153L, 154L, 155L, 156L, 37L, 
                     157L, 158L, 159L, 38L, 39L, 40L, 41L, 42L, 160L, 161L, 43L, 162L, 
                     44L, 163L, 164L, 165L, 166L, 45L, 167L, 46L, 47L, 168L, 48L, 
                     49L, 169L, 50L, 170L, 171L, 51L, 172L, 52L, 173L, 174L, 175L, 
                     53L, 54L, 55L, 176L, 177L, 56L, 57L, 58L, 178L, 179L, 59L, 60L, 
                     180L, 181L, 182L, 61L, 183L, 62L, 63L, 64L, 184L, 65L, 66L, 67L, 
                     68L, 69L, 70L, 71L, 72L, 73L, 74L, 185L, 75L, 76L, 77L, 186L, 
                     78L, 187L, 79L, 80L, 188L, 81L, 82L, 83L, 84L, 85L, 189L, 190L, 
                     191L, 86L, 87L, 192L, 193L, 194L, 88L, 89L, 90L, 91L, 92L, 93L, 
                     94L, 195L, 196L, 197L, 198L, 95L, 199L, 200L, 96L, 201L, 202L, 
                     203L, 204L, 97L, 98L, 205L, 206L, 207L, 208L, 209L, 99L, 210L, 
                     211L, 212L, 100L, 213L, 214L, 215L, 101L), .Dim = c(214L, 2L)), 
  edge.length = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1), 
  Nnode = 114L, 
  tip.label = c("Brachyspira", 
                "Haemophilus", "Aggregatibacter", "Acinetobacter", "Klebsiella", 
                "Salmonella", "Escherichia", "Enterobacter", "Shigella", 
                "Citrobacter", "Hafnia", "Succinatimonas", "Corallococcus", 
                "Bilophila", "Desulfovibrio", "Azospirillum", "Acidiphilium", 
                "Acetobacter", "Sutterella", "Parasutterella", "Oxalobacter", 
                "Porphyromonas", "Paraprevotella", "Prevotella", "Alistipes", 
                "Rikenella", "Tannerella", "Parabacteroides", "Odoribacter", 
                "Butyricimonas", "Bacteroides", "Coprobacter", "Barnesiella", 
                "Fusobacterium", "Coraliomargarita", "Akkermansia", "Bifidobacterium", 
                "Gordonibacter", "Eggerthella", "Cryptobacterium", "Adlercreutzia", 
                "Enterorhabdus", "Collinsella", "Olsenella", "Lactobacillus", 
                "Weissella", "Oenococcus", "Lactococcus", "Streptococcus", 
                "Enterococcus", "Staphylococcus", "Bacillus", "Dialister", 
                "Veillonella", "Megasphaera", "Megamonas", "Mitsuokella", 
                "Selenomonas", "Phascolarctobacterium", "Acidaminococcus", 
                "Oscillibacter", "Intestinibacter", "Peptoclostridium", "Peptostreptococcus", 
                "Dorea", "Roseburia", "Anaerostipes", "Tyzzerella", "Coprococcus", 
                "Blautia", "Butyrivibrio", "Marvinbryantia", "Lachnoclostridium", 
                "Oribacterium", "Flavonifractor", "Intestinimonas", "Pseudoflavonifractor", 
                "Eubacterium", "Clostridium", "Butyricicoccus", "Faecalibacterium", 
                "Ruminococcus", "Anaerotruncus", "Subdoligranulum", "Ruminiclostridium", 
                "Parvimonas", "Peptoniphilus", "Catenibacterium", "Solobacterium", 
                "Coprobacillus", "Holdemania", "Erysipelatoclostridium", 
                "Turicibacter", "Stoquefichus", "Mycoplasma", "Acholeplasma", 
                "Pyramidobacter", "Synergistes", "Methanobrevibacter", "Methanomethylophilus", 
                "Methanoculleus"), root.edge = 1), 
  .Names = c("edge", "edge.length", 
             "Nnode", "tip.label", "root.edge"), class = "phylo", order = "cladewise"
)

代码： 

df <- data.frame(id = tree$tip.label, p = runif(length(tree$tip.label)))

p1 <- ggtree(tree) +
  geom_tiplab()

facet_plot(p1, panel = "p-value", data = df, geom = geom_point, aes(x = p))

在此处输入图像描述

但是在这里，属的名称被截断了，所以我修改了xlim参数以完整地查看它们。

p2 <- 
  ggtree(tree) + 
  geom_tiplab() + 
  xlim(c(0,7))

facet_plot(p2, panel = "p-value", data = df, geom = geom_point, aes(x = p))

在此处输入图像描述

有用！但是，xlim传播到第二个面板......我该如何解决这个问题？

我尝试添加xlim(0:1)或xlim = 0:1输入，facet_plot()但这不起作用...

在 F. Privé 回答后编辑：

我需要将标签保留在分支的右侧，因为我必须在它们上添加一些标签/统计信息。我希望它们左对齐。

4

2 回答 2

2

ggtreexlim_expand专门为此目的提供功能。

您需要指定限制和要应用它的面板。在您的情况下，您希望将其应用于Tree面板：

p1 <- ggtree(tree) +
  geom_tiplab(size = 2) + 
  xlim_expand(c(0,15), panel = "Tree")

facet_plot(p1, panel = "p-value", data = df, geom = geom_point, aes(x = p))

示例图

我夸大了示例的限制，但您可以根据自己的喜好进行调整。

于 2018-04-08T09:58:02.693 回答
1

您可以使用hjustandoffset参数（尽管我不太了解它们）：

p2 <- 
  ggtree(tree) + 
  geom_tiplab(hjust = 1, offset = 8)

facet_plot(p2, panel = "p-value", data = df, geom = geom_point, aes(x = p))
于 2018-02-22T14:14:12.100 回答