## Warning: package 'ape' was built under R version 3.4.4Manipulating tree object is frustrated with the fragmented functions available for working with phylo object, not to mention linking external data to the phylogeny structure. Using tidy data principles can make phylogenetic tree manipulation tasks easier and consistent with tools already in wide use, including dplyr, tidyr, ggplot2 and ggtree.
Convert tree object to tidy data frame and vice versa
phylo object
The phylo class defined in ape is fundamental for phylogenetic analysis in R. Most of the R packages in this field rely extensively on phylo object. The tidytree package provides as_data_frame method to convert the phylo object to tidy data frame, a tbl_tree object.
library(ape)
set.seed(2017)
tree <- rtree(4)
tree##
## Phylogenetic tree with 4 tips and 3 internal nodes.
##
## Tip labels:
## [1] "t2" "t1" "t4" "t3"
##
## Rooted; includes branch lengths.x <- as_data_frame(tree)
x## # A tibble: 7 x 4
## parent node branch.length label
## <int> <int> <dbl> <chr>
## 1 7 1 0.472 t2
## 2 7 2 0.274 t1
## 3 6 3 0.674 t4
## 4 5 4 0.00202 t3
## 5 5 5 NA <NA>
## 6 5 6 0.0393 <NA>
## 7 6 7 0.435 <NA>The tbl_tree object can be converted back to a phylo object.
as.phylo(x)## Warning: package 'bindrcpp' was built under R version 3.4.4##
## Phylogenetic tree with 4 tips and 3 internal nodes.
##
## Tip labels:
## [1] "t2" "t1" "t4" "t3"
##
## Rooted; includes branch lengths.Using tbl_tree object makes tree and data manipulation more effective and easier. For example, we can link evolutionary trait to phylogeny using the verbs full_join
d <- tibble(label = paste0('t', 1:4),
trait = rnorm(4))
y <- full_join(x, d, by = 'label')treedata object
The tidytree package defines a treedata class to store phylogenetic tree with associated data. After mapping external data to the tree structure, the tbl_tree object can be converted to a treedata object.
as.treedata(y)## 'treedata' S4 object'.
##
## ...@ phylo:
## Phylogenetic tree with 4 tips and 3 internal nodes.
##
## Tip labels:
## [1] "t2" "t1" "t4" "t3"
##
## Rooted; includes branch lengths.
##
## with the following features available:
## 'trait'.The treedata class is also used in treeio package to store evolutionary evidences inferred by commonly used software (BEAST, EPA, HYPHY, MrBayes, PAML, PHYLODOG, pplacer, r8s, RAxML and RevBayes).
The tidytree package also provides as_data_frame to convert treedata object to a tidy data frame. The phylogentic tree structure and the evolutionary inferences were stored in the tbl_tree object, making it consistent and easier for manipulating evolutionary statistics inferred by different software as well as linking external data to the same tree structure.
y %>% as.treedata %>% as_data_frame## # A tibble: 7 x 5
## parent node branch.length label trait
## <int> <int> <dbl> <chr> <dbl>
## 1 7 1 0.472 t2 -0.00152
## 2 7 2 0.274 t1 -1.96
## 3 6 3 0.674 t4 1.56
## 4 5 4 0.00202 t3 -0.265
## 5 5 5 NA <NA> NA
## 6 5 6 0.0393 <NA> NA
## 7 6 7 0.435 <NA> NAGrouping taxa
tidytree implemented groupOTU and groupClade for adding taxa grouping information to the input tbl_tree object. These grouping information can be used directly in tree visualization (e.g. coloring tree based on grouping) with ggtree.
groupClade
The groupClade method accepts an internal node or a vector of internal nodes to add grouping information of clade/clades.
nwk <- '(((((((A:4,B:4):6,C:5):8,D:6):3,E:21):10,((F:4,G:12):14,H:8):13):13,((I:5,J:2):30,(K:11,L:11):2):17):4,M:56);'
tree <- read.tree(text=nwk)
groupClade(as_data_frame(tree), c(17, 21))## # A tibble: 25 x 5
## parent node branch.length label group
## <int> <int> <dbl> <chr> <fct>
## 1 20 1 4. A 1
## 2 20 2 4. B 1
## 3 19 3 5. C 1
## 4 18 4 6. D 1
## 5 17 5 21. E 1
## 6 22 6 4. F 2
## 7 22 7 12. G 2
## 8 21 8 8. H 2
## 9 24 9 5. I 0
## 10 24 10 2. J 0
## # ... with 15 more rowsgroupOTU
## the input nodes can be node ID or label
groupOTU(x, c('t1', 't4'), group_name = "fake_group")## # A tibble: 7 x 5
## parent node branch.length label fake_group
## <int> <int> <dbl> <chr> <fct>
## 1 7 1 0.472 t2 0
## 2 7 2 0.274 t1 1
## 3 6 3 0.674 t4 1
## 4 5 4 0.00202 t3 0
## 5 5 5 NA <NA> 0
## 6 5 6 0.0393 <NA> 1
## 7 6 7 0.435 <NA> 1The groupOTU will trace back from input nodes to most recent common ancestor. In this example, nodes 2, 3, 7 and 6 (2 (t1) -> 7 -> 6 and 3 (t4) -> 6) are grouping together.
Related OTUs are grouping together and they are not necessarily within a clade. They can be monophyletic (clade), polyphyletic or paraphyletic.
cls <- list(c1=c("A", "B", "C", "D", "E"),
c2=c("F", "G", "H"),
c3=c("L", "K", "I", "J"),
c4="M")
as_data_frame(tree) %>% groupOTU(cls)## # A tibble: 25 x 5
## parent node branch.length label group
## <int> <int> <dbl> <chr> <fct>
## 1 20 1 4. A c1
## 2 20 2 4. B c1
## 3 19 3 5. C c1
## 4 18 4 6. D c1
## 5 17 5 21. E c1
## 6 22 6 4. F c2
## 7 22 7 12. G c2
## 8 21 8 8. H c2
## 9 24 9 5. I c3
## 10 24 10 2. J c3
## # ... with 15 more rowsIf there are conflicts when tracing back to mrca, user can set overlap parameter to “origin” (the first one counts), “overwrite” (default, the last one counts) or “abandon” (un-selected for grouping), see also discussion here.