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rotl/vignettes/data_mashups.Rmd.orig 0000644 0001762 0000144 00000020520 14442034113 017311 0 ustar ligges users ---
title: "Connecting data to Open Tree trees"
author: "David Winter"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
css: vignette.css
vignette: >
%\VignetteIndexEntry{Connecting data to Open Tree trees}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.path = "fig-vignettes-")
```
## Combining data from OToL and other sources.
One of the major goals of `rotl` is to help users combine data from other
sources with the phylogenetic trees in the Open Tree database. This examples
document describes some of the ways in which a user might connect data to trees
from Open Tree.
## Get Open Tree IDs to match your data.
Let's say you have a dataset where each row represents a measurement taken from
one species, and your goal is to put these measurements in some phylogenetic
context. Here's a small example: the best estimate of the mutation rate for a
set of unicellular Eukaryotes along with some other property of those species
which might explain the mutation rate:
```{r, data}
csv_path <- system.file("extdata", "protist_mutation_rates.csv", package = "rotl")
mu <- read.csv(csv_path, stringsAsFactors = FALSE)
mu
```
If we want to get a tree for these species we need to start by finding the
unique ID for each of these species in the Open Tree database. We can use the
Taxonomic Name Resolution Service (`tnrs`) functions to do this. Before we do
that we should see if any of the taxonomic contexts, which can be used to narrow
a search and avoid conflicts between different codes, apply to our group of species:
```{r, context}
library(rotl)
tnrs_contexts()
```
Hmm, none of those groups contain all of our species. In this case we can
search using the `All life` context and the function `tnrs_match_names`:
```{r, match}
taxon_search <- tnrs_match_names(names = mu$species, context_name = "All life")
knitr::kable(taxon_search)
```
Good, all of our species are known to Open Tree. Note, though, that one of the names
is a synonym. _Saccharomyces pombe_ is older name for what is now called
_Schizosaccharomyces pombe_. As the name suggests, the Taxonomic Name
Resolution Service is designed to deal with these problems (and similar ones
like misspellings), but it is always a good idea to check the results of
`tnrs_match_names` closely to ensure the results are what you expect.
In this case we have a good ID for each of our species so we can move on. Before
we do that, let's ensure we can match up our original data to the Open Tree
names and IDs by adding them to our `data.frame`:
```{r, munge}
mu$ott_name <- unique_name(taxon_search)
mu$ott_id <- taxon_search$ott_id
```
## Find a tree with your taxa
Now let's find a tree. There are two possible options here: we can search for
published studies that include our taxa or we can use the 'synthetic tree' from
Open Tree. We can try both approaches.
### Published trees
Before we can search for published studies or trees, we should check out the
list of properties we can use to perform such searches:
```{r, properties}
studies_properties()
```
We have `ottIds` for our taxa, so let's use those IDs to search for trees that
contain them. Starting with our first species _Tetrahymena thermophila_ we can
use `studies_find_trees` to do this search.
```{r taxon_count}
studies_find_trees(property = "ot:ottId", value = as.character(ott_id(taxon_search)[1]))
```
Well... that's not very promising. We can repeat that process for all of the IDs
to see if the other species are better represented.
```{r, all_taxa_count}
hits <- lapply(mu$ott_id, studies_find_trees, property = "ot:ottId", detailed = FALSE)
sapply(hits, function(x) sum(x[["n_matched_trees"]]))
```
OK, most of our species are not in any of the published trees available. You can
help fix this sort of problem by [making sure you submit your published trees to
Open Tree](https://tree.opentreeoflife.org/curator).
### A part of the synthesis tree
Thankfully, we can still use the complete Tree of Life made from the
combined results of all of the published trees and taxonomies that go into Open
Tree. The function `tol_induced_subtree` will fetch a tree relating a set of IDs.
Using the default arguments you can get a tree object into your R session:
```{r subtree, fig.width=7, fig.height=4}
ott_in_tree <- ott_id(taxon_search)[is_in_tree(ott_id(taxon_search))]
tr <- tol_induced_subtree(ott_ids = ott_in_tree)
plot(tr)
```
### Connect your data to the tips of your tree
Now we have a tree for of our species, how can we use the tree and the data
together?
The package `phylobase` provide an object class called `phylo4d`, which is
designed to represent a phylogeny and data associated with its tips. In order to
get our tree and data into one of these objects we have to make sure the labels
in the tree and in our data match exactly. That's not quite the case at the
moment (tree labels have underscores and IDs appended):
```{r, match_names}
mu$ott_name[1]
tr$tip.label[4]
```
`rotl` provides a convenience function `strip_ott_ids` to deal with these.
```{r, sub}
tr$tip.label <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label %in% mu$ott_name
```
Ok, now the tips are together we can make a new dataset. The `phylo4d()`
functions matches tip labels to the row names of a `data.frame`, so let's make
a new dataset that contains just the relevant data and has row names to match
the tree
```{r phylobase}
library(phylobase)
mu_numeric <- mu[, c("mu", "pop.size", "genome.size")]
rownames(mu_numeric) <- mu$ott_name
tree_data <- phylo4d(tr, mu_numeric)
```
And now we can plot the data and the tree together
```{r, fig.width=7, fig.height=5}
plot(tree_data)
```
## Find external data associated with studies, trees and taxa from Open Tree
In the above example we looked for a tree that related species in another dataset.
Now we will go the other way, and try to find data associated with Open Tree records
in other databases.
### Get external data from a study
Let's imagine you were interested in extending or reproducing the results of a
published study. If that study is included in Open Tree you can find it via
`studies_find_studies` or `studies_find_trees` and retrieve the published trees
with `get_study`. `rotl` will also help you find external. The function
`study_external_IDs` retrieves the DOI for a given study, and uses that to
gather some more data:
```{r}
extra_data <- try(study_external_IDs("pg_1980"), silent = TRUE)
if (!inherits(extra_data, "try-error")) {
extra_data
}
```
Here the returned object contains an `external_data_url` (in this case a link to
the study in Treebase), a pubmed ID for the paper and a vector IDs for the
NCBI's nucleotide database. The packages `treebase` and `rentrez` provide
functions to make use of these IDs within R.
As an example, let's use `rentrez` to download the first two DNA seqences and
print them.
```{r}
library(rentrez)
seqs <- try(entrez_fetch(db = "nucleotide", id = extra_data$nucleotide_ids[1:2], rettype = "fasta"), silent = TRUE)
if (inherits(seqs, "try-error")) {
cat("NCBI temporarily down.")
} else {
cat(seqs)
}
```
You could further process these sequences in R with the function `read.dna` from
`ape` or save them to disk by specifying a file name with `cat`.
### Find a OTT taxon in another taxonomic database
It is also possible map an Open Tree taxon to a record in another taxonomic
database. For instance, if we wanted to search for data about one of the tips of
the sub-tree we fetched in the example above we could do so using
`taxon_external_IDs`:
```{r}
Tt_ids <- taxon_external_IDs(mu$ott_id[2])
Tt_ids
```
A user could then use `rgbif` to find locality records using the gbif ID or
`rentrez` to get genetic or bibliometric data about from the NCBI's databases.
## What next
The demonstration gets you to the point of visualizing your data in a
phylogenetic context. But there's a lot more you do with this sort of data in R.
For instance, you could use packages like `ape`, `caper`, `phytools` and
`mcmcGLMM` to perform phylogenetic comparative analyses of your data. You could
gather more data on your species using packages that connect to
trait databases like `rfishbase`, `AntWeb` or `rnpn` which provides data from
the US National Phenology Network. You could also use `rentrez` to find genetic
data for each of your species, and use that data to generate branch lengths for
the phylogeny.
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title: "How to use rotl?"
author: "François Michonneau"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
css: vignette.css
vignette: >
%\VignetteIndexEntry{How to use rotl?}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.path = "fig-vignettes-")
```
`rotl` provides an interface to the Open Tree of Life (OTL) API and allows users
to query the API, retrieve parts of the Tree of Life and integrate these parts
with other R packages.
The OTL API provides services to access:
* the **Tree of Life** a.k.a. TOL (the synthetic tree): a single draft tree that is
a combination of **the OTL taxonomy** and the **source trees** (studies)
* the **Taxonomic name resolution services** a.k.a. TNRS: the methods for
resolving taxonomic names to the internal identifiers used by the TOL and the
GOL (the `ott ids`).
* the **Taxonomy** a.k.a. OTT (for Open Tree Taxonomy): which represents the
synthesis of the different taxonomies used as a backbone of the TOL when no
studies are available.
* the **Studies** containing the source trees used to build the TOL, and
extracted from the scientific literature.
In `rotl`, each of these services correspond to functions with different
prefixes:
| Service | `rotl` prefix |
|---------------|---------------|
| Tree of Life | `tol_` |
| TNRS | `tnrs_` |
| Taxonomy | `taxonomy_` |
| Studies | `studies_` |
`rotl` also provides a few other functions and methods that can be used to
extract relevant information from the objects returned by these functions.
## Demonstration of a basic workflow
The most common use for `rotl` is probably to start from a list of species and
get the relevant parts of the tree for these species. This is a two step
process:
1. the species names need to be matched to their `ott_id` (the Open Tree
Taxonomy identifiers) using the Taxonomic name resolution services (TNRS)
1. these `ott_id` will then be used to retrieve the relevant parts of the Tree
of Life.
### Step 1: Matching taxonomy to the `ott_id`
Let's start by doing a search on a diverse group of taxa: a tree frog (genus
_Hyla_), a fish (genus _Salmo_), a sea urchin (genus _Diadema_), and a nautilus
(genus _Nautilus_).
```{r}
library(rotl)
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
```
It's always a good idea to check that the resolved names match what you
intended:
`r knitr::kable(resolved_names)`
The column `unique_name` sometimes indicates the higher taxonomic level
associated with the name. The column `number_matches` indicates the number of
`ott_id` that corresponds to a given name. In this example, our search on
_Diadema_ returns 2 matches, and the one returned by default is indeed the sea
urchin that we want for our query. The argument `context_name` allows you to
limit the taxonomic scope of your search. _Diadema_ is also the genus name of a
fungus. To ensure that our search is limited to animal names, we could do:
```{r}
resolved_names <- tnrs_match_names(taxa, context_name = "Animals")
```
If you are trying to build a tree with deeply divergent taxa that the argument
`context_name` cannot fix, see "How to change the ott ids assigned to my taxa?"
in the FAQ below.
### Step 2: Getting the tree corresponding to our taxa
Now that we have the correct `ott_id` for our taxa, we can ask for the tree
using the `tol_induced_subtree()` function. By default, the object returned by
`tol_induced_subtree` is a phylo object (from the
[ape](https://cran.r-project.org/package=ape) package), so we can plot it
directly.
```{r, fig.width=7, fig.height=4}
my_tree <- tol_induced_subtree(ott_ids = resolved_names$ott_id)
plot(my_tree, no.margin = TRUE)
```
## FAQ
### How to change the ott ids assigned to my taxa?
If you realize that `tnrs_match_names` assigns the incorrect taxonomic group to
your name (e.g., because of synonymy) and changing the `context_name` does not
help, you can use the function `inspect`. This function takes the object
resulting from `tnrs_match_names()`, and either the row number, the taxon name
(you used in your search in lowercase), or the `ott_id` returned by the initial
query.
To illustrate this, let's re-use the previous query but this time pretending that
we are interested in the fungus _Diadema_ and not the sea urchin:
```{r}
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
resolved_names
inspect(resolved_names, taxon_name = "diadema")
```
In our case, we want the second row in this data frame to replace the
information that initially matched for _Diadema_. We can now use the `update()`
function, to change to the correct taxa (the fungus not the sea urchin):
```{r}
resolved_names <- update(resolved_names,
taxon_name = "diadema",
new_row_number = 2
)
## we could also have used the ott_id to replace this taxon:
## resolved_names <- update(resolved_names, taxon_name = "diadema",
## new_ott_id = 4930522)
```
And now our `resolved_names` data frame includes the taxon we want:
`r knitr::kable(resolved_names)`
### How do I know that the taxa I'm asking for is the correct one?
The function `taxonomy_taxon_info()` takes `ott_ids` as arguments and returns
taxonomic information about the taxa. This output can be passed to some helpers
functions to extract the relevant information. Let's illustrate this with our
_Diadema_ example
```{r}
diadema_info <- taxonomy_taxon_info(631176)
tax_rank(diadema_info)
synonyms(diadema_info)
tax_name(diadema_info)
```
In some cases, it might also be useful to investigate the taxonomic tree
descending from an `ott_id` to check that it's the correct taxon and to
determine the species included in the Open Tree Taxonomy:
```{r}
diadema_tax_tree <- taxonomy_subtree(631176)
diadema_tax_tree
```
By default, this function return all taxa (including self, and internal)
descending from this `ott_id` but it also possible to return `phylo` object.
### How do I get the tree for a particular taxonomic group?
If you are looking to get the tree for a particular taxonomic group, you need to
first identify it by its node id or ott id, and then use the `tol_subtree()`
function:
```{r, fig.width=7, fig.height=4}
mono_id <- tnrs_match_names("Monotremata")
mono_tree <- tol_subtree(ott_id = ott_id(mono_id))
plot(mono_tree)
```
### How do I find trees from studies focused on my favourite taxa?
The function `studies_find_trees()` allows the user to search for studies
matching a specific criteria. The function `studies_properties()` returns the
list of properties that can be used in the search.
```{r}
furry_studies <- studies_find_studies(property = "ot:focalCladeOTTTaxonName", value = "Mammalia")
furry_ids <- furry_studies$study_ids
```
Now that we know the `study_id`, we can ask for the meta data information
associated with this study:
```{r}
furry_meta <- get_study_meta("pg_2550")
get_publication(furry_meta) ## The citation for the source of the study
get_tree_ids(furry_meta) ## This study has 10 trees associated with it
candidate_for_synth(furry_meta) ## None of these trees are yet included in the OTL
```
Using `get_study("pg_2550")` would returns a `multiPhylo` object (default) with
all the trees associated with this particular study, while
`get_study_tree("pg_2550", "tree5513")` would return one of these trees.
### The tree returned by the API has duplicated tip labels, how can I work around it?
You may encounter the following error message:
```
Error in rncl(file = file, ...) : Taxon number 39 (coded by the token Pratia
angulata) has already been encountered in this tree. Duplication of taxa in a
tree is prohibited.
```
This message occurs as duplicate labels are not allowed in the NEXUS format and
it is stricly enforced by the part of the code used by `rotl` to import the
trees in memory.
If you use a version of `rotl` more recent than 0.4.1, this should not happen by
default for the function `get_study_tree`. If it happens with another function,
please [let us know](https://github.com/ropensci/rotl/issues).
The easiest way to work around this is to save the tree in a file, and use APE
to read it in memory:
```{r, eval=FALSE}
get_study_tree(
study_id = "pg_710", tree_id = "tree1277",
tip_label = "ott_taxon_name", file = "/tmp/tree.tre",
file_format = "newick"
)
tr <- ape::read.tree(file = "/tmp/tree.tre")
```
### How do I get the higher taxonomy for a given taxa?
If you encounter a taxon name you are not familiar with, it might be useful to
obtain its higher taxonomy to see where it fits in the tree of life. We can
combine several taxonomy methods to extract this information easily.
```{r}
giant_squid <- tnrs_match_names("Architeuthis")
tax_lineage(taxonomy_taxon_info(ott_id(giant_squid), include_lineage = TRUE))
```
### Why are OTT IDs discovered with `rotl` missing from an induced subtree?
Some taxonomic names that can be retrieved through the taxonomic name
resolution service are not part of the Open Tree's synthesis tree. These are
usually traditional higher-level taxa that have been found to be paraphyletic.
For instance, if you wanted to fetch a tree relating the three birds that go
into a [Turkducken](https://en.wikipedia.org/wiki/Turducken) as well as the pork
used for stuffing, you might search for the turkey, duck, chicken, and pork
genera:
```{r}
turducken <- c("Meleagris", "Anas", "Gallus", "Sus")
taxa <- tnrs_match_names(turducken, context = "Animals")
taxa
```
We have the OTT ids for each genus, however, if we tried to get the induced
subtree from these results, we would get an error:
```{r, error=TRUE}
tr <- tol_induced_subtree(ott_id(taxa))
```
As the error message suggests, some of the taxa are not found in the synthetic
tree. This occurs for 2 main reasons: either the taxa is invalid, or it is part
of a group that is not monophyletic in the synthetic tree. There are two ways to
get around this issue: (1) removing the taxa that are not part of the Open Tree;
(2) using the complete species name.
#### Removing the taxa missing from the synthetic tree
To help with this situation, `rotl` provides a way to identify the OTT ids that
are not part of the synthetic tree. The function `is_in_tree()` takes the output
of the `ott_id()` function and returns a vector of logical indicating whether
the taxa are part of the synthetic tree. We can then use to only keep the taxa that appear in the synthetic tree:
```{r}
in_tree <- is_in_tree(ott_id(taxa))
in_tree
tr <- tol_induced_subtree(ott_id(taxa)[in_tree])
```
#### Using the full taxonomic names
The best way to avoid these problems is to specify complete species names
(species being the lowest level of classification in the Open Tree taxonomy they
are guaranteed to be monophyletic):
```{r, fig.width=7, fig.height=4}
turducken_spp <- c("Meleagris gallopavo", "Anas platyrhynchos", "Gallus gallus", "Sus scrofa")
taxa <- tnrs_match_names(turducken_spp, context = "Animals")
tr <- tol_induced_subtree(ott_id(taxa))
plot(tr)
```
rotl/vignettes/fig-vignettes-subtree-1.png 0000644 0001762 0000144 00000044633 14442034171 020342 0 ustar ligges users PNG
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mI,섀im,KP|dX.݁wJ3Lɮn[ONN^__Czaujv= GGGEQTgWWWFcvvnߗe+ \ OjZ^t:|zzZ^^6,˻ZqssSCSSa2:u5xw>L>>;;+(j˲ fxyzzZ㟇Q:o/ [[[ǭVL_7|h4z߽ Ej\\\oA~@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBN =@8' zpBNnet IENDB` rotl/R/ 0000755 0001762 0000144 00000000000 14442060264 011435 5 ustar ligges users rotl/R/methods.R 0000644 0001762 0000144 00000006257 14441553346 013244 0 ustar ligges users ############################################################################
## methods ##
############################################################################
##' Methods for dealing with objects containing taxonomic information
##' (Taxonomy, TNRS endpoints)
##'
##' This is the page for the generic methods. See the help pages for
##' \code{\link{taxonomy_taxon_info}}, \code{\link{taxonomy_mrca}}, and
##' \code{\link{tnrs_match_names}} for more information.
##'
##' @title Methods for Taxonomy
##' @param tax an object returned by \code{\link{taxonomy_taxon_info}},
##' \code{\link{taxonomy_mrca}}, or \code{\link{tnrs_match_names}}
##' @param ... additional arguments (see
##' \code{\link{tnrs_match_names}})
##' @rdname taxonomy-methods
##' @export
tax_rank <- function(tax, ...) {
UseMethod("tax_rank")
}
##' @export
##' @rdname taxonomy-methods
ott_id <- function(tax, ...) {
UseMethod("ott_id")
}
##' @export
##' @rdname taxonomy-methods
synonyms <- function(tax, ...) {
UseMethod("synonyms")
}
##' @export
##' @rdname taxonomy-methods
tax_sources <- function(tax, ...) UseMethod("tax_sources")
##' @export
##' @rdname taxonomy-methods
is_suppressed <- function(tax, ...) UseMethod("is_suppressed")
##' @export
##' @rdname taxonomy-methods
unique_name <- function(tax, ...) UseMethod("unique_name")
##' @export
##' @rdname taxonomy-methods
tax_name <- function(tax, ...) UseMethod("tax_name")
### flags -----------------------------------------------------------------------
##' @export
##' @rdname match_names-methods
flags <- function(tax, ...) UseMethod("flags")
###
##' Retrieve the detailed information for the list of studies used in
##' the Tree of Life.
##'
##' @title List of studies used in the Tree of Life
##'
##' @details This function takes the object resulting from
##' \code{tol_about(study_list = TRUE)}, \code{tol_mrca()},
##' \code{tol_node_info()}, and returns a data frame listing the
##' \code{tree_id}, \code{study_id} and \code{git_sha} for the
##' studies currently included in the Tree of Life.
##'
##' @param tax a list containing a \code{source_id_map} slot.
##' @param ... additional arguments (currently unused)
##'
##' @return a data frame
##' @export
source_list <- function(tax, ...) UseMethod("source_list")
##' Extract the lineage information (higher taxonomy) from an object
##' returned by \code{\link{taxonomy_taxon_info}}.
##'
##' The object passed to this function must have been created using
##' the argument \code{include_lineage=TRUE}.
##'
##' @title Lineage of a taxon
##' @param tax an object created by \code{\link{taxonomy_taxon_info}}
##' using the argument \code{include_lineage=TRUE}.
##' @param ... additional arguments (currently unused).
##' @return A list with one slot per taxon that contains a data frame
##' with 3 columns: the taxonomy rank, the name, and unique name
##' for all taxa included in the lineage of the taxon up to the
##' root of the tree.
##' @rdname tax_lineage
##' @export
tax_lineage <- function(tax, ...) UseMethod("tax_lineage")
##' @export
##' @rdname tol_node_info
tol_lineage <- function(tax, ...) UseMethod("tol_lineage")
rotl/R/api-taxonomy.R 0000644 0001762 0000144 00000004217 14441553346 014220 0 ustar ligges users ##' @importFrom httr content
## Summary information about the OpenTree Taxonomy (OTT)
.taxonomy_about <- function(...) {
res <- otl_POST(path = "/taxonomy/about", body = list(), ...)
res
}
##' @importFrom jsonlite unbox
##' @importFrom httr content
## Information about an OpenTree Taxonomy (OTT) taxon
.taxonomy_taxon_info <- function(ott_id = NULL,
include_children = FALSE,
include_lineage = FALSE,
include_terminal_descendants = FALSE,
...) {
ott_id <- check_ott_ids(ott_id)
if (length(ott_id) > 1) {
stop("Must only supply one ", sQuote("ott_id"), " argument")
}
check_is_flag(include_children)
check_is_flag(include_lineage)
check_is_flag(include_terminal_descendants)
q <- list(
ott_id = jsonlite::unbox(ott_id),
include_children = jsonlite::unbox(include_children),
include_lineage = jsonlite::unbox(include_lineage),
include_terminal_descendants = jsonlite::unbox(include_terminal_descendants)
)
res <- otl_POST(path = "/taxonomy/taxon_info", body = q, ...)
res
}
##' @importFrom jsonlite unbox
##' @importFrom httr content
## Get a subtree from the OpenTree Taxonomy (OTT) taxonomic tree
.taxonomy_subtree <- function(ott_id = NULL, label_format = NULL, ...) {
ott_id <- check_ott_ids(ott_id)
if (length(ott_id) > 1) {
stop("Must only supply one ", sQuote("ott_id"), " argument")
}
q <- list(ott_id = jsonlite::unbox(ott_id))
if (!is.null(label_format)) {
if (!check_label_format(label_format)) {
stop(
sQuote("label_format"), " must be one of: ", sQuote("name"), ", ",
sQuote("id"), ", or ", sQuote("name_and_id")
)
}
q$label_format <- jsonlite::unbox(label_format)
}
res <- otl_POST(path = "/taxonomy/subtree", body = q, ...)
res
}
##' @importFrom httr content
## Get the most recent common ancestor (MRCA) from nodes in the OpenTree Taxonomy (OTT)
.taxonomy_mrca <- function(ott_ids = NULL, ...) {
ott_ids <- check_ott_ids(ott_ids)
q <- list(ott_ids = ott_ids)
res <- otl_POST(path = "/taxonomy/mrca", body = q, ...)
res
}
rotl/R/match_names.R 0000644 0001762 0000144 00000033621 14441553346 014053 0 ustar ligges users ## internal function that match the arguments provided to the correct
## row number in the data frame representing the Open Tree Taxonomy
## for a series of matched names.
check_args_match_names <- function(response, row_number, taxon_name, ott_id) {
orig_order <- attr(response, "original_order")
if (is.null(orig_order)) {
stop(
sQuote(substitute(response)), " was not created using ",
sQuote("tnrs_match_names")
)
}
if (missing(row_number) && missing(taxon_name) && missing(ott_id)) {
stop(
"You must specify one of ", sQuote("row_number"),
sQuote("taxon_name"), " or ", sQuote("ott_id")
)
} else if (!missing(row_number) && missing(taxon_name) && missing(ott_id)) {
if (!is.numeric(row_number)) {
stop(sQuote("row_number"), " must be a numeric.")
}
if (!all(row_number %in% orig_order)) {
stop(sQuote("row_number"), " is not a valid row number.")
}
i <- orig_order[row_number]
} else if (missing(row_number) && !missing(taxon_name) && missing(ott_id)) {
if (!is.character(taxon_name)) {
stop(sQuote("taxon_name"), " must be a character.")
}
i <- orig_order[match(tolower(taxon_name), response$search_string)]
if (any(is.na(i))) {
stop("Can't find ", taxon_name)
}
} else if (missing(row_number) && missing(taxon_name) && !missing(ott_id)) {
if (!check_numeric(ott_id)) {
stop(sQuote("ott_id"), " must look like a number.")
}
i <- orig_order[match(ott_id, response$ott_id)]
if (any(is.na(i))) stop("Can't find ", ott_id)
} else {
stop(
"You must use only one of ",
sQuote("row_number"),
sQuote("taxon_name"),
" or ", sQuote("ott_id"), "."
)
}
if (length(i) > 1) {
stop("You must supply a single element for each argument.")
}
i
}
match_row_number <- function(response, row_number, taxon_name, ott_id) {
## all the checks on the validity of the arguments are taken care
## by check_args_match_names()
if (missing(row_number) && missing(taxon_name) &&
missing(ott_id)) {
stop(
"You must specify one of ", sQuote("row_number"), " ",
sQuote("taxon_name"), " ", sQuote("ott_id")
)
} else if (!missing(row_number) && (missing(taxon_name) && missing(ott_id))) {
i <- row_number
} else if (!missing(taxon_name) && (missing(row_number) && missing(ott_id))) {
i <- match(tolower(taxon_name), response[["search_string"]])
} else if (!missing(ott_id) && (missing(row_number) && missing(taxon_name))) {
i <- match(ott_id, response[["ott_id"]])
} else {
stop(
"You must use only one of ", sQuote("row_number"),
" ", sQuote("taxon_name"), " ", sQuote("ott_id")
)
}
if (length(i) > 1) {
stop("You must supply a single element for each argument.")
}
i
}
##' Taxonomic names may have different meanings in different taxonomic
##' contexts, as the same genus name can be applied to animals and
##' plants for instance. Additionally, the meaning of a taxonomic name
##' may have change throughout its history, and may have referred to a
##' different taxon in the past. In such cases, a given names might
##' have multiple matches in the Open Tree Taxonomy. These functions
##' allow users to inspect (and update) alternative meaning of a given
##' name and its current taxonomic status according to the Open Tree
##' Taxonomy.
##'
##' To inspect alternative taxonomic meanings of a given name, you
##' need to provide the object resulting from a call to the
##' tnrs_match_names function, as well as one of either the row number
##' corresponding to the name in this object, the name itself (as used
##' in the original query), or the ott_id listed for this name.
##'
##' To update one of the name, you also need to provide the row number
##' in which the name to be replaced appear or its ott id.
##'
##' @title Inspect and Update alternative matches for a name returned
##' by tnrs_match_names
##' @param response an object generated by the
##' \code{\link{tnrs_match_names}} function
##' @param row_number the row number corresponding to the name to
##' inspect
##' @param taxon_name the taxon name corresponding to the name to
##' inspect
##' @param ott_id the ott id corresponding to the name to inspect
##' @param ... currently ignored
##' @return a data frame
##' @seealso \code{\link{tnrs_match_names}}
##' @examples
##' \dontrun{
##' matched_names <- tnrs_match_names(c("holothuria", "diadema", "boletus"))
##' inspect(matched_names, taxon_name="diadema")
##' new_matched_names <- update(matched_names, taxon_name="diadema",
##' new_ott_id = 631176)
##' new_matched_names
##' }
##' @export
##' @rdname match_names
inspect.match_names <- function(response, row_number, taxon_name, ott_id, ...) {
i <- check_args_match_names(response, row_number, taxon_name, ott_id)
j <- match_row_number(response, row_number, taxon_name, ott_id)
if (attr(response, "has_original_match")[j]) {
res <- attr(response, "original_response")
summary_match <- build_summary_match(res,
res_id = i, match_id = NULL,
initial_creation = FALSE
)
} else {
summary_match <- response[j, ]
}
clean_tnrs_summary(summary_match)
}
##' @export
##' @rdname match_names
inspect <- function(response, ...) UseMethod("inspect")
##' @param object an object created by \code{\link{tnrs_match_names}}
##' @param new_row_number the row number in the output of
##' \code{\link{inspect}} to replace the taxa specified by
##' \code{row_number}, \code{taxon_name}, or \code{ott_id}.
##' @param new_ott_id the ott id of the taxon to replace the taxa
##' specified by \code{row_number}, \code{taxon_name}, or
##' \code{ott_id}.
##' @export
##' @rdname match_names
##' @importFrom stats update
update.match_names <- function(object, row_number, taxon_name, ott_id,
new_row_number, new_ott_id, ...) {
response <- object
i <- check_args_match_names(response, row_number, taxon_name, ott_id)
j <- match_row_number(response, row_number, taxon_name, ott_id)
res <- attr(response, "original_response")
if (!attr(response, "has_original_match")[j]) {
warning(
"There is no match for this name, ",
"so there is nothing to replace it with."
)
return(response)
}
tmpRes <- res$results[[i]]
if (missing(row_number)) {
if (!missing(taxon_name)) {
rnb <- match(tolower(taxon_name), response$search_string)
} else if (!missing(ott_id)) {
rnb <- match(ott_id, response$ott_id)
}
} else {
rnb <- row_number
}
if (missing(new_row_number) && missing(new_ott_id)) {
stop(
"You must specify either ", sQuote("new_row_number"),
" or ", sQuote("new_ott_id")
)
} else if (!missing(new_row_number) && missing(new_ott_id)) {
if (!new_row_number %in% seq_len(length(tmpRes$matches))) {
stop(sQuote("new_row_number"), " is not a valid row number.")
}
j <- new_row_number
} else if (missing(new_row_number) && !missing(new_ott_id)) {
all_ott_id <- sapply(
lapply(
tmpRes[["matches"]],
function(x) x[["taxon"]]
),
function(x) .tax_ott_id(x)
)
j <- match(new_ott_id, all_ott_id)
if (any(is.na(j))) stop("Can't find ", new_ott_id)
} else {
stop(
"You must use only one of ", sQuote("new_row_number"),
" or ", sQuote("new_ott_id")
)
}
if (length(j) > 1) stop("You must supply a single element for each argument")
summ_match <- summary_row_factory(res, res_id = i, match_id = j)
response[rnb, ] <- summ_match
attr(response, "match_id")[rnb] <- j
clean_tnrs_summary(response)
}
## Access the elements for a given match:
## is_synonym, score, nomenclature_code, is_approximate_match, taxon
get_list_element <- function(response, i, list_name) {
list_content <- lapply(
response[["results"]][[i]][["matches"]],
function(x) {
x[[list_name]]
}
)
list_content
}
match_names_method_factory <- function(list_name) {
function(tax, row_number, taxon_name, ott_id, ...) {
response <- tax
res <- attr(response, "original_response")
no_args <- all(c(
missing(row_number), missing(taxon_name),
missing(ott_id)
))
if (no_args) {
res_i <- attr(response, "original_order")[attr(response, "has_original_match")]
ret <- lapply(res_i, function(i) {
get_list_element(res, i, list_name)
})
names(ret) <- sapply(res_i, function(i) {
get_list_element(res, i, "matched_name")[[1]]
})
## ret is already in the correct order so we can use a sequence
## to extract the correct element
ret <- mapply(function(x, i) {
ret[[x]][i]
}, seq_along(ret), attr(response, "match_id")[attr(response, "has_original_match")])
if (all(sapply(ret, length) == 1)) {
ret <- unlist(ret, use.names = TRUE)
}
} else {
i <- check_args_match_names(response, row_number, taxon_name, ott_id)
j <- match_row_number(response, row_number, taxon_name, ott_id)
if (attr(response, "has_original_match")[j]) {
ret <- get_list_element(res, i, list_name)[attr(response, "match_id")[j]]
} else {
ret <- list(
ott_id = NA_character_,
name = response[["search_string"]][j],
unique_name = NA_character_,
rank = NA_character_,
tax_sources = NA_character_,
flags = NA_character_,
synonyms = NA_character_,
is_suppressed = NA_character_
)
ret <- list(ret)
}
}
ret
}
}
match_names_taxon_method_factory <- function(.f) {
function(tax, row_number, taxon_name, ott_id, ...) {
extract_tax_list <- match_names_method_factory("taxon")
tax_info <- extract_tax_list(tax,
row_number = row_number,
taxon_name = taxon_name,
ott_id = ott_id
)
res <- lapply(tax_info, function(x) .f(x))
names(res) <- vapply(tax_info, function(x) .tax_unique_name(x), character(1))
res <- add_otl_class(res, .f)
res
}
}
##' \code{rotl} provides a collection of functions that allows users
##' to extract relevant information from an object generated by
##' \code{\link{tnrs_match_names}} function.
##'
##' These methods optionally accept one of the arguments
##' \code{row_number}, \code{taxon_name} or \code{ott_id} to retrieve
##' the corresponding information for one of the matches in the object
##' returned by the \code{\link{tnrs_match_names}} function.
##'
##' If these arguments are not provided, these methods can return
##' information for the matches currently listed in the object
##' returned by \code{\link{tnrs_match_names}}.
##'
##' @title \code{ott_id} and \code{flags} for taxonomic names matched
##' by \code{tnrs_match_names}
##' @param tax an object returned by \code{\link{tnrs_match_names}}
##' @param row_number the row number corresponding to the name for
##' which to list the synonyms
##' @param taxon_name the taxon name corresponding to the name for
##' which to list the synonyms
##' @param ott_id the ott id corresponding to the name for which to
##' list the synonyms
##' @param ... currently ignored
##' @return A list of the ott ids or flags for the taxonomic names
##' matched with \code{\link{tnrs_match_names}}, for either one or
##' all the names.
##' @examples
##' \dontrun{
##' rsp <- tnrs_match_names(c("Diadema", "Tyrannosaurus"))
##' rsp$ott_id # ott id for match currently in use
##' ott_id(rsp) # similar as above but elements are named
##'
##' ## flags() is useful for instance to determine if a taxon is extinct
##' flags(rsp, taxon_name="Tyrannosaurus")
##' }
##' @export
##' @rdname match_names-methods
ott_id.match_names <- match_names_taxon_method_factory(.tax_ott_id)
##' @export
##' @rdname match_names-methods
flags.match_names <- match_names_taxon_method_factory(.tax_flags)
##' When querying the Taxonomic Name Resolution Services for a
##' particular taxonomic name, the API returns as possible matches all
##' names that include the queried name as a possible synonym. This
##' function allows you to explore other synonyms for an accepted
##' name, and allows you to determine why the name you queried is
##' returning an accepted synonym.
##'
##' To list synonyms for a given taxonomic name, you need to provide
##' the object resulting from a call to the
##' \code{\link{tnrs_match_names}} function, as well as one of either
##' the row number corresponding to the name in this object, the name
##' itself (as used in the original query), or the ott_id listed for
##' this name. Otherwise, the synonyms for all the currently matched
##' names are returned.
##'
##' @title List the synonyms for a given name
##' @param tax a data frame generated by the
##' \code{\link{tnrs_match_names}} function
##' @param row_number the row number corresponding to the name for
##' which to list the synonyms
##' @param taxon_name the taxon name corresponding to the name for
##' which to list the synonyms
##' @param ott_id the ott id corresponding to the name for which to
##' list the synonyms
##' @param ... currently ignored
##' @return a list whose elements are all synonym names (as vectors of
##' character) for the taxonomic names that match the query (the
##' names of the elements of the list).
##' @examples
##' \dontrun{
##' echino <- tnrs_match_names(c("Diadema", "Acanthaster", "Fromia"))
##' ## These 3 calls are identical
##' synonyms(echino, taxon_name="Acanthaster")
##' synonyms(echino, row_number=2)
##' synonyms(echino, ott_id=337928)
##' }
##' @export
synonyms.match_names <- match_names_taxon_method_factory(.tax_synonyms)
##' @export
tax_sources.match_names <- match_names_taxon_method_factory(.tax_sources)
##' @export
tax_rank.match_names <- match_names_taxon_method_factory(.tax_rank)
##' @export
is_suppressed.match_names <- match_names_taxon_method_factory(.tax_is_suppressed)
##' @export
unique_name.match_names <- match_names_taxon_method_factory(.tax_unique_name)
##' @export
tax_name.match_names <- match_names_taxon_method_factory(.tax_name)
rotl/R/api-tol.R 0000644 0001762 0000144 00000010423 14441553346 013134 0 ustar ligges users ##' @importFrom jsonlite unbox
##' @importFrom httr content
## Summary information about the OpenTree Tree of Life
.tol_about <- function(include_source_list = FALSE, ...) {
check_is_flag(include_source_list)
q <- list(include_source_list = jsonlite::unbox(include_source_list))
res <- otl_POST(path = "tree_of_life/about", body = q, ...)
res
}
##' @importFrom jsonlite unbox
##' @importFrom httr content
## Get summary information about a node in the OpenTree Tree of Life
.tol_node_info <- function(ott_id = NULL, node_id = NULL, include_lineage = FALSE, ...) {
check_is_flag(include_lineage)
if (is.null(ott_id) && is.null(node_id)) {
stop("Must provide either ", sQuote("ott_id"), " or ", sQuote("node_id"))
}
if (!is.null(ott_id) && !is.null(node_id)) {
stop("Must provide either ", sQuote("ott_id"), " or ", sQuote("node_id"), ", not both.")
}
if (!is.null(ott_id)) {
ott_id <- check_ott_ids(ott_id)
if (length(ott_id) != 1) {
stop("Please provide a single ", sQuote("ott_id"), call. = FALSE)
}
q <- list(ott_id = jsonlite::unbox(ott_id), include_lineage = jsonlite::unbox(include_lineage))
} else {
if (!check_valid_node_id(node_id)) {
stop("Argument ", sQuote("node_id"), " must look like \'ott123\' or \'mrcaott123ott456\'.")
}
q <- list(node_id = jsonlite::unbox(node_id), include_lineage = jsonlite::unbox(include_lineage))
}
res <- otl_POST(path = "tree_of_life/node_info", body = q, ...)
res
}
##' @importFrom httr content
## Get the MRCA of a set of nodes
.tol_mrca <- function(ott_ids = NULL, node_ids = NULL, ...) {
if (is.null(ott_ids) && is.null(node_ids)) {
stop("Must provide ", sQuote("ott_ids"), " or ", sQuote("node_ids"), " (or both).")
}
q <- list()
if (!is.null(ott_ids)) {
ott_ids <- check_ott_ids(ott_ids)
q$ott_ids <- ott_ids
}
if (!is.null(node_ids)) {
check_node_ids(node_ids)
q$node_ids <- node_ids
}
res <- otl_POST(path = "tree_of_life/mrca", body = q, ...)
res
}
# ignoring 'include_lineage' for subtree below. arguson only
##' @importFrom jsonlite unbox
##' @importFrom httr content
## Get a subtree from the OpenTree Tree of Life
.tol_subtree <- function(ott_id = NULL, node_id = NULL, label_format = NULL, ...) {
if (is.null(ott_id) && is.null(node_id)) {
stop("Must provide either ", sQuote("ott_id"), " or ", sQuote("node_id"))
}
if (!is.null(ott_id) && !is.null(node_id)) {
stop("Must provide either ", sQuote("ott_id"), " or ", sQuote("node_id"), ", not both.")
}
if (!is.null(ott_id)) {
ott_id <- check_ott_ids(ott_id)
if (length(ott_id) != 1) {
stop("Please provide a single ", sQuote("ott_id"))
}
q <- list(ott_id = jsonlite::unbox(ott_id))
} else {
if (!check_valid_node_id(node_id)) {
stop("Argument ", sQuote("node_id"), " must look like \'ott123\' or \'mrcaott123ott456\'.")
}
q <- list(node_id = jsonlite::unbox(node_id))
}
if (!is.null(label_format)) {
if (!check_label_format(label_format)) {
stop(
sQuote("label_format"), " must be one of: ", sQuote("name"), ", ",
sQuote("id"), ", or ", sQuote("name_and_id")
)
}
q$label_format <- jsonlite::unbox(label_format)
}
res <- otl_POST(path = "tree_of_life/subtree", body = q, ...)
res
}
##' @importFrom httr content
## Get an induced subtree from the OpenTree Tree of Life from a set of nodes
.tol_induced_subtree <- function(ott_ids = NULL, node_ids = NULL, label_format = NULL, ...) {
if (is.null(ott_ids) && is.null(node_ids)) {
stop("Must provide ", sQuote("ott_ids"), " or ", sQuote("node_ids"), " (or both).")
}
q <- list()
if (!is.null(label_format)) {
if (!check_label_format(label_format)) {
stop(
sQuote("label_format"), " must be one of: ", sQuote("name"), ", ",
sQuote("id"), ", or ", sQuote("name_and_id")
)
}
q$label_format <- jsonlite::unbox(label_format)
}
if (!is.null(ott_ids)) {
ott_ids <- check_ott_ids(ott_ids)
q$ott_ids <- ott_ids
}
if (!is.null(node_ids)) {
check_node_ids(node_ids)
q$node_ids <- node_ids
}
if ((length(ott_ids) + length(node_ids)) < 2) {
stop("At least two valid ", sQuote("ott_ids"), " or ", sQuote("node_ids"), " must be provided.")
}
res <- otl_POST("tree_of_life/induced_subtree", body = q, ...)
res
}
rotl/R/utils.R 0000644 0001762 0000144 00000001440 14441553346 012726 0 ustar ligges users rotl_is_flag <- function(x) {
identical(length(x), 1L) &&
rlang::is_bare_logical(x) &&
!is.na(x)
}
rotl_is_string <- function(x) {
identical(length(x), 1L) &&
rlang::is_bare_character(x) &&
!is.na(x)
}
check_factory <- function(f, x, arg_name, msg) {
if (f(x)) {
return(TRUE)
}
stop(
paste0(
"Argument ",
sQuote(arg_name),
msg
),
call. = FALSE
)
}
check_is_flag <- function(x) {
arg_name <- deparse(substitute(x))
check_factory(
rotl_is_flag,
x,
arg_name,
" is not a flag (a length one logical vector)."
)
}
check_is_string <- function(x) {
arg_name <- deparse(substitute(x))
check_factory(
rotl_is_string,
x,
arg_name,
" is not a string (a length one character vector)."
)
}
rotl/R/api-collections.R 0000644 0001762 0000144 00000001454 14441553346 014660 0 ustar ligges users ## This endpoint currently returns JSON in XML with mime type as text/html
.collection_find_collections <- function(property = NULL, value = NULL,
verbose = FALSE, ...) {
check_is_flag(verbose)
req_body <- list()
req_body$verbose <- verbose
res <- otl_POST(
path = "collections/find_collections",
body = req_body, ...
)
res
}
.collection_properties <- function(...) {
req_body <- list()
res <- otl_POST(
path = "collections/properties",
body = req_body, ...
)
res
}
.get_collection <- function(owner_id = NULL, collection_name = NULL, ...) {
check_is_string(owner_id)
check_is_string(collection_name)
req_body <- list()
res <- otl_GET(path = paste("collections", owner_id, collection_name,
sep = "/"
), ...)
res
}
rotl/R/base.R 0000644 0001762 0000144 00000013412 14441553346 012502 0 ustar ligges users otl_base_url <- function(dev = FALSE) {
if (dev) {
return("devapi.opentreeoflife.org")
}
"api.opentreeoflife.org"
}
otl_url <- function(dev = FALSE) {
paste0("https://", otl_base_url(dev))
}
otl_version <- function(version) {
if (missing(version)) {
return("v3")
} else {
return(version)
}
}
##' @importFrom rlang interrupt
is_otl_online <- function(dev = FALSE) {
is_online <- !is.null(curl::nslookup(otl_base_url(dev), error = FALSE))
if (!is_online) {
message("No internet connection or the Open Tree of Life's API is down.")
rlang::interrupt()
}
}
# Take a request object and return list (if JSON) or plain text (if another
# type)
##' @importFrom httr content
##' @importFrom jsonlite fromJSON
otl_parse <- function(req) {
if (grepl("application/json", req[["headers"]][["content-type"]])) {
return(jsonlite::fromJSON(httr::content(req, "text", encoding = "UTF-8"), simplifyVector = FALSE))
}
txt <- httr::content(req, as = "text", encoding = "UTF-8")
if (identical(txt, "")) {
stop("No output to parse; check your query.", call. = FALSE)
}
txt
}
otl_check_error <- function(cont) {
if (is.list(cont)) {
if (exists("description", cont)) {
if (exists("Error", cont$description)) {
stop(paste("Error: ", cont$description$error, "\n", sep = ""))
} else if (exists("message", cont)) {
stop(paste("Message: ", cont$descrption$message, "\n", sep = ""))
}
}
}
}
## Check and parse result of query
##' @importFrom rlang interrupt
otl_check <- function(req) {
early_stop <- function(m) {
message(m)
rlang::interrupt()
}
tryCatch({
if (!req$status_code < 400) {
msg <- otl_parse(req)
stop("HTTP failure: ", req$status_code, "\n", msg, call. = FALSE)
}
desc <- otl_parse(req)
otl_check_error(desc)
desc
},
error = function(e) early_stop(e),
warning = function(w) early_stop(w)
)
}
##' @importFrom httr GET
otl_GET <- function(path, url = otl_url(...), otl_v = otl_version(...), ...) {
is_otl_online()
req <- httr::GET(url, path = paste(otl_v, path, sep = "/"), ...)
otl_check(req)
}
##' @importFrom jsonlite toJSON
##' @importFrom httr POST
otl_POST <- function(path, body, url = otl_url(...), otl_v = otl_version(...), ...) {
is_otl_online()
stopifnot(is.list(body))
body_json <- ifelse(length(body), jsonlite::toJSON(body), "")
req <- httr::POST(url,
path = paste(otl_v, path, sep = "/"),
body = body_json, ...
)
otl_check(req)
}
otl_formats <- function(format) {
switch(tolower(format),
"nexus" = ".nex",
"newick" = ".tre",
"nexml" = ".nexml",
"json" = ".json",
""
) # fall through is no extension = nex(j)son
}
## Strip all characters except the ottId from a OpenTree label (internal or terminal)
otl_ottid_from_label <- function(label) {
return(as.numeric(gsub("(.+[ _]ott)([0-9]+)", "\\2", label)))
}
##' @importFrom rncl read_newick_phylo
phylo_from_otl <- function(res, dedup = FALSE) {
if (is.list(res)) {
if (!is.null(res$newick)) {
tree <- res$newick
} else if (!is.null(res$subtree)) {
tree <- res$subtree
} else {
stop("Cannot find tree")
}
} else if (is.character(res)) {
tree <- res
} else {
stop("I don't know how to deal with this format.")
}
if (grepl("\\(", tree)) {
fnm <- tempfile()
cat(tree, file = fnm)
if (!dedup) {
phy <- rncl::read_newick_phylo(fnm)
} else {
dedup_tr <- deduplicate_labels(fnm)
phy <- rncl::read_newick_phylo(dedup_tr)
unlink(dedup_tr)
}
unlink(fnm)
} else {
phy <- tree_to_labels(tree)$tip_label
}
return(phy)
}
nexml_from_otl <- function(res) {
if (!requireNamespace("RNeXML", quietly = TRUE)) {
stop("The RNeXML package is needed to use the nexml file format")
}
fnm <- tempfile()
cat(res, file = fnm)
phy <- RNeXML::nexml_read(x = fnm)
unlink(fnm)
phy
}
## check if the argument provided looks like a number (can be coerced
## to integer/numeric).
check_numeric <- function(x) {
if (is.null(x)) {
return(FALSE)
}
if (length(x) != 1) {
stop("only 1 element should be provided")
}
if (!is.numeric(x)) {
x <- as.character(x)
if (any(is.na(x))) {
return(FALSE)
}
return(grepl("^[0-9]+$", x))
} else {
return(x %% 1 == 0)
}
}
## Check that ott_ids are not NULL, not NAs and look like numbers
check_ott_ids <- function(ott_ids) {
if (!is.null(ott_ids)) {
if (inherits(ott_ids, "otl_ott_id")) {
## convert objects returned by ott_id method to a vector
ott_ids <- unlist(ott_ids)
}
if (any(is.na(ott_ids))) {
stop("NAs are not allowed")
}
if (!all(sapply(ott_ids, check_numeric))) {
stop(sQuote("ott_ids"), " must look like numbers.")
}
} else {
stop("You must supply some OTT ids.")
}
ott_ids
}
## all nodes have a node_id (character, e.g. "ott12345" or "mrcaott123ott456")
check_valid_node_id <- function(x) {
if (length(x) != 1) {
stop("only 1 element should be provided")
}
if (!is.character(x)) {
return(FALSE)
}
if (grepl("^mrcaott\\d+ott\\d+", x) || grepl("^ott\\d+", x)) {
return(TRUE)
} else {
return(FALSE)
}
}
check_node_ids <- function(node_ids) {
if (!is.null(node_ids)) {
if (!is.character(node_ids)) {
stop("Argument ", sQuote("node_ids"), " must be of type character.")
}
if (any(is.na(node_ids))) {
stop("NAs are not allowed")
}
if (!all(sapply(node_ids, check_valid_node_id))) {
stop(sQuote("node_ids"), " must look like \'ott123\' or \'mrcaott123ott456\'.")
}
}
}
# node labels for tree_of_life subtree and induced_subtree
# might also be useful for taxonomy queries
check_label_format <- function(x) {
if (x %in% c("name", "id", "name_and_id")) {
return(TRUE)
} else {
return(FALSE)
}
}
rotl/R/tnrs.R 0000644 0001762 0000144 00000031507 14441645011 012552 0 ustar ligges users ##' Match taxonomic names to the Open Tree Taxonomy.
##'
##' Accepts one or more taxonomic names and returns information about
##' potential matches for these names to known taxa in the Open Tree
##' Taxonomy.
##'
##' This service uses taxonomic contexts to disambiguate homonyms and misspelled
##' names; a context may be specified using the \code{context_name} argument.
##' The default value for \code{context_name} is "All life". If no context is
##' specified (i.e., \code{context_name} is set to \code{NULL}), then the
##' context will be inferred (i.e., the shallowest taxonomic context that
##' contains all unambiguous names in the input). Taxonomic contexts are
##' uncontested higher taxa that have been selected to allow limits to be
##' applied to the scope of TNRS searches (e.g. 'match names only within
##' flowering plants'). Once a context has been identified (either
##' user-specified or inferred), all taxon name matches will performed only
##' against taxa within that context. For a list of available taxonomic
##' contexts, see \code{\link{tnrs_contexts}}.
##'
##' A name is considered unambiguous if it is not a synonym and has
##' only one exact match to any taxon name in the entire taxonomy.
##'
##' When the name search returns multiple matches, the taxon with the highest
##' match score is returned. If the name returned is not the one you intended,
##' you can use the \code{inspect} function to check the other taxa returned by
##' your search. The
##' \href{https://docs.ropensci.org/rotl/articles/rotl.html#how-to-change-the-ott-ids-assigned-to-my-taxa}{Getting
##' Started vignette} has more information on how to do this.
##'
##' Several functions listed in the \sQuote{See also} section can be
##' used to inspect and manipulate the object generated by this
##' function.
##'
##'
##' @title Match names to the Open Tree Taxonomy
##' @param names taxon names to be queried. Currently limited to 10,000 names
##' for exact matches and 2,500 names for approximate matches (character
##' vector)
##' @param context_name name of the taxonomic context to be searched (length-one
##' character vector or \code{NULL}). Must match (case sensitive) one of the
##' values returned by \code{\link{tnrs_contexts}}. Default to "All life".
##' @param do_approximate_matching A logical indicating whether or not to
##' perform approximate string (a.k.a. \dQuote{fuzzy}) matching. Using
##' \code{FALSE} will greatly improve speed. Default, however, is \code{TRUE}.
##' @param ids A vector of ids to use for identifying names. These will be
##' assigned to each name in the names array. If ids is provided, then ids and
##' names must be identical in length.
##' @param include_suppressed Ordinarily, some quasi-taxa, such as incertae
##' sedis buckets and other non-OTUs, are suppressed from TNRS results. If
##' this parameter is true, these quasi-taxa are allowed as possible TNRS
##' results.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A data frame summarizing the results of the query. The original
##' query output is appended as an attribute to the returned object (and can
##' be obtained using \code{attr(object, "original_response")}).
##' @seealso \code{\link{inspect.match_names}},
##' \code{\link{update.match_names}}, \code{\link{synonyms.match_names}}.
##' @examples \dontrun{
##' deuterostomes <- tnrs_match_names(names=c("echinodermata", "xenacoelomorpha",
##' "chordata", "hemichordata"))
##' }
##' @importFrom stats setNames
##' @export
tnrs_match_names <- function(names = NULL, context_name = "All life",
do_approximate_matching = TRUE, ids = NULL,
include_suppressed = FALSE, ...) {
if (!is.null(context_name) &&
!context_name %in% unlist(tnrs_contexts(...))) {
stop(
"The ", sQuote("context_name"),
" is not valid. Check possible values using tnrs_contexts()"
)
}
## take care of duplicated names
if (any(duplicated(tolower(names)))) {
names <- tolower(names)
warning("Some names were duplicated: ",
paste(sQuote(names[duplicated(names)]), collapse = ", "), ".",
call. = FALSE
)
names <- unique(names)
}
res <- .tnrs_match_names(
names = names, context_name = context_name,
do_approximate_matching = do_approximate_matching,
ids = ids, include_suppressed = include_suppressed,
...
)
check_tnrs(res)
match_ids <- highest_match_score(res)
if (!identical(length(res[["results"]]), length(match_ids))) {
stop(
"The number of matches and the number of 'results' elements should",
" be the same."
)
}
summary_match <- mapply(
function(rid, mid) {
build_summary_match(
res,
res_id = rid,
match_id = mid,
initial_creation = TRUE
)
},
seq_along(res[["results"]]),
match_ids,
SIMPLIFY = FALSE
)
## add taxon names with no maches
summary_match <- do.call("rbind", summary_match)
summary_match <- as.data.frame(summary_match, stringsAsFactors = FALSE)
summary_match$search_string <- gsub("\\\\", "", summary_match$search_string)
## reorder to match original query
ordr <- match(tolower(names), tolower(summary_match$search_string))
stopifnot(identical(length(match_ids), length(ordr)))
summary_match <- summary_match[ordr, ]
match_ids <- match_ids[ordr]
summary_match[["approximate_match"]] <-
convert_to_logical(summary_match[["approximate_match"]])
summary_match[["is_synonym"]] <-
convert_to_logical(summary_match[["is_synonym"]])
summary_match[["flags"]] <- convert_to_logical(summary_match[["flags"]])
has_original_match <- !is.na(summary_match[["number_matches"]])
json_coords <- data.frame(
search_string = names,
original_order = as.numeric(rownames(summary_match)),
match_id = match_ids,
has_original_match = has_original_match,
row.names = seq_along(names),
stringsAsFactors = FALSE
)
attr(summary_match, "original_order") <- as.numeric(rownames(summary_match))
attr(summary_match, "original_response") <- res
attr(summary_match, "match_id") <- match_ids
attr(summary_match, "has_original_match") <- has_original_match
attr(summary_match, "json_coords") <- json_coords
class(summary_match) <- c("match_names", "data.frame")
rownames(summary_match) <- NULL
summary_match
}
##' @importFrom stats na.omit
convert_to_logical <- function(x) {
if (all(stats::na.omit(x) %in% c("TRUE", "FALSE"))) {
x <- as.logical(x)
} else {
x
}
}
check_tnrs <- function(req) {
no_match <- req[["unmatched_names"]]
if (any(vapply(no_match, length, integer(1)) > 0)) {
warning(
paste(unlist(no_match), collapse = ", "), " are not matched",
call. = FALSE
)
}
}
tnrs_columns <- list(
"search_string" = function(x) x[["search_string"]],
"unique_name" = function(x) .tax_unique_name(x[["taxon"]]),
"approximate_match" = function(x) x[["is_approximate_match"]],
"score" = function(x) x[["score"]],
"ott_id" = function(x) .tax_ott_id(x[["taxon"]]),
"is_synonym" = function(x) x[["is_synonym"]],
"flags" = function(x) paste(.tax_flags(x[["taxon"]]), collapse = ", ")
)
summary_row_factory <- function(res, res_id, match_id, columns = tnrs_columns) {
res_address <- res[["results"]][[res_id]][["matches"]][[match_id]]
ret <- sapply(columns, function(f) f(res_address))
n_match <- length(res[["results"]][[res_id]][["matches"]])
c(ret, number_matches = n_match)
}
build_summary_match <- function(res, res_id, match_id = NULL, initial_creation) {
if (length(res_id) > 1 &&
(!is.null(match_id) && length(match_id) > 1)) {
stop("Something is wrong. Please contact us.")
}
build_summary_row <- function(rid) {
if (is.null(match_id)) {
match_id <- seq_len(length(res[["results"]][[rid]][["matches"]]))
}
if (identical(length(match_id), 0L) ||
is.null(res[["results"]][[rid]][["matches"]][match_id][[1]])) {
return(build_empty_row(tolower(res[["results"]][[rid]][["name"]])))
}
res <- lapply(match_id, function(mid) {
summary_row_factory(res, rid, mid)
})
if (identical(length(match_id), 1L)) {
unlist(res)
} else {
res
}
}
summary_row <- lapply(res_id, build_summary_row)
if (identical(length(res_id), 1L)) {
summary_row <- unlist(summary_row, recursive = FALSE)
}
## Needed if only 1 row returned
if (!inherits(summary_row, "list")) {
summary_row <- list(summary_row)
}
summary_match <- do.call("rbind", summary_row)
summary_match <- data.frame(summary_match, stringsAsFactors = FALSE)
names(summary_match) <- c(names(tnrs_columns), "number_matches")
clean_tnrs_summary(summary_match)
}
##' @importFrom stats setNames
build_empty_row <- function(x) {
no_match_row <- stats::setNames(
rep(NA, length(tnrs_columns) + 1),
c(names(tnrs_columns), "number_matches")
)
no_match_row[1] <- x
no_match_row
}
lowest_ott_id <- function(rsp) {
vapply(seq_along(rsp[["results"]]), function(x) {
.r <- build_summary_match(
res = rsp, res_id = x, match_id = NULL,
initial_creation = TRUE
)
.r <- .r[(!as.logical(.r[["is_synonym"]])) &
!is.na(.r[["flags"]]) &
.r[["flags"]] == "", ]
if (nrow(.r) > 0) {
which.min(.r[["ott_id"]])
} else {
1L
}
}, integer(1))
}
highest_match_score <- function(rsp) {
vapply(seq_along(rsp[["results"]]), function(x) {
.r <- build_summary_match(
res = rsp, res_id = x, match_id = NULL,
initial_creation = TRUE
)
.r <- .r[(!as.logical(.r[["is_synonym"]])) &
!is.na(.r[["flags"]]) &
.r[["flags"]] == "", ]
if (nrow(.r) > 0) {
which.max(.r[["score"]])
} else {
1L
}
}, integer(1))
}
clean_tnrs_summary <- function(summary_match) {
summary_match[["approximate_match"]] <-
convert_to_logical(summary_match[["approximate_match"]])
summary_match[["score"]] <- as.numeric(summary_match[["score"]])
summary_match[["is_synonym"]] <-
convert_to_logical(summary_match[["is_synonym"]])
summary_match[["flags"]] <- convert_to_logical(summary_match[["flags"]])
summary_match[["ott_id"]] <- as.integer(summary_match[["ott_id"]])
summary_match[["number_matches"]] <-
as.integer(summary_match[["number_matches"]])
summary_match
}
##' This function returns a list of pre-defined taxonomic contexts
##' (i.e. clades) which can be used to limit the scope of tnrs
##' queries.
##'
##' Taxonomic contexts are available to limit the scope of TNRS
##' searches. These contexts correspond to uncontested higher taxa
##' such as 'Animals' or 'Land plants'. This service returns a list
##' containing all available taxonomic context names, which may be
##' used as input (via the \code{context_name} argument in other
##' functions) to limit the search scope of other services including
##' \code{\link{tnrs_match_names}}.
##' @title TNRS contexts
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return Returns invisibly a list for each major clades (e.g.,
##' animals, microbes, plants, fungi, life) whose elements
##' contains the possible contexts.
##' @export
tnrs_contexts <- function(...) {
res <- .tnrs_contexts(...)
class(res) <- "tnrs_contexts"
res
}
##' @export
print.tnrs_contexts <- function(x, ...) {
cat("Possible contexts:\n")
lapply(x, function(t) {
res <- unlist(t)
cat(" ", res[1], "\n")
if (length(res) > 1) {
lapply(seq(2, length(res), by = 5), function(l) {
m <- ifelse(l + 5 <= length(res), l + 4, length(res))
cat(" ", paste(res[l:m], collapse = ", "), "\n")
})
}
})
}
##' Return a taxonomic context given a list of taxonomic names
##'
##' Find the least inclusive taxonomic context that includes all the
##' unambiguous names in the input set. Unambiguous names are names
##' with exact matches to non-homonym taxa. Ambiguous names (those
##' without exact matches to non-homonym taxa) are indicated in
##' results.
##'
##' @title Infer the taxonomic context from a list of names
##' @param names Vector of taxon names.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A list including the context name, the context ott id and
##' possibly the names in the query that have an ambiguous
##' taxonomic meaning in the query.
##' @examples
##' \dontrun{
##' res <- tnrs_infer_context(names=c("Stellula calliope", "Struthio camelus"))
##' }
##' @export
tnrs_infer_context <- function(names = NULL, ...) {
res <- .tnrs_infer_context(names = names, ...)
return(res)
}
rotl/R/studies-methods.R 0000644 0001762 0000144 00000005203 14441553346 014710 0 ustar ligges users
### list_trees -----------------------------------------------------------------
##' List trees ids in objects returned by
##' \code{\link{studies_find_studies}} and
##' \code{\link{studies_find_trees}}.
##'
##' \code{list_trees} returns all trees associated with a particular
##' study when used on an object returned by
##' \code{\link{studies_find_studies}}, but only the trees that match
##' the search criteria when used on objects returned by
##' \code{\link{studies_find_trees}}.
##'
##' @param matched_studies an object created by
##' \code{studies_find_trees} or \code{studies_find_studies}.
##' @param study_id a \code{study_id} listed in the object returned by
##' \code{studies_find_trees}
##' @param ... Currently unused
##' @return \code{list_trees} returns a list of the tree_ids for each
##' study that match the requested criteria. If a \code{study_id}
##' is provided, then only the trees for this study are returned
##' as a vector.
##' @seealso \code{\link{studies_find_studies}} and
##' \code{\link{studies_find_trees}}. The help for these functions
##' have examples demonstrating the use of \code{list_trees}.
##' @export
list_trees <- function(matched_studies, ...) UseMethod("list_trees")
##' @rdname list_trees
##' @export
list_trees.matched_studies <- function(matched_studies, study_id, ...) {
res <- attr(matched_studies, "found_trees")
if (missing(study_id)) {
res
} else {
if (is.na(match(study_id, names(res)))) {
stop(sQuote(study_id), " isn't a valid id.")
} else {
res[[study_id]]
}
}
}
##' @export
##' @rdname get_study_meta
get_tree_ids <- function(sm) UseMethod("get_tree_ids")
##' @export
##' @rdname get_study_meta
get_publication <- function(sm) UseMethod("get_publication")
##' @export
##' @rdname get_study_meta
candidate_for_synth <- function(sm) UseMethod("candidate_for_synth")
##' @export
##' @rdname get_study_meta
get_study_year <- function(sm) UseMethod("get_study_year")
##' @export
##' @rdname get_study_meta
get_tree_ids.study_meta <- function(sm) {
unlist(sm[["nexml"]][["treesById"]][[sm[["nexml"]][["^ot:treesElementOrder"]][[1]]]][["^ot:treeElementOrder"]])
}
##' @export
##' @rdname get_study_meta
get_publication.study_meta <- function(sm) {
pub <- sm[["nexml"]][["^ot:studyPublicationReference"]]
attr(pub, "DOI") <- sm[["nexml"]][["^ot:studyPublication"]][["@href"]]
pub
}
##' @export
##' @rdname get_study_meta
candidate_for_synth.study_meta <- function(sm) {
unlist(sm[["nexml"]][["^ot:candidateTreeForSynthesis"]])
}
##' @export
##' @rdname get_study_meta
get_study_year.study_meta <- function(sm) {
sm[["nexml"]][["^ot:studyYear"]]
}
rotl/R/studies-utils.R 0000644 0001762 0000144 00000004720 14441553346 014410 0 ustar ligges users ## Unexported function that generates a data frame summarizing the metadata.
## This function is used by both studies_find_studies and studies_find_trees,
## to generate the output when using the argument detailed=TRUE
##' @importFrom stats setNames
summarize_meta <- function(study_ids) {
fill <- function(x) {
if (length(unlist(x))) {
x
} else {
""
}
}
meta_raw <- lapply(study_ids, function(x) get_study_meta(x))
## Extract the metadata
meta <- lapply(meta_raw, function(m) {
c(
tree_ids = fill(list(get_tree_ids(m))),
study_year = fill(get_study_year(m)),
publication = fill(get_publication(m)),
doi = fill(attr(get_publication(m), "DOI")),
candidate = fill(list(candidate_for_synth(m)))
)
})
## Convert into a data frame
dat <- lapply(meta, function(m) {
c(
n_trees = length(m[["tree_ids"]]),
tree_ids = limit_trees(m[["tree_ids"]]),
candidate = paste(m[["candidate"]], collapse = ", "),
study_year = m[["study_year"]],
title = fill(extract_title(m[["publication"]])),
study_doi = m[["doi"]]
)
})
dat <- do.call("rbind", dat)
dat <- cbind(study_ids = study_ids, dat)
rownames(dat) <- NULL
dat <- data.frame(dat, stringsAsFactors = FALSE)
## Add list of found trees as attributes
found_trees <- lapply(meta, function(m) {
m[["tree_ids"]]
})
found_trees <- stats::setNames(found_trees, study_ids)
attr(dat, "found_trees") <- found_trees
attr(dat, "metadata") <- meta_raw
dat
}
## Unexported function that attempts to extract title from the
## citation information associated with the study information. The
## function gets the element that follows what looks like a year in
## the string.
## pub_orig: the publication string extracted from the study metadata
## split_char: the character on which the bibliographic elements are
## separated with. (currently only deals with . and ,)
extract_title <- function(pub_orig, split_char = "\\.") {
pub <- unlist(strsplit(pub_orig, split = split_char))
pub <- gsub("^\\s|\\s$", "", pub)
which_year <- grep("^\\d{4}[a-z]?$", pub)
res <- pub[which_year + 1]
if (length(res) > 0) {
return(res)
} else if (split_char == ",") {
return(character(0))
} else {
extract_title(pub_orig, ",")
}
}
## Unexported function that limit the display of tree_ids to the first
## 5 values.
limit_trees <- function(x) {
if (length(x) > 5) {
x <- c(x[1:5], "...")
}
paste(x, collapse = ", ")
}
rotl/R/studies.R 0000644 0001762 0000144 00000051125 14441553346 013253 0 ustar ligges users ##' Return the list of study properties that can be used to search
##' studies and trees used in the synthetic tree.
##'
##' The list returned has 2 elements \code{tree_properties} and
##' \code{studies_properties}. Each of these elements lists additional
##' arguments to customize the API request properties that can be used
##' to search for trees and studies that are contributing to the
##' synthetic tree. The definitions of these properties are available
##' from
##' \url{https://github.com/OpenTreeOfLife/phylesystem-api/wiki/NexSON}
##'
##' @title Properties of the Studies
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A list of the study properties that can be used to find
##' studies and trees that are contributing to the synthetic tree.
##' @seealso \code{\link{studies_find_trees}}
##' @export
##' @examples
##' \dontrun{
##' all_the_properties <- studies_properties()
##' unlist(all_the_properties$tree_properties)
##' }
studies_properties <- function(...) {
res <- .studies_properties(...)
lapply(res, unlist)
}
##' Return the identifiers of studies that match given properties
##'
##' @title Find a Study
##' @param exact Should exact matching be used? (logical, default
##' \code{FALSE})
##' @param property The property to be searched on (character)
##' @param value The property value to be searched on (character)
##' @param detailed If \code{TRUE} (default), the function will return
##' a data frame that summarizes information about the study (see
##' \sQuote{Value}). Otherwise, it only returns the study
##' identifiers.
##' @param verbose Should the output include all metadata (logical
##' default \code{FALSE})
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return If \code{detailed=TRUE}, the function returns a data frame
##' listing the study id (\code{study_ids}), the number of trees
##' associated with this study (\code{n_trees}), the tree ids (at
##' most 5) associated with the studies (\code{tree_ids}), the
##' tree id that is a candidate for the synthetic tree if any
##' (\code{candidate}), the year of publication of the study
##' (\code{study_year}), the title of the publication for the
##' study (\code{title}), and the DOI (Digital Object Identifier)
##' for the study (\code{study_doi}).
##'
##' If \code{detailed=FALSE}, the function returns a data frame
##' with a single column containing the study identifiers.
##' @seealso \code{\link{studies_properties}} which lists properties
##' against which the studies can be
##' searched. \code{\link{list_trees}} that returns a list for all
##' tree ids associated with a study.
##' @export
##' @examples
##' \dontrun{
##' ## To match a study for which the identifier is already known
##' one_study <- studies_find_studies(property="ot:studyId", value="pg_719")
##' list_trees(one_study)
##'
##' ## To find studies pertaining to Mammals
##' mammals <- studies_find_studies(property="ot:focalCladeOTTTaxonName",
##' value="mammalia")
##' ## To extract the tree identifiers for each of the studies
##' list_trees(mammals)
##' ## ... or for a given study
##' list_trees(mammals, "ot_308")
##'
##' ## Just the identifiers without other information about the studies
##' mammals <- studies_find_studies(property="ot:focalCladeOTTTaxonName",
##' value="mammalia", detailed=FALSE)
##' }
studies_find_studies <- function(property = NULL, value = NULL, verbose = FALSE,
exact = FALSE, detailed = TRUE, ...) {
.res <- .studies_find_studies(
property = property, value = value,
verbose = verbose, exact = exact, ...
)
res <- vapply(
.res[["matched_studies"]],
function(x) x[["ot:studyId"]],
character(1)
)
if (detailed) {
dat <- summarize_meta(res)
} else {
meta_raw <- .res
dat <- data.frame(study_ids = res, stringsAsFactors = FALSE)
attr(dat, "found_trees") <- paste(
"If you want to get a list of the",
"trees associated with the studies,",
"use", sQuote("detailed = TRUE")
)
class(dat) <- c("study_ids", class(dat))
attr(dat, "metadata") <- meta_raw
}
class(dat) <- c("matched_studies", class(dat))
dat
}
##' @export
print.study_ids <- function(x, ...) {
print(format(x), ...)
}
##' Return a list of studies for which trees match a given set of
##' properties
##'
##' The list of possible values to be used as values for the argument
##' \code{property} can be found using the function
##' \code{\link{studies_properties}}.
##'
##' @title Find Trees
##' @param property The property to be searched on (character)
##' @param value The property-value to be searched on (character)
##' @param verbose Should the output include all metadata? (logical,
##' default \code{FALSE})
##' @param exact Should exact matching be used for the value?
##' (logical, default \code{FALSE})
##' @param detailed Should a detailed report be provided? If
##' \code{TRUE} (default), the output will include metadata about
##' the study that include trees matching the property. Otherwise,
##' only information about the trees will be provided.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A data frame that summarizes the trees found (and their
##' associated studies) for the requested criteria. If a study has
##' more than 5 trees, the \code{tree_ids} of the first ones will
##' be shown, followed by \code{...} to indicate that more are
##' present.
##'
##' If \code{detailed=FALSE}, the data frame will include the
##' study ids of the study (\code{study_ids}), the number of trees
##' in this study that match the search criteria
##' (\code{n_matched_trees}), the tree ids that match the search
##' criteria (\code{match_tree_ids}).
##'
##' If \code{detailed=TRUE}, in addition of the fields listed
##' above, the data frame will also contain the total number of
##' trees associated with the study (\code{n_trees}), all the tree
##' ids associated with the study (\code{tree_ids}), the tree id
##' that is a potential candidate for inclusion in the synthetic
##' tree (if any) (\code{candidate}), the year the study was
##' published (\code{study_year}), the title of the study
##' (\code{title}), the DOI for the study (\code{study_doi}).
##'
##' @seealso \code{\link{studies_properties}} which lists properties
##' the studies can be searched on. \code{\link{list_trees}} for
##' listing the trees that match the query.
##' @export
##' @importFrom stats setNames
##' @examples
##' \dontrun{
##' res <- studies_find_trees(property="ot:ottTaxonName", value="Drosophila",
##' detailed=FALSE)
##' ## summary of the trees and associated studies that match this criterion
##' res
##' ## With metadata about the studies (default)
##' res <- studies_find_trees(property="ot:ottTaxonName", value="Drosophila",
##' detailed=TRUE)
##' ## The list of trees for each study that match the search criteria
##' list_trees(res)
##' ## the trees for a given study
##' list_trees(res, study_id = "pg_2769")
##' }
studies_find_trees <- function(property = NULL, value = NULL, verbose = FALSE,
exact = FALSE, detailed = TRUE, ...) {
.res <- .studies_find_trees(
property = property, value = value,
verbose = verbose, exact = exact, ...
)
study_ids <- vapply(
.res[["matched_studies"]],
function(x) x[["ot:studyId"]],
character(1)
)
n_matched_trees <- vapply(
.res[["matched_studies"]],
function(x) length(x[["matched_trees"]]),
numeric(1)
)
match_tree_ids <- lapply(
.res[["matched_studies"]],
function(x) {
sapply(
x[["matched_trees"]],
function(y) y[["ot:treeId"]]
)
}
)
# this one doesn't return all of the treeids. confusing, bc trees are what is wanted
# tree_str <- vapply(match_tree_ids, limit_trees, character(1))
tree_str <- sapply(match_tree_ids, function(x) paste(x, collapse = ", "))
res <- data.frame(study_ids, n_matched_trees,
match_tree_ids = tree_str,
stringsAsFactors = FALSE
)
if (detailed) {
meta <- summarize_meta(study_ids)
# the next bit seems really slow (JWB)
res <- merge(meta, res)
attr(res, "metadata") <- attr(meta, "metadata")
} else {
attr(res, "metadata") <- .res
}
attr(res, "found_trees") <- stats::setNames(match_tree_ids, study_ids)
class(res) <- c("matched_studies", class(res))
res
}
##' Returns the trees associated with a given study
##'
##' If \code{file_format} is missing, the function returns an object
##' of the class \code{phylo} from the \code{ape} package
##' (default), or an object of the class \code{nexml} from the
##' \code{RNeXML} package.
##'
##' Otherwise \code{file_format} can be either \code{newick},
##' \code{nexus}, \code{nexml} or \code{json}, and the function will
##' generate a file of the selected format. In this case, a file name
##' needs to be provided using the argument \code{file}. If a file
##' with the same name already exists, it will be silently
##' overwritten.
##'
##' @title Get all the trees associated with a particular study
##' @param study_id the study ID for the study of interest (character)
##' @param object_format the class of the object the query should
##' return (either \code{phylo} or \code{nexml}). Ignored if
##' \code{file_format} is specified.
##' @param file_format the format of the file to be generated
##' (\code{newick}, \code{nexus}, \code{nexml} or \code{json}).
##' @param file the file name where the output of the function will be
##' saved.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return if \code{file_format} is missing, an object of class
##' \code{phylo} or \code{nexml}, otherwise a logical indicating
##' whether the file was successfully created.
##' @seealso \code{\link{get_study_meta}}
##' @export
##' @importFrom jsonlite toJSON
##' @examples
##' \dontrun{
##' that_one_study <- get_study(study_id="pg_719", object_format="phylo")
##' if (require(RNeXML)) { ## if RNeXML is installed get the object directly
##' nexml_study <- get_study(study_id="pg_719", object_format="nexml")
##' } else { ## otherwise write it to a file
##' get_study(study_id="pg_719", file_format="nexml", file=tempfile(fileext=".nexml"))
##' }
##' }
get_study <- function(study_id = NULL, object_format = c("phylo", "nexml"),
file_format, file, ...) {
object_format <- match.arg(object_format)
if (!missing(file)) {
if (!missing(file_format)) {
file_format <- match.arg(file_format, c("newick", "nexus", "nexml", "json"))
res <- .get_study(study_id, format = file_format)
unlink(file)
if (identical(file_format, "json")) {
cat(jsonlite::toJSON(res), file = file)
} else {
cat(res, file = file)
}
return(invisible(file.exists(file)))
} else {
stop(sQuote("file_format"), " must be specified.")
}
} else if (identical(object_format, "phylo")) {
file_format <- "newick"
res <- .get_study(study_id = study_id, format = file_format, ...)
res <- phylo_from_otl(res)
} else if (identical(object_format, "nexml")) {
file_format <- "nexml"
res <- .get_study(study_id = study_id, format = file_format, ...)
res <- nexml_from_otl(res)
} else {
stop("Something is very wrong. Contact us.")
}
res
}
##' Returns a specific tree from within a study
##'
##' @title Study Tree
##' @param study_id the identifier of a study (character)
##' @param tree_id the identifier of a tree within the study
##' @param object_format the class of the object to be returned
##' (default and currently only possible value \code{phylo} from
##' the \code{ape} package).
##' @param tip_label the format of the tip
##' labels. \dQuote{\code{original_label}} (default) returns the
##' original labels as provided in the study,
##' \dQuote{\code{ott_id}} labels are replaced by their ott IDs,
##' \dQuote{\code{ott_taxon_name}} labels are replaced by their
##' Open Tree Taxonomy taxon name.
##' @param file_format the format of the file to be generated
##' (\code{newick} default, \code{nexus}, or \code{json}).
##' @param file the file name where the output of the function will be
##' saved.
##' @param deduplicate logical (default \code{TRUE}). If the tree
##' returned by the study contains duplicated taxon names, should they
##' be made unique? It is normally illegal for NEXUS/Newick tree
##' strings to contain duplicated tip names. This is a workaround to
##' circumvent this requirement. If \code{TRUE}, duplicated tip labels
##' will be appended \code{_1}, \code{_2}, etc.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return if \code{file_format} is missing, an object of class
##' \code{phylo}, otherwise a logical indicating whether the file
##' was successfully created.
##' @export
##' @importFrom jsonlite toJSON
##' @examples
##' \dontrun{
##' tree <- get_study_tree(study_id="pg_1144", tree_id="tree2324")
##'
##' ## comparison of the first few tip labels depending on the options used
##' head(get_study_tree(study_id="pg_1144", tree_id="tree2324", tip_label="original_label")$tip.label)
##' head(get_study_tree(study_id="pg_1144", tree_id="tree2324", tip_label="ott_id")$tip.label)
##' head(get_study_tree(study_id="pg_1144", tree_id="tree2324", tip_label="ott_taxon_name")$tip.label)
##' }
get_study_tree <- function(study_id = NULL, tree_id = NULL, object_format = c("phylo"),
tip_label = c("original_label", "ott_id", "ott_taxon_name"),
file_format, file, deduplicate = TRUE, ...) {
object_format <- match.arg(object_format)
tip_label <- match.arg(tip_label)
tip_label <- switch(tip_label,
original_labels = "ot:originallabel",
ott_id = "ot:ottid",
ott_taxon_name = "ot:otttaxonname"
)
if (!missing(file)) {
if (!missing(file_format)) {
file_format <- match.arg(file_format, c("newick", "nexus", "json"))
if (missing(file)) stop("You must specify a file to write your output")
res <- .get_study_tree(
study_id = study_id, tree_id = tree_id,
format = file_format, tip_label = tip_label, ...
)
unlink(file)
if (identical(file_format, "json")) {
cat(jsonlite::toJSON(res), file = file)
} else {
cat(res, file = file)
}
return(invisible(file.exists(file)))
} else {
stop(sQuote("file_format"), " must be specified.")
}
} else if (identical(object_format, "phylo")) {
file_format <- "newick"
res <- .get_study_tree(
study_id = study_id, tree_id = tree_id,
format = file_format, tip_label = tip_label, ...
)
res <- phylo_from_otl(res, dedup = deduplicate)
} else {
stop("Something is very wrong. Contact us.")
}
res
}
##' Retrieve metadata about a study in the Open Tree of Life datastore.
##'
##' \code{get_study_meta} returns a long list of attributes for the
##' studies that are contributing to the synthetic tree. To help with
##' the extraction of relevant information from this list, several
##' helper functions exists: \itemize{
##'
##' \item {get_tree_ids} { The identifiers of the trees
##' associated with the study }
##'
##' \item {get_publication} { The citation information of the
##' publication for the study. The DOI (or URL) for the study is
##' available as an attribute to the returned object (i.e.,
##' \code{attr(object, "DOI")} ) }.
##'
##' \item {candidate_for_synth} { The identifier of the tree(s) from
##' the study used in the synthetic tree. This is a subset of the
##' result of \code{get_tree_ids}.
##'
##' \item {get_study_year} { The year of publication of the study. }
##'
##' }
##' }
##'
##' @title Study Metadata
##' @param study_id the study identifier (character)
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @param sm an object created by \code{get_study_meta}
##' @return named-list containing the metadata associated with the
##' study requested
##' @export
##' @examples
##' \dontrun{
##' req <- get_study_meta("pg_719")
##' get_tree_ids(req)
##' candidate_for_synth(req)
##' get_publication(req)
##' get_study_year(req)
##' }
get_study_meta <- function(study_id, ...) {
res <- .get_study_meta(study_id = study_id, ...)
class(res) <- "study_meta"
attr(res, "study_id") <- study_id
res
}
##' @export
print.study_meta <- function(x, ...) {
cat("Metadata for OToL study ", attr(x, "study_id"), ". Contents:\n", sep = "")
cat(paste0(" $nexml$", names(x$nexml)), sep = "\n")
}
##' Retrieve subtree from a specific tree in the Open Tree of Life data store
##'
##' @title Study Subtree
##' @param study_id the study identifier (character)
##' @param tree_id the tree identifier (character)
##' @param object_format the class of the object returned by the
##' function (default, and currently only possibility \code{phylo}
##' from the \code{ape} package)
##' @param tip_label the format of the tip
##' labels. \dQuote{\code{original_label}} (default) returns the
##' original labels as provided in the study,
##' \dQuote{\code{ott_id}} labels are replaced by their ott IDs,
##' \dQuote{\code{ott_taxon_name}} labels are replaced by their
##' Open Tree Taxonomy taxon name.
##' @param file_format character, the file format to use to save the
##' results of the query (possible values, \sQuote{newick} or
##' \sQuote{nexus}).
##' @param file character, the path and file name where the output
##' should be written.
##' @param deduplicate logical (default \code{TRUE}). If the tree
##' returned by the study contains duplicated taxon names, should
##' they be made unique? It is normally illegal for NEXUS/Newick
##' tree strings to contain duplicated tip names. This is a
##' workaround to circumvent this requirement. If \code{TRUE},
##' duplicated tip labels will be appended \code{_1}, \code{_2},
##' etc.
##' @param subtree_id, either a node id that specifies a subtree or
##' \dQuote{ingroup} which returns the ingroup for this subtree.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @export
##' @examples
##' \dontrun{
##' small_tr <- get_study_subtree(study_id="pg_1144", tree_id="tree5800", subtree_id="node991044")
##' ingroup <- get_study_subtree(study_id="pg_1144", tree_id="tree5800", subtree_id="ingroup")
##' nexus_file <- tempfile(fileext=".nex")
##' get_study_subtree(study_id="pg_1144", tree_id="tree5800", subtree_id="ingroup", file=nexus_file,
##' file_format="nexus")
##' }
get_study_subtree <- function(study_id, tree_id, subtree_id, object_format = c("phylo"),
tip_label = c("original_label", "ott_id", "ott_taxon_name"),
file_format, file, deduplicate = TRUE, ...) {
object_format <- match.arg(object_format)
tip_label <- match.arg(tip_label)
tip_label <- switch(tip_label,
original_labels = "ot:originallabel",
ott_id = "ot:ottid",
ott_taxon_name = "ot:otttaxonname"
)
if (!missing(file)) {
if (!missing(file_format)) {
if (missing(file)) stop("You must specify a file to write your output")
file_format <- match.arg(file_format, c("newick", "nexus"))
res <- .get_study_subtree(
study_id = study_id, tree_id = tree_id,
subtree_id = subtree_id, format = file_format,
tip_label = tip_label, ...
)
unlink(file)
cat(res, file = file)
return(invisible(file.exists(file)))
} else {
stop(sQuote("file_format"), " must be specified.")
}
} else if (identical(object_format, "phylo")) {
file_format <- "newick"
res <- .get_study_subtree(
study_id = study_id, tree_id = tree_id,
subtree_id = subtree_id, format = file_format,
tip_label = tip_label, ...
)
res <- phylo_from_otl(res, dedup = deduplicate)
## NeXML should be possible for both object_format and file_format but it seems there
## is something wrong with the server at this time (FM - 2015-06-07)
## } else if (identical(object_format, "nexml")) {
## file_format <- "nexml"
## res <- .get_study_subtree(study_id, tree_id, subtree_id, format=file_format)
## res <- nexml_from_otl(res)
} else {
stop("Something is very wrong. Contact us.")
}
res
}
rotl/R/api-tnrs.R 0000644 0001762 0000144 00000003545 14441553346 013333 0 ustar ligges users ##' @importFrom jsonlite unbox
##' @importFrom httr content
## Match taxon names
.tnrs_match_names <- function(names = NULL, context_name = NULL,
do_approximate_matching = TRUE,
ids = NULL, include_suppressed = FALSE, ...) {
if (is.null(names)) {
stop("You must supply a ", sQuote("names"), " argument")
} else if (!is.character(names)) {
stop(
"Argument ", sQuote("names"), " must be of class ",
sQuote("character")
)
}
if (!is.null(ids)) {
if (length(ids) != length(names)) {
stop(
"Arguments ", sQuote("ids"), " and ",
sQuote("names"), " must be of the same length"
)
} else if (!is.character(ids)) {
stop(
"Argument ", sQuote("ids"), " must be of class ",
sQuote("character")
)
}
}
check_is_flag(do_approximate_matching)
check_is_flag(include_suppressed)
if (!is.null(context_name)) {
check_is_string(context_name)
context_name <- jsonlite::unbox(context_name)
}
q <- list(
names = names, context_name = context_name,
do_approximate_matching = jsonlite::unbox(do_approximate_matching),
ids = ids, include_suppressed = jsonlite::unbox(include_suppressed)
)
toKeep <- sapply(q, is.null)
q <- q[!toKeep]
res <- otl_POST("tnrs/match_names", body = q, ...)
res
}
##' @importFrom httr content
## Get OpenTree TNRS contexts
.tnrs_contexts <- function(...) {
res <- otl_POST("tnrs/contexts", body = list(), ...)
res
}
## Infer taxonomic context from a set of names
.tnrs_infer_context <- function(names = NULL, ...) {
if (is.null(names)) {
stop("Must supply a \'names\' argument")
} else if (!is.character(names)) {
stop("Argument \'names\' must be of class \"character\"")
}
q <- list(names = names)
res <- otl_POST("tnrs/infer_context", body = q, ...)
res
}
rotl/R/api-studies.R 0000644 0001762 0000144 00000013567 14441553346 014032 0 ustar ligges users ##' @importFrom jsonlite unbox
##' @importFrom httr content
## Return a list of studies from the OpenTree docstore that match a given properties
.studies_find_studies <- function(property = NULL, value = NULL, verbose = FALSE,
exact = FALSE, ...) {
if (!is.logical(verbose)) stop("Argument \'verbose\' should be logical")
if (!is.logical(exact)) stop("Argument \'exact\' should be logical")
req_body <- list()
if (!is.null(property)) {
if (!is.character(property)) {
stop("Argument \'property\' must be of class \"character\"")
}
req_body$property <- jsonlite::unbox(property)
} else {
stop("Must supply a \'property\' argument")
}
if (!is.null(value)) {
if (!is.character(value)) {
if (check_numeric(value)) {
value <- as.character(value)
} else {
stop("Argument \'value\' must be of class \"character\"")
}
}
req_body$value <- jsonlite::unbox(value)
} else {
stop("Must supply a \'value\' argument")
}
req_body$verbose <- jsonlite::unbox(verbose)
req_body$exact <- jsonlite::unbox(exact)
res <- otl_POST(
path = "studies/find_studies/",
body = req_body,
...
)
res
}
##' @importFrom jsonlite unbox
##' @importFrom httr content
## Return a list of trees from the OpenTree docstore that match a given properties
.studies_find_trees <- function(property = NULL, value = NULL, verbose = FALSE,
exact = FALSE, ...) {
if (!is.logical(verbose)) {
stop("Argument \'verbose\' must be of class \"logical\"")
}
if (!is.logical(exact)) {
stop("Argument \'exact\' must be of class \"logical\"")
}
req_body <- list()
if (!is.null(property)) {
if (!is.character(property)) {
stop("Argument \'property\' must be of class \"character\"")
}
req_body$property <- jsonlite::unbox(property)
} else {
stop("Must supply a \'property\' argument")
}
if (!is.null(value)) {
if (!is.character(value)) {
if (check_numeric(value)) {
value <- as.character(value)
} else {
stop("Argument \'value\' must be of class \"character\"")
}
}
req_body$value <- jsonlite::unbox(value)
} else {
stop("Must supply a \'value\' argument")
}
res <- otl_POST(
path = "studies/find_trees",
body = c(
req_body,
verbose = jsonlite::unbox(verbose),
exact = jsonlite::unbox(exact)
), ...
)
res
}
##' @importFrom httr content
## Return a list of properties that can be used to search studies and trees
.studies_properties <- function() {
res <- otl_POST(path = "studies/properties/", body = list())
res
}
##' @importFrom httr content
## Get a study from the OpenTree docstore
.get_study <- function(study_id = NULL, format = c("", "nexus", "newick", "nexml", "json"),
...) {
if (is.null(study_id)) {
stop("Must supply a \'study_id\' argument")
} else if (!is.character(study_id)) {
if (check_numeric(study_id)) {
study_id <- as.character(study_id)
} else {
stop("Argument \'study_id\' must be of class \"character\"")
}
}
format <- match.arg(format)
res <- otl_GET(
path = paste("study",
paste0(study_id, otl_formats(format)),
sep = "/"
),
...
)
res
}
##' @importFrom httr content
## Get a tree in a study from the OpenTree docstore
.get_study_tree <- function(study_id = NULL, tree_id = NULL, format = c("json", "newick", "nexus"),
tip_label = c("ot:originallabel", "ot:ottid", "ot:otttaxonname"),
...) {
if (is.null(study_id)) {
stop("Must supply a \'study_id\' argument")
} else if (!is.character(study_id)) {
stop("Argument \'study_id\' must be of class \"character\"")
}
if (is.null(tree_id)) {
stop("Must supply a \'tree\' argument")
} else if (!is.character(tree_id)) {
stop("Argument \'tree\' must be of class \"character\"")
}
format <- match.arg(format)
tip_label <- match.arg(tip_label)
tip_label <- paste0("/?tip_label=", tip_label)
tree_file <- paste0(tree_id, otl_formats(format), tip_label)
res <- otl_GET(path = paste("study", study_id, "tree", tree_file, sep = "/"), ...)
res
}
##' @importFrom httr content
.get_study_meta <- function(study_id, ...) {
otl_GET(path = paste("study", study_id, "meta", sep = "/"), ...)
}
##' @importFrom httr content
.get_study_subtree <- function(study_id, tree_id, subtree_id,
format = c("newick", "nexus", "nexml"),
tip_label = c("ot:originallabel", "ot:ottid", "ot:otttaxonname"),
...) {
if (is.null(study_id)) {
stop("Must supply a \'study_id\' argument")
} else if (!is.character(study_id)) {
stop("Argument \'study_id\' must be of class \"character\"")
}
if (is.null(tree_id)) {
stop("Must supply a \'tree\' argument")
} else if (!is.character(tree_id)) {
stop("Argument \'tree\' must be of class \"character\"")
}
if (is.null(subtree_id)) {
stop("Must supply a \'subtree\' argument")
} else if (!is.character(subtree_id)) {
stop("Argument \'subtree\' must be of class \"character\"")
}
format <- match.arg(format)
format <- otl_formats(format)
tip_label <- match.arg(tip_label)
url_stem <- paste("study", study_id, "subtree", paste0(tree_id, format), sep = "/")
res <- otl_GET(path = paste0(url_stem, "?subtree_id=", subtree_id, "&tip_label=", tip_label), ...)
res
}
### Let's not worry about those for now, as their results could be
### obtained using get_study_tree
get_study_otu <- function(study_id, otu = NULL, ...) {
otl_GET(path = paste("study", study_id, "otu", otu, sep = "/"), ...)
}
get_study_otus <- function(study_id, otus, ...) {
otl_GET(path = paste("study", study_id, "otu", otus, sep = "/"), ...)
}
get_study_otumap <- function(study_id, ...) {
otl_GET(path = paste("study", study_id, "otumap", sep = "/"))
}
rotl/R/deduplicate_labels.R 0000644 0001762 0000144 00000002562 14441553346 015401 0 ustar ligges users ## Create a vector (character) that contains the NEWICK tree strings
## found in a file
parse_newick <- function(file) {
trs <- readLines(file, warn = FALSE)
trs <- strsplit(trs, split = ";")
trs <- sapply(trs, function(x) gsub("^\\s+|\\s+$", "", x))
trs <- unlist(trs)
trs <- gsub("\\s", "_", trs)
trs <- trs[nchar(trs) > 0]
trs
}
## Internal function to be used by `deduplicate_labels` that:
## 1. identify duplicated labels
## 2. make them unique
## 3. replace the duplicated labels by their unique counterparts
dedup_lbl <- function(tr_str) {
tr_lbl <- tree_to_labels(tr_str, remove_quotes = TRUE)$tip_label
tr_lbl_unq <- make.unique(tr_lbl, sep = "_")
if (!identical(tr_lbl, tr_lbl_unq)) {
for (i in seq_along(tr_lbl)) {
tr_str <- sub(
paste0("([\\(|,]\\'?)\\Q", tr_lbl[i], "\\E(\\'?[:|\\)|,])"),
paste0("\\1", tr_lbl_unq[i], "\\2"), tr_str
)
}
warning("Some tip labels were duplicated and have been modified: ",
paste(tr_lbl[duplicated(tr_lbl)], collapse = ", "),
call. = FALSE
)
}
paste0(tr_str, ";")
}
## Main function: takes a file with potentially duplicated tip labels
## and reate a new file with unique labels
deduplicate_labels <- function(file) {
tr_strs <- parse_newick(file)
tr_dedup <- sapply(tr_strs, dedup_lbl)
tmp_tr <- tempfile()
cat(tr_dedup, file = tmp_tr, sep = "\n")
tmp_tr
}
rotl/R/tol.R 0000644 0001762 0000144 00000061053 14442033755 012370 0 ustar ligges users
.source_list <- function(tax, ...) {
if (!exists("source_id_map", tax)) {
## it should only be missing with tol_about when using
## include_source_list=FALSE
stop(
"Make sure that your object has been created using ",
sQuote("tol_about(include_source_list = TRUE)")
)
}
tt <- lapply(tax[["source_id_map"]], function(x) {
c(x[["study_id"]], x[["tree_id"]], x[["git_sha"]])
})
tt <- do.call("rbind", tt)
setNames(
as.data.frame(tt, stringsAsFactors = FALSE),
c("study_id", "tree_id", "git_sha")
)
}
##' Basic information about the Open Tree of Life (the synthetic tree)
##'
##' @title Information about the Tree of Life
##'
##' @details Summary information about the current draft tree of life,
##' including information about the list of trees and the taxonomy
##' used to build it. The object returned by \code{tol_about} can
##' be passed to the taxonomy methods (\code{tax_name()},
##' \code{tax_rank()}, \code{tax_sources()}, \code{ott_id}), to
##' extract relevant taxonomic information for the root of the
##' synthetic tree.
##'
##' @param include_source_list Logical (default =
##' \code{FALSE}). Return an ordered list of source trees.
##' @param tax an object created with a call to \code{tol_about}.
##' @param ... additional arguments to customize the API call (see
##' \code{\link{rotl}} for more information).
##'
##' @return An invisible list of synthetic tree summary statistics:
##'
##' \itemize{
##'
##' \item {date_created} {String. The creation date of the tree.}
##'
##' \item {num_source_studies} {Integer. The number of studies
##' (publications)used as sources.}
##'
##' \item {num_source_trees} {The number of trees used as sources
##' (may be >1 tree per study).}
##'
##' \item {taxonomy_version} {The Open Tree Taxonomy version used
##' as a source.}
##'
##' \item {filtered_flags} {List. Taxa with these taxonomy flags were
##' not used in construction of the tree.}
##'
##' \item {root} {List. Describes the root node:}
##' \itemize{
##' \item {node_id} {String. The canonical identifier of the node.}
##'
##' \item {num_tips} {Numeric. The number of descendant tips.}
##'
##' \item {taxon} {A list of taxonomic properties:}
##' \itemize{
##' \item {ott_id} {Numeric. The OpenTree Taxonomy ID (ott_id).}
##'
##' \item {name} {String. The taxonomic name of the queried node.}
##'
##' \item {unique_name} {String. The string that uniquely
##' identifies the taxon in OTT.}
##'
##' \item {rank} {String. The taxonomic rank of the taxon in OTT.}
##'
##' \item {tax_sources} {List. A list of identifiers for taxonomic
##' sources, such as other taxonomies, that define taxa judged
##' equivalent to this taxon.}
##' }
##' }
##'
##' \item {source_list} {List. Present only if
##' \code{include_source_list} is \code{TRUE}. The sourceid
##' ordering is the precedence order for synthesis, with
##' relationships from earlier trees in the list having priority
##' over those from later trees in the list. See
##' \code{source_id_map} below for study details.}
##'
##' \item {source_id_map} {Named list of lists. Present only if
##' \code{include_source_list} is \code{TRUE}. Names correspond to
##' the \sQuote{sourceids} used in \code{source_list}
##' above. Source trees will have the following properties:}
##'
##' \itemize{
##' \item {git_sha} {String. The git SHA identifying a particular source
##' version.}
#'
##' \item {tree_id} {String. The tree id associated with the study id used.}
##'
##' \item {study_id} {String. The study identifier. Will typically include
##' a prefix ("pg_" or "ot_").}
##' }
##'
##' \item {synth_id} {The unique string for this version of the tree.}
##' }
##' @seealso \code{\link{source_list}} to explore the list of studies
##' used in the synthetic tree (see example).
##'
##' @examples
##' \dontrun{
##' res <- tol_about()
##' tax_sources(res)
##' ott_id(res)
##' studies <- source_list(tol_about(include_source_list=TRUE))}
##' @rdname tol_about
##' @export
tol_about <- function(include_source_list = FALSE, ...) {
res <- .tol_about(include_source_list = include_source_list, ...)
class(res) <- c("tol_summary", class(res))
res
}
##' @export
print.tol_summary <- function(x, ...) {
cat("\nOpenTree Synthetic Tree of Life.\n\n")
cat("Tree version: ", x$synth_id, "\n", sep = "")
cat("Taxonomy version: ", x$taxonomy, "\n", sep = "")
cat("Constructed on: ", x$date_created, "\n", sep = "")
cat("Number of terminal taxa: ", x$root$num_tips, "\n", sep = "")
cat("Number of source trees: ", x$num_source_trees, "\n", sep = "")
cat("Number of source studies: ", x$num_source_studies, "\n", sep = "")
cat("Source list present: ", ifelse(exists("source_list", x), "true", "false"), "\n", sep = "")
cat("Root taxon: ", x$root$taxon$name, "\n", sep = "")
cat("Root ott_id: ", x$root$taxon$ott_id, "\n", sep = "")
cat("Root node_id: ", x$root$node_id, "\n", sep = "")
}
tol_about_method_factory <- function(.f) {
function(tax, ...) {
res <- list(.f(tax[["root"]][["taxon"]]))
names(res) <- .tax_unique_name(tax[["root"]][["taxon"]])
res <- add_otl_class(res, .f)
res
}
}
##' @export
##' @rdname tol_about
tax_rank.tol_summary <- tol_about_method_factory(.tax_rank)
##' @export
##' @rdname tol_about
tax_sources.tol_summary <- tol_about_method_factory(.tax_sources)
##' @export
##' @rdname tol_about
unique_name.tol_summary <- tol_about_method_factory(.tax_unique_name)
##' @export
##' @rdname tol_about
tax_name.tol_summary <- tol_about_method_factory(.tax_name)
##' @export
##' @rdname tol_about
ott_id.tol_summary <- tol_about_method_factory(.tax_ott_id)
##' @export
##' @rdname source_list
source_list.tol_summary <- .source_list
##' Most Recent Common Ancestor for a set of nodes
##'
##' @title MRCA of taxa from the synthetic tree
##'
##' @details Get the MRCA of a set of nodes on the current synthetic
##' tree. Accepts any combination of node ids and ott ids as
##' input. Returns information about the most recent common
##' ancestor (MRCA) node as well as the most recent taxonomic
##' ancestor (MRTA) node (the closest taxonomic node to the MRCA
##' node in the synthetic tree; the MRCA and MRTA may be the same
##' node). If they are the same, the taxonomic information will be
##' in the \code{mrca} slot, otherwise they will be in the
##' \code{nearest_taxon} slot of the list. If any of the specified
##' nodes is not in the synthetic tree an error will be returned.
##'
##' Taxonomic methods (\code{tax_sources()}, \code{ott_id()},
##' \code{unique_name()}, ...) are available on the objects
##' returned by \code{tol_mrca()}. If the MRCA node is MRTA, the
##' name of the object returned by these methods will start with
##' \sQuote{ott}, otherwise it will start with \sQuote{mrca}.
##'
##' @param ott_ids Numeric vector. The ott ids for which the MRCA is desired.
##' @param node_ids Character vector. The node ids for which the MRCA is desired.
##' @param tax an object returned by \code{tol_mrca()}.
##' @param ... additional arguments to customize the API call (see
##' \code{\link{rotl}} for more information).
##'
##' @return An invisible list of the MRCA node properties:
##'
##' \itemize{
##'
##' \item {mrca} {List of node properties.}
##'
##' \itemize{
##' \item {node_id} {String. The canonical identifier of the node.}
##'
##' \item {num_tips} {Numeric. The number of descendant tips.}
##'
##' \item {taxon} {A list of taxonomic properties. Only returned if
##' the queried node is a taxon. (If the node is not a taxon, a
##' \code{nearest_taxon} list is returned (see below)).}
##'
##' \itemize{
##' \item {ott_id} {Numeric. The OpenTree Taxonomy ID (ottID).}
##'
##' \item {name} {String. The taxonomic name of the queried node.}
##'
##' \item {unique_name} {String. The string that uniquely
##' identifies the taxon in OTT.}
##'
##' \item {rank} {String. The taxonomic rank of the taxon in OTT.}
##'
##' \item {tax_sources} {List. A list of identifiers for taxonomic
##' sources, such as other taxonomies, that define taxa judged
##' equivalent to this taxon.}
##' }
##'
##' The following properties list support/conflict for the node across
##' synthesis source trees. All properties involve sourceid keys and
##' nodeid values (see \code{source_id_map} below) Not all properties are
##' are present for every node.
##'
##' \item {partial_path_of} {List. The edge below this synthetic tree node
##' is compatible with the edge below each of these input tree nodes (one
##' per tree). Each returned element is reported as sourceid:nodeid.}
##'
##' \item {supported_by} {List. Input tree nodes (one per tree) that support
##' this synthetic tree node. Each returned element is reported as
##' sourceid:nodeid.}
##'
##' \item {terminal} {List. Input tree nodes (one per tree) that are equivalent
##' to this synthetic tree node (via an exact mapping, or the input tree
##' terminal may be the only terminal descended from this synthetic tree node.
##' Each returned element is reported as sourceid:nodeid.}
##'
##' \item {conflicts_with} {Named list of lists. Names correspond to
##' sourceid keys. Each list contains input tree node ids (one or more per tree)
##' that conflict with this synthetic node.}
##' }
##'
##' \item {nearest_taxon} {A list of taxonomic properties of the nearest rootward
##' taxon node to the MRCA node. Only returned if the MRCA node is a not taxon
##' (otherwise the \code{taxon} list above is returned).}
##'
##' \itemize{
##' \item {ott_id} {Numeric. The OpenTree Taxonomy ID (ottID).}
##'
##' \item {name} {String. The taxonomic name of the queried node.}
##'
##' \item {unique_name} {String. The string that uniquely
##' identifies the taxon in OTT.}
##'
##' \item {rank} {String. The taxonomic rank of the taxon in OTT.}
##'
##' \item {tax_sources} {List. A list of identifiers for taxonomic
##' sources, such as other taxonomies, that define taxa judged
##' equivalent to this taxon.}
##' }
##'
##' \item {source_id_map} {Named list of lists. Names correspond to the
##' sourceid keys used in the support/conflict properties of the \code{mrca}
##' list above. Source trees will have the following properties:}
##'
##' \itemize{
##' \item {git_sha} {The git SHA identifying a particular source
##' version.}
##'
##' \item {tree_id} {The tree id associated with the study id used.}
##'
##' \item {study_id} {The study identifier. Will typically include
##' a prefix ("pg_" or "ot_").}
##' }
##' The only sourceid that does not correspond to a source tree is the taxonomy,
##' which will have the name "ott"+`taxonomy_version`, and the value is the
##' ott_id of the taxon in that taxonomy version. "Taxonomy" will only ever
##' appear in \code{supported_by}.
##'
##' }
##'
##' @examples
##' \dontrun{
##' birds_mrca <- tol_mrca(ott_ids=c(412129, 119214))
##' ott_id(birds_mrca)
##' tax_sources(birds_mrca)
##' }
##' @rdname tol_mrca
##' @export
tol_mrca <- function(ott_ids = NULL, node_ids = NULL, ...) {
res <- .tol_mrca(ott_ids = ott_ids, node_ids = node_ids, ...)
class(res) <- c("tol_mrca", class(res))
return(res)
}
##' @export
print.tol_mrca <- function(x, ...) {
cat("\nOpenTree MRCA node.\n\n")
cat("Node id: ", x$mrca$node_id, "\n", sep = "")
cat("Number of terminal descendants: ", x$mrca$num_tips, "\n", sep = "")
if (is_taxon(x[["mrca"]][["taxon"]])) {
cat("Is taxon: TRUE\n")
cat("Name: ", x$mrca$taxon$name, "\n", sep = "")
cat("ott id: ", x$mrca$taxon$ott_id, "\n", sep = "")
} else {
cat("Is taxon: FALSE\n")
cat("Nearest taxon:\n")
cat(" Name: ", x$nearest_taxon$name, "\n", sep = "")
cat(" ott id: ", x$nearest_taxon$ott_id, "\n", sep = "")
}
}
tol_mrca_method_factory <- function(.f) {
function(tax, ...) {
if (is_taxon(tax[["mrca"]][["taxon"]])) {
res <- list(.f(tax[["mrca"]][["taxon"]]))
names(res) <- .tax_unique_name(tax[["mrca"]][["taxon"]])
attr(res, "taxon_type") <- "mrca"
} else {
res <- list(.f(tax[["nearest_taxon"]]))
names(res) <- .tax_unique_name(tax[["nearest_taxon"]])
attr(res, "taxon_type") <- "nearest_taxon"
}
res <- add_otl_class(res, .f)
res
}
}
##' @export
##' @rdname tol_mrca
tax_sources.tol_mrca <- tol_mrca_method_factory(.tax_sources)
##' @export
##' @rdname tol_mrca
unique_name.tol_mrca <- tol_mrca_method_factory(.tax_unique_name)
##' @export
##' @rdname tol_mrca
tax_name.tol_mrca <- tol_mrca_method_factory(.tax_name)
##' @export
##' @rdname tol_mrca
tax_rank.tol_mrca <- tol_mrca_method_factory(.tax_rank)
##' @export
##' @rdname tol_mrca
ott_id.tol_mrca <- tol_mrca_method_factory(.tax_ott_id)
##' @export
##' @rdname tol_mrca
source_list.tol_mrca <- .source_list
##' Extract a subtree from the synthetic tree from an Open Tree node id.
##'
##' @title Extract a subtree from the synthetic tree
##'
##' @details Given a node, return the subtree of the synthetic tree descended
##' from that node. The start node may be specified using either a node id
##' or an ott id, but not both. If the specified node is not in the
##' synthetic tree an error will be returned. There is a size limit of
##' 25000 tips for this method.
##'
##' @param ott_id Numeric. The ott id of the node in the tree that should
##' serve as the root of the tree returned.
##' @param node_id Character. The node id of the node in the tree that should
##' serve as the root of the tree returned.
##' @param label_format Character. Defines the label type; one of
##' \dQuote{\code{name}}, \dQuote{\code{id}}, or
##' \dQuote{\code{name_and_id}} (the default).
##' @param file If specified, the function will write the subtree to a
##' file in newick format.
##' @param ... additional arguments to customize the API call (see
##' \code{\link{rotl}} for more information).
##'
##' @return If no value is specified to the \code{file} argument
##' (default), a phylogenetic tree of class \code{phylo}.
##' Otherwise, the function returns invisibly a logical indicating
##' whether the file was successfully created.
##'
##' @examples
##' \dontrun{
##' res <- tol_subtree(ott_id=241841)}
##' @export
tol_subtree <- function(ott_id = NULL, node_id = NULL, label_format = NULL,
file, ...) {
res <- .tol_subtree(
ott_id = ott_id, node_id = node_id,
label_format = label_format, ...
)
if (!missing(file)) {
unlink(file)
cat(res$newick, file = file)
return(invisible(file.exists(file)))
} else {
phy <- phylo_from_otl(res)
return(phy)
}
}
##' Return the induced subtree on the synthetic tree that relates a list of nodes.
##'
##' @title Subtree from the Open Tree of Life
##'
##' @details Return a tree with tips corresponding to the nodes identified in
##' the input set that is consistent with the topology of the current
##' synthetic tree. This tree is equivalent to the minimal subtree
##' induced on the draft tree by the set of identified nodes.
##'
##' @param ott_ids Numeric vector. OTT ids indicating nodes to be used as tips
##' in the induced tree.
##' @param node_ids Character vector. Node ids indicating nodes to be used as
##' tips in the induced tree.
##' @param label_format Character. Defines the label type; one of
##' \dQuote{\code{name}}, \dQuote{\code{id}}, or \dQuote{\code{name_and_id}}
##' (the default).
##' @param file If specified, the function will write the subtree to a file in
##' newick format.
##' @param ... additional arguments to customize the API call (see
##' \code{\link{rotl}} for more information).
##'
##' @return If no value is specified to the \code{file} argument
##' (default), a phylogenetic tree of class \code{phylo}.
##'
##' Otherwise, the function returns invisibly a logical indicating
##' whether the file was successfully created.
##'
##' Note that the tree returned when specifying a file name with the
##' \code{file} argument is the \dQuote{raw} Newick string returned by Open
##' Tree of Life. This string contains singleton nodes, and therefore will
##' be different from the tree returned as a \code{phylo} object which will
##' not contain these singleton nodes.
##'
##' @examples
##' \dontrun{
##' ## Result as a `phylo` object
##' res <- tol_induced_subtree(ott_ids = c(292466, 267845, 316878, 102710))
##'
##' ## Raw Newick string from Open Tree of Life
##' tree_file <- tempfile(fileext = ".tre")
##' tol_induced_subtree(ott_ids = c(292466, 267845, 316878, 102710),
##' file=tree_file)
##'
##' }
##' @export
tol_induced_subtree <- function(ott_ids = NULL, node_ids = NULL, label_format = NULL,
file, ...) {
res <- .tol_induced_subtree(
ott_ids = ott_ids, node_ids = node_ids,
label_format = label_format, ...
)
if (!missing(file)) {
unlink(file)
cat(res$newick, file = file)
return(file.exists(file))
} else {
phy <- phylo_from_otl(res)
return(phy)
}
}
##' Strip OTT ids from tip labels
##' @param tip_labels a character vector containing tip labels (most
##' likely the \code{tip.label} element from a tree returned by
##' \code{\link{tol_induced_subtree}}
##' @param remove_underscores logical (defaults to FALSE). If set to
##' TRUE underscores in tip labels are converted to spaces
##' @return A character vector containing the contents of
##' \code{tip_labels} with any OTT ids removed.
##'
##' @examples
##' \dontrun{
##' genera <- c("Perdix", "Setophaga", "Cinclus", "Struthio")
##' tr <- tol_induced_subtree(ott_ids=c(102710, 285198, 267845, 292466))
##' tr$tip.label %in% genera
##' tr$tip.label <- strip_ott_ids(tr$tip.label)
##' tr$tip.label %in% genera
##' }
##' @export
strip_ott_ids <- function(tip_labels, remove_underscores = FALSE) {
stripped <- sub("_ott\\d+$", "", tip_labels)
if (remove_underscores) {
return(gsub("_", " ", stripped))
}
stripped
}
##' Get summary information about a node in the synthetic tree
##'
##' @title Node info
##'
##' @details Returns summary information about a node in the graph. The
##' node of interest may be specified using either a node id or an
##' taxon id, but not both. If the specified node or OTT id is not
##' in the graph, an error will be returned.
##'
##' If the argument \code{include_lineage=TRUE} is used, you can
##' use \code{tax_lineage()} or \code{tol_lineage} to return the
##' taxonomic information or the node information for all the
##' ancestors to this node, down to the root of the tree.
##'
##'
##' @param ott_id Numeric. The OpenTree taxonomic identifier.
##' @param node_id Character. The OpenTree node identifier.
##' @param include_lineage Logical (default = FALSE). Whether to return the
##' lineage of the node from the synthetic tree.
##' @param ... additional arguments to customize the API call (see
##' ?rotl for more information)
##'
##' @return \code{tol_node_info} returns an invisible list of summary
##' information about the queried node:
##'
##' \itemize{
##'
##' \item {node_id} {String. The canonical identifier of the node.}
##'
##' \item {num_tips} {Numeric. The number of descendant tips.}
##'
##' \item {partial_path_of} {List. The edge below this synthetic tree node
##' is compatible with the edge below each of these input tree nodes (one
##' per tree). Each returned element is reported as sourceid:nodeid.}
##'
##' \item {query} { The node id that resolved to this node. This can differ
##' from the node_id field if the query id is not canonical. }
##'
##' \item {taxon} {A list of taxonomic properties. Only returned if
##' the queried node is a taxon. Each source has:}
##'
##' \itemize{
##' \item {ott_id} {Numeric. The OpenTree Taxonomy ID (ottID).}
##'
##' \item {name} {String. The taxonomic name of the queried node.}
##'
##' \item {unique_name} {String. The string that uniquely
##' identifies the taxon in OTT.}
##'
##' \item {rank} {String. The taxonomic rank of the taxon in OTT.}
##'
##' \item {tax_sources} {List. A list of identifiers for taxonomic
##' sources, such as other taxonomies, that define taxa judged
##' equivalent to this taxon.}
##' }
##'
##' The following properties list support/conflict for the node across
##' synthesis source trees. All properties involve sourceid keys and
##' nodeid values (see \code{source_id_map} below).
##'
##' \item {supported_by} {List. Input tree nodes (one per tree) that support
##' this synthetic tree node. Each returned element is reported as
##' sourceid:nodeid.}
##'
##' \item {terminal} {List. Input tree nodes (one per tree) that are
##' equivalent to this synthetic tree node (via an exact mapping, or the
##' input tree terminal may be the only terminal descended from this
##' synthetic tree node. Each returned element is reported as
##' sourceid:nodeid.}
##'
##' \item {conflicts_with} {Named list of lists. Names correspond to
##' sourceid keys. Each list contains input tree node ids (one or more per
##' tree) that conflict with this synthetic node.}
##' }
##'
##' \code{tol_lineage} and \code{tax_lineage} return data
##' frames. \code{tol_lineage} indicate for each ancestor its
##' node identifier, the number of tips descending from that
##' node, and whether it corresponds to a taxonomic level.
##'
##' @examples
##' \dontrun{
##' birds <- tol_node_info(ott_id=81461, include_lineage=TRUE)
##' source_list(birds)
##' tax_rank(birds)
##' ott_id(birds)
##' tax_lineage(birds)
##' tol_lineage(birds)}
##' @export
tol_node_info <- function(ott_id = NULL, node_id = NULL, include_lineage = FALSE, ...) {
res <- .tol_node_info(
ott_id = ott_id, node_id = node_id,
include_lineage = include_lineage, ...
)
class(res) <- c("tol_node", class(res))
return(res)
}
tol_node_method_factory <- function(.f) {
function(tax, ...) {
if (exists("taxon", tax)) {
res <- setNames(
list(.f(tax[["taxon"]])),
.tax_unique_name(tax[["taxon"]])
)
} else if (exists("node_id", tax)) {
res <- setNames(tax[["node_id"]], tax[["node_id"]])
} else {
res <- NA
}
res <- add_otl_class(res, .f)
res
}
}
##' @export
print.tol_node <- function(x, ...) {
cat("\nOpenTree node.\n\n")
cat("Node id: ", x$node_id, "\n", sep = "")
cat("Number of terminal descendants: ", x$num_tips, "\n", sep = "")
if (is_taxon(x[["taxon"]])) {
cat("Is taxon: TRUE\n")
cat("Name: ", x$taxon$name, "\n", sep = "")
cat("Rank: ", x$taxon$rank, "\n", sep = "")
cat("ott id: ", x$taxon$ott_id, "\n", sep = "")
} else {
cat("Is taxon: FALSE\n")
}
}
##' @export
##' @param tax an object returned by \code{tol_node_info}.
##' @rdname tol_node_info
tax_rank.tol_node <- tol_node_method_factory(.tax_rank)
##' @export
##' @rdname tol_node_info
tax_sources.tol_node <- tol_node_method_factory(.tax_sources)
##' @export
##' @rdname tol_node_info
unique_name.tol_node <- tol_node_method_factory(.tax_unique_name)
##' @export
##' @rdname tol_node_info
tax_name.tol_node <- tol_node_method_factory(.tax_name)
##' @export
##' @rdname tol_node_info
ott_id.tol_node <- tol_node_method_factory(.tax_ott_id)
##' @export
##' @rdname tol_node_info
source_list.tol_node <- .source_list
##' @export
##' @rdname tol_node_info
tax_lineage.tol_node <- function(tax, ...) {
check_lineage(tax)
lg <- lapply(tax[["lineage"]], function(x) {
if (exists("taxon", x)) {
build_lineage(x[["taxon"]])
} else {
NULL
}
})
lg <- do.call("rbind", lg)
as.data.frame(lg, stringsAsFactors = FALSE)
}
##' @export
##' @rdname tol_node_info
tol_lineage.tol_node <- function(tax, ...) {
check_lineage(tax)
lg <- lapply(tax[["lineage"]], function(x) {
c(
"node_id" = x[["node_id"]],
"num_tips" = x[["num_tips"]],
"is_taxon" = exists("taxon", x)
)
})
lg <- do.call("rbind", lg)
as.data.frame(lg, stringsAsFactors = FALSE)
}
rotl/R/taxonomy.R 0000644 0001762 0000144 00000024553 14441553346 013456 0 ustar ligges users ##' Summary information about the Open Tree Taxonomy (OTT)
##'
##' Return metadata and information about the taxonomy
##' itself. Currently, the available metadata is fairly sparse, but
##' includes (at least) the version, and the location from which the
##' complete taxonomy source files can be downloaded.
##'
##' @title Information about the Open Tree Taxonomy
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A list with the following properties:
##' \itemize{
##'
##' \item {weburl} {String. The release page for this version
##' of the taxonomy.}
##'
##' \item {author} {String. The author string.}
##'
##' \item {name} {String. The name of the taxonomy.}
##'
##' \item {source} {String. The full identifying information for
##' this version of the taxonomy.}
##'
##' \item {version} {String. The version number of the taxonomy.}
##' }
##' @examples
##' \dontrun{
##' taxonomy_about()
##' }
##' @export
taxonomy_about <- function(...) {
res <- .taxonomy_about(...)
return(res)
}
##' Information about taxa.
##'
##' Given a vector of ott ids, \code{taxonomy_taxon_info} returns
##' information about the specified taxa.
##'
##' The functions \code{tax_rank}, \code{tax_name}, and
##' \code{synonyms} can extract this information from an object
##' created by the \code{taxonomy_taxon_info()}.
##'
##' @title Taxon information
##' @param ott_ids the ott ids of the taxon of interest (numeric or
##' character containing only numbers)
##' @param include_children whether to include information about all
##' the children of this taxon. Default \code{FALSE}.
##' @param include_lineage whether to include information about all
##' the higher level taxa that include the \code{ott_ids}.
##' Default \code{FALSE}.
##' @param include_terminal_descendants whether to include the list of
##' terminal \code{ott_ids} contained in the \code{ott_ids}
##' provided.
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @param tax an object generated by the \code{taxonomy_taxon_info}
##' function
##' @return \code{taxonomy_taxon_info} returns a list detailing
##' information about the taxa. \code{tax_rank} and
##' \code{tax_name} return a vector. \code{synonyms} returns a
##' list whose elements are the synonyms for each of the
##' \code{ott_id} requested.
##'
##' @seealso \code{\link{tnrs_match_names}} to obtain \code{ott_id}
##' from a taxonomic name.
##' @examples
##' \dontrun{
##' req <- taxonomy_taxon_info(ott_id=515698)
##' tax_rank(req)
##' tax_name(req)
##' synonyms(req)
##' }
##' @export
taxonomy_taxon_info <- function(ott_ids, include_children = FALSE,
include_lineage = FALSE,
include_terminal_descendants = FALSE, ...) {
res <- lapply(ott_ids, function(x) {
.taxonomy_taxon_info(
ott_id = x,
include_children = include_children,
include_lineage = include_lineage,
include_terminal_descendants = include_terminal_descendants,
...
)
})
names(res) <- ott_ids
class(res) <- "taxon_info"
return(res)
}
##' Given an ott id, return the inclusive taxonomic subtree descended
##' from the specified taxon.
##'
##' If the output of this function is exported to a file, the only
##' possible value for the \code{output_format} argument is
##' \dQuote{\code{newick}}. If the file provided already exists, it
##' will be silently overwritten.
##'
##' @title Taxonomy subtree
##' @param ott_id The ott id of the taxon of interest.
##' @param output_format the format of the object to be returned. See
##' the \sQuote{Return} section.
##' @param label_format Character. Defines the label type; one of
##' \dQuote{\code{name}}, \dQuote{\code{id}}, or
##' \dQuote{\code{name_and_id}} (the default).
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @param file the file name where to save the output of the
##' function. Ignored unless \code{output_format} is set to
##' \dQuote{\code{phylo}}.
##' @return If the \code{file} argument is missing: \itemize{
##'
##' \item{\dQuote{\code{taxa}}} { a list of the taxa names
##' (species) in slot \code{tip_label}, and higher-level taxonomy
##' (e.g., families, genera) in slot \code{edge_label}, descending
##' from the taxa corresponding to the \code{ott_id} provided. }
##'
##' \item{\dQuote{\code{newick}}} { a character vector containing
##' the newick formatted string corresponding to the taxonomic
##' subtree for the \code{ott_id} provided. }
##'
##' \item{\dQuote{\code{phylo}}} { an object of the class
##' \code{phylo} from the \code{ape} package. }
##'
##' \item{\dQuote{\code{raw}}} { the direct output from the API,
##' i.e., a list with an element named \sQuote{newick} that
##' contains the subtree as a newick formatted string. }
##'
##' }
##'
##' If a \code{file} argument is provided (and
##' \code{output_format} is set to \dQuote{\code{phylo}}), a
##' logical indicating whether the file was successfully created.
##'
##' @examples
##' \dontrun{
##' req <- taxonomy_subtree(ott_id=515698)
##' plot(taxonomy_subtree(ott_id=515698, output_format="phylo"))
##' }
##' @export
taxonomy_subtree <- function(ott_id = NULL,
output_format = c("taxa", "newick", "phylo", "raw"),
label_format = NULL, file, ...) {
output_format <- match.arg(output_format)
res <- .taxonomy_subtree(ott_id = ott_id, label_format = label_format, ...)
if (!missing(file) && !identical(output_format, "newick")) {
warning(
sQuote("file"),
" argument is ignored, you can only write newick tree strings to a file."
)
}
if (identical(output_format, "raw")) {
return(res)
} else if (identical(output_format, "newick")) {
res <- res$newick
if (!missing(file)) {
unlink(file)
cat(res, file = file)
invisible(return(file.exists(file)))
}
} else if (identical(output_format, "phylo")) {
res <- phylo_from_otl(res)
} else { ## in all other cases use tree_to_labels
res <- tree_to_labels(res)
}
return(res)
}
##' Taxonomic Least Inclusive Common Ancestor (MRCA)
##'
##' Given a set of OTT ids, get the taxon that is the most recent common
##' ancestor (the MRCA) of all the identified taxa.
##'
##' @title Taxonomic MRCA
##' @param ott_ids a vector of ott ids for the taxa whose MRCA is to
##' be found (numeric).
##' @param tax an object generated by the \code{taxonomy_mrca}
##' function
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return \itemize{
##'
##' \item{\code{taxonomy_mrca}} { returns a list about the
##' taxonomic information relating to the MRCA for the ott_ids
##' provided. }
##'
##' \item{\code{tax_rank}} { returns a character vector of the
##' taxonomic rank for the MRCA. }
##'
##' \item{\code{tax_name}} { returns a character vector the
##' Open Tree Taxonomy name for the MRCA. }
##'
##' \item{\code{ott_id}} { returns a numeric vector of the ott id
##' for the MRCA. }
##'
##' }
##' @examples
##' \dontrun{
##' req <- taxonomy_mrca(ott_ids=c(515698,590452,643717))
##' tax_rank(req)
##' tax_name(req)
##' ott_id(req)
##' }
##' @export
taxonomy_mrca <- function(ott_ids = NULL, ...) {
res <- .taxonomy_mrca(ott_ids = ott_ids, ...)
class(res) <- c("taxon_mrca", class(res))
return(res)
}
### methods for taxonomy_taxon_info ---------------------------------------------
taxon_info_method_factory <- function(.f) {
function(tax, ...) {
res <- lapply(tax, .f)
names(res) <- vapply(tax, .tax_unique_name, character(1))
res <- add_otl_class(res, .f)
res
}
}
##' @export
##' @rdname taxonomy_taxon_info
tax_rank.taxon_info <- taxon_info_method_factory(.tax_rank)
##' @export
##' @rdname taxonomy_taxon_info
tax_name.taxon_info <- taxon_info_method_factory(.tax_name)
##' @export
##' @rdname taxonomy_taxon_info
unique_name.taxon_info <- taxon_info_method_factory(.tax_unique_name)
##' @export
##' @rdname taxonomy_taxon_info
synonyms.taxon_info <- taxon_info_method_factory(.tax_synonyms)
##' @export
##' @rdname taxonomy_taxon_info
ott_id.taxon_info <- taxon_info_method_factory(.tax_ott_id)
##' @export
##' @rdname taxonomy_taxon_info
tax_sources.taxon_info <- taxon_info_method_factory(.tax_sources)
##' @export
##' @rdname taxonomy_taxon_info
is_suppressed.taxon_info <- taxon_info_method_factory(.tax_is_suppressed)
##' @export
##' @rdname taxonomy_taxon_info
flags.taxon_info <- taxon_info_method_factory(.tax_flags)
### methods for taxonomy_mrca ---------------------------------------------------
taxon_mrca_method_factory <- function(.f) {
function(tax, ...) {
res <- list(.f(tax[["mrca"]]))
names(res) <- .tax_unique_name(tax[["mrca"]])
res <- add_otl_class(res, .f)
res
}
}
##' @export
##' @rdname taxonomy_mrca
tax_rank.taxon_mrca <- taxon_mrca_method_factory(.tax_rank)
##' @export
##' @rdname taxonomy_mrca
tax_name.taxon_mrca <- taxon_mrca_method_factory(.tax_name)
##' @export
##' @rdname taxonomy_mrca
ott_id.taxon_mrca <- taxon_mrca_method_factory(.tax_ott_id)
##' @export
##' @rdname taxonomy_mrca
unique_name.taxon_mrca <- taxon_mrca_method_factory(.tax_unique_name)
##' @export
##' @rdname taxonomy_mrca
tax_sources.taxon_mrca <- taxon_mrca_method_factory(.tax_sources)
##' @export
##' @rdname taxonomy_mrca
flags.taxon_mrca <- taxon_mrca_method_factory(.tax_flags)
##' @export
##' @rdname taxonomy_mrca
is_suppressed.taxon_mrca <- taxon_mrca_method_factory(.tax_is_suppressed)
### method for extracting higher taxonomy from taxonomy_taxon_info calls -------
get_lineage <- function(tax) {
check_lineage(tax)
lg <- lapply(tax[["lineage"]], build_lineage)
lg <- do.call("rbind", lg)
as.data.frame(lg, stringsAsFactors = FALSE)
}
build_lineage <- function(x) {
c(
"rank" = .tax_rank(x),
"name" = .tax_name(x),
"unique_name" = .tax_unique_name(x),
"ott_id" = .tax_ott_id(x)
)
}
check_lineage <- function(tax) {
if (!exists("lineage", tax)) {
stop(
"The object needs to be created using ",
sQuote("include_lineage=TRUE")
)
}
}
##' @export
##' @rdname tax_lineage
tax_lineage.taxon_info <- function(tax, ...) {
lapply(tax, get_lineage)
}
rotl/R/external_data.R 0000644 0001762 0000144 00000011506 14441553346 014405 0 ustar ligges users ##' Get external identifiers for data associated with an Open Tree study
##'
##' Data associated with studies contributing to the Open Tree synthesis may
##' be available from other databases. In particular, trees and alignments
##' may be available from treebase and DNA sequences and bibliographic
##' information associated with a given study may be available from the NCBI.
##' This function retrieves that information for a given study.
##'
##' @param study_id An open tree study ID
##' @return A study_external_data object (which inherits from a list) which
##' contains some of the following.
##' @return doi, character, the DOI for the paper describing this study
##' @return external_data_url, character, a URL to an external data repository
##' (e.g. a treebase entry) if one exists.
##' @return pubmed_id character, the unique ID for this study in the NCBI's pubmed database
##' @return popset_ids character, vector of IDs for the NCBI's popset database
##' @return nucleotide_ids character, vector of IDs for the NCBI's nucleotide database
##' @seealso studies_find_studies (used to discover study IDs)
##' @importFrom httr parse_url
##' @importFrom rentrez entrez_search
##' @importFrom rentrez entrez_link
##' @examples
##' \dontrun{
##' flies <- studies_find_studies(property="ot:focalCladeOTTTaxonName", value="Drosophilidae")
##' study_external_IDs(flies[2,]$study_ids)
##' }
##' @export
study_external_IDs <- function(study_id) {
meta <- get_study_meta(study_id)
data_deposit <- meta[["nexml"]][["^ot:dataDeposit"]][["@href"]]
url <- attr(get_publication(meta), "DOI")
doi <- parse_url(url)$path
pmid <- get_pmid(doi, study_id)
res <- list(
doi = doi,
pubmed_id = pmid,
external_data_url = data_deposit
)
if (!is.null(pmid)) {
res$popset_ids <- entrez_link(dbfrom = "pubmed", db = "popset", id = pmid)[["links"]][["pubmed_popset"]]
res$nucleotide_ids <- entrez_link(dbfrom = "pubmed", db = "nuccore", id = pmid)[["links"]][["pubmed_nuccore"]]
}
structure(res, class = c("study_external_data", "list"), id = study_id)
}
##' Get external identifiers for data associated with an Open Tree taxon
##'
##' The Open Tree taxonomy is a synthesis of multiple reference taxonomies. This
##' function retrieves identifiers to external taxonomic records that have
##' contributed the rank, position and definition of a given Open Tree taxon.
##'
##' @param taxon_id An open tree study ID
##' @return a data.frame in which each row represents a unique record in an
##' external database. The column "source" provides and abbreviated name for the
##' database, and "id" the unique ID for the record.
##' @seealso tnrs_matchnames, which can be used to search for taxa by name.
##' @seealso taxonomy_taxon, for more information about a given taxon.
##' @examples
##' \dontrun{
##' gibbon_IDs <- taxon_external_IDs(712902)
##' }
##' @export
taxon_external_IDs <- function(taxon_id) {
taxon_info <- taxonomy_taxon_info(taxon_id)
srcs <- taxon_info[[1]][["tax_sources"]]
res <- do.call(rbind.data.frame, strsplit(unlist(srcs), ":"))
names(res) <- c("source", "id")
res
}
#' @export
print.study_external_data <- function(x, ...) {
cat("External data identifiers for study", attr(x, "study_id"), "\n")
cat(" $doi: ", x[["doi"]], "\n")
if (!is.null(x$pubmed_id)) {
cat(" $pubmed_id: ", x[["pubmed_id"]], "\n")
}
if (!is.null(x$popset_ids)) {
cat(" $popset_ids: vector of", length(x[["popset_ids"]]), "IDs \n")
}
if (!is.null(x$nucleotide_ids)) {
cat(" $nucleotide_ids: vector of", length(x[["nucleotide_ids"]]), "IDs\n")
}
if (nchar(x[["external_data_url"]]) > 0) {
cat(" $external_data_url", x[["external_data_url"]], "\n")
}
cat("\n")
}
## Maybe include these functions to get summary information about a
## set of linked sequences?
# summarize_nucleotide_data <- function(id_vector){
# summs <- entrez_summary(db="nuccore", id=id_vector)
# interesting <- extract_from_esummary(summs, c("uid", "title", "slen", "organism", "completeness"), simplify=FALSE)
# do.call(rbind.data.frame, interesting)
# }
#
# summarize_popset_data <- function(id_vector){
# summs <- entrez_summary(db="popset", id=id_vector)
# interesting <- extract_from_esummary(summs, c("uid", "title"), simplify=FALSE)
# do.call(rbind.data.frame, interesting)
# }
#
# Un-exported function to convert doi->pmid. Also takes study_id as an argument in
# order to provide a helpful error message when 0 or >1 pmids are returned.
get_pmid <- function(doi, study_id) {
pubmed_search <- entrez_search(db = "pubmed", term = paste0(doi, "[DOI]"))
if (length(pubmed_search$ids) == 0) {
warning("Could not find PMID for study'", study_id, "', skipping NCBI data")
return(NULL)
}
if (length(pubmed_search$ids) > 1) {
warning("Found more than one PMID matching study'", study_id, "', skipping NCBI data")
return(NULL)
}
pubmed_search$ids
}
rotl/R/tree_to_labels.R 0000644 0001762 0000144 00000002655 14441553346 014562 0 ustar ligges users ## Function to extract tip and edge labels from newick formatted strings
## useful when the tree is too small to be read in by ape/rncl.
## tr needs to be a newick formatted tree string
## - missing tips are removed (OK for OTL as it won't happen)
tree_to_labels <- function(tr, remove_quotes = TRUE) {
n_right <- unlist(gregexpr("\\)", tr))
n_left <- unlist(gregexpr("\\(", tr))
if (n_right[1] == -1) n_right <- 0 else n_right <- length(n_right)
if (n_left[1] == -1) n_left <- 0 else n_left <- length(n_left)
if (!identical(n_right, n_left)) {
stop("invalid newick string, numbers of ( and ) don't match")
}
## remove white spaces
tr <- gsub("\\s+", "", tr)
## remove branch lengths
tr <- gsub(":[0-9]+(\\.[0-9]+)?", "", tr)
## TODO?: remove comments
if (n_right < 1) {
## if only 1 tip
tip_lbl <- gsub(";$", "", tr)
edge_lbl <- character(0)
} else {
## extract edge labels
edge_lbl <- unlist(strsplit(tr, ")"))
edge_lbl <- grep("^[^\\(]", edge_lbl, value = T)
edge_lbl <- gsub("(,|;).*$", "", edge_lbl)
edge_lbl <- edge_lbl[nzchar(edge_lbl)]
## extract tips
tip_lbl <- unlist(strsplit(tr, ","))
tip_lbl <- gsub("^\\(*", "", tip_lbl)
tip_lbl <- gsub("\\).*$", "", tip_lbl)
tip_lbl <- tip_lbl[nzchar(tip_lbl)]
}
if (remove_quotes) {
tip_lbl <- gsub("^(\\\"|\\\')(.+)(\\\'|\\\")$", "\\2", tip_lbl)
}
list(tip_label = tip_lbl, edge_label = edge_lbl)
}
rotl/R/tax_utils.R 0000644 0001762 0000144 00000004137 14441553346 013610 0 ustar ligges users ## all extended-taxon-descriptors have:
## - ott_id
## - name
## - rank
## - unique_name
## - tax_sources
## and they may have
## - flags
## - synonyms
## - is_suppressed
## builds the functions to access the content of the taxon descriptors.
## slot: the name of the list element we need to access
## flatten: if the list element is a list, make it a vector
## optional: is the slot found in all taxon descriptors or only in some
tax_access_factory <- function(slot, flatten, optional) {
function(tax) {
if ((!exists(slot, tax))) {
if (optional) {
warning("This object doesn't have ", sQuote(slot), call. = FALSE)
return(NULL)
} else {
stop("Invalid taxon object", call. = FALSE)
}
} else {
if (flatten) {
unlist(tax[[slot]])
} else {
tax[[slot]]
}
}
}
}
.tax_ott_id <- tax_access_factory("ott_id", flatten = FALSE, optional = FALSE)
.tax_name <- tax_access_factory("name", flatten = FALSE, optional = FALSE)
.tax_rank <- tax_access_factory("rank", flatten = FALSE, optional = FALSE)
.tax_sources <- tax_access_factory("tax_sources",
flatten = TRUE,
optional = FALSE
)
.tax_unique_name <- tax_access_factory("unique_name",
flatten = FALSE,
optional = FALSE
)
## optional
.tax_flags <- tax_access_factory("flags", flatten = TRUE, optional = TRUE)
.tax_is_suppressed <- tax_access_factory("is_suppressed",
flatten = FALSE,
optional = TRUE
)
.tax_synonyms <- tax_access_factory("synonyms", flatten = TRUE, optional = TRUE)
## Does the slot element represent a taxon?
is_taxon <- function(slot) {
if (all(c(
"ott_id", "name", "rank", "tax_sources",
"unique_name"
) %in% names(slot))) {
TRUE
} else {
FALSE
}
}
### adds a class to the objects returned by the methods
add_otl_class <- function(res, .f) {
## we need a prefix to avoid class name conflict
## apparently the class "name" already exists
class(res) <- c(
paste0("otl_", as.list(environment(.f))[["slot"]]),
class(res)
)
res
}
##' @export
`[.otl_ott_id` <- function(x, i, ...) {
r <- NextMethod()
class(r) <- class(x)
r
}
rotl/R/is_in_tree.R 0000644 0001762 0000144 00000004215 14441553346 013711 0 ustar ligges users ##' Some valid taxonomic names do not occur in the Synthetic
##' Tree. This convenience function allows you to check whether a
##' given Open Tree Taxonomy identifier (OTT id) is in the tree. A taxonomic
##' name may not occur in the synthetic tree because (1) it is an
##' extinct or invalid taxon, or (2) it is part of a group that is not
##' monophyletic in the tree.
##'
##' @title Check that OTT ids occur in the Synthetic Tree
##' @param ott_ids a vector of Open Tree Taxonomy identifiers
##' @param ... additional arguments to customize the API request (see
##' \code{\link{rotl}} package documentation).
##' @return A named logical vector. \code{TRUE} indicates that the OTT
##' id is in the synthetic tree, and \code{FALSE} that it is not.
##' @examples
##' \dontrun{
##' plant_families <- c("Asteraceae", "Solanaceae", "Poaceae", "Amaranthaceae",
##' "Zamiaceae", "Araceae", "Juncaceae")
##' matched_names <- tnrs_match_names(plant_families)
##' ## This fails because some ott ids are not in the tree
##' ## plant_tree <- tol_induced_subtree(ott_id(matched_names))
##' ## So let's check which ones are actually in the tree first:
##' in_tree <- is_in_tree(ott_id(matched_names))
##' ## This now works:
##' plant_tree <- tol_induced_subtree(ott_id(matched_names)[in_tree])
##' }
##'
##' @export
is_in_tree <- function(ott_ids, ...) UseMethod("is_in_tree")
##' @export
is_in_tree.otl_ott_id <- function(ott_ids, ...) {
in_tree <- vapply(ott_ids, function(ottid) {
test <- try(tol_node_info(ottid, ...), silent = TRUE)
if (inherits(test, "try-error")) {
if (grepl("was not found", test) &&
grepl(paste0("'ott", ottid, "'"), test)) {
} else {
warning("something seems off, check your internet connection?")
}
return(FALSE)
} else {
ott_id(test)[[1]] == ottid
}
}, logical(1), USE.NAMES = TRUE)
in_tree
}
##' @export
is_in_tree.default <- function(ott_ids, ...) {
chk_ids <- vapply(ott_ids, check_numeric, logical(1))
if (all(chk_ids)) {
is_in_tree.otl_ott_id(ott_ids)
} else {
stop("Invalid format for ott_ids (they should look like numbers)")
}
}
rotl/R/rotl-package.R 0000644 0001762 0000144 00000003562 14442060264 014137 0 ustar ligges users ##' An Interface to the Open Tree of Life API
##'
##' The Open Tree of Life is an NSF funded project that is generating
##' an online, comprehensive phylogenetic tree for 1.8 million
##' species. \code{rotl} provides an interface that allows you to
##' query and retrieve the parts of the tree of life that is of
##' interest to you.
##'
##' \code{rotl} provides function to most of the end points the API
##' provides. The documentation of the API is available at:
##' \url{https://github.com/OpenTreeOfLife/opentree/wiki/Open-Tree-of-Life-APIs}
##'
##' @section Customizing API calls:
##'
##' All functions that use API end points can take 2 arguments to
##' customize the API call and are passed as \code{...} arguments.
##'
##' \itemize{
##'
##' \item{ \code{otl_v} } { This argument controls which version
##' of the API your call is using. The default value for this
##' argument is a call to the non-exported function
##' \code{otl_version()} which returns the current version of the
##' Open Tree of Life APIs (v2).}
##'
##' \item{ \code{dev_url} } { This argument controls whether to use
##' the development version of the API. By default, \code{dev_url}
##' is set to \code{FALSE}, using \code{dev_url = TRUE} in your
##' function calls will use the development version.}
##'
##' }
##'
##' For example, to use the development version of the API, you
##' could use: \code{tnrs_match_names("anas", dev_url=TRUE)}
##'
##' Additional arguments can also be passed to the
##' \code{\link[httr]{GET}} and \code{\link[httr]{POST}} methods.
##'
##'
##' @section Acknowledgments:
##'
##' This package was started during the Open Tree of Life Hackathon
##' organized by the OpenTree of Life, the NESCent Hackathon
##' Interoperability Phylogenetic group, and Arbor.
##'
##' @name rotl
##' @docType package
##' @import ape
NULL
rotl/NEWS.md 0000644 0001762 0000144 00000016240 14442554002 012333 0 ustar ligges users # rotl 3.1.0
## Breaking change
* `tnrs_match_names` now returns the taxon with the highest matching score
instead of the one with the lowest OTT id (issues #127 and #143 reported by
@ajrominger).
# rotl 3.0.14
* Make sure that the package fails gracefully when there is no
internet connection.
* The vignettes are now pre-computed.
# rotl 3.0.12
* The default argument `context_name` for the function `tnrs_match_names` was
changed from `NULL` to `All life`. This changes is made to address what could
lead to unexpected results. Previously, the context was inferred based on the
first match when several names were provided (see #134 reported by @LunaSare,
and https://github.com/OpenTreeOfLife/feedback/issues/528)/
* The "Suggest" dependency `fulltext` was removed following its archival from
CRAN.
# rotl 3.0.11
## Other changes
* When none of the names provided to `tnrs_match_names` had a match in the Open
Tree of Life, an error was thrown and nothing was returned. To make the
behavior of the function more consistent with other situations, when none of
the names provided have a match, a tibble is returned and a warning is issued.
## Bug Fix
* When attempting to match a name that did not exist in the tree, an error was
thrown (bug #128, PR #129, @daijiang)
# rotl 3.0.10
* Small fixes following updates to the Open Tree of Life API (no visible change
for users).
* Updated documentation to reflect new value in output of `tol_node_info()`.
# rotl 3.0.9
* Small fixes following updates to the Open Tree of Life API.
# rotl 3.0.7
* Minor update to vignette to address change to TNRS endpoint (underscores can't
be included in the taxon names anymore).
# rotl 3.0.6
* Minor update to address warnings seen on CRAN.
# rotl 3.0.5
## New features
* The data types in the data frame returned by `tnrs_match_names` are
consistent, and remain the same even after using `update()`.
## Other changes
* Small internal changes that reflect changes in the data structures returned by
the API.
# rotl 3.0.4
## New features
* To improve stability of results across releases of the Open Tree Taxonomy, the
TNRS match with the lowest OTT id is returned instead of the first one in case
a name is shared across multiple domains (related to #88)
* A warning is issued when users attempt to use TNRS on duplicated names.
## Other changes
* Fix typos and workaround broken API to retrieve supplementary materials in
vignette.
# rotl 3.0.3
## New features
* The function `get_study_subtree` gains the argument `tip_label` to control the
formatting of the tip labels, #90, reported by @bomeara
* The new function `is_in_tree` takes a list of OTT ids (i.e., the output of
`ott_id()`), and returns a vector of logical indicating whether they are
included in the synthetic tree (workaround #31).
## Bug fixes
* The function `get_study_subtree` ignored the argument `subtree_id`, #89
reported by @bomeara
## Other chaanges
* `citation("rotl")` now includes the reference to the Open Tree of Life
publication.
* The "How to use rotl?" vignette was updated to document the behavior of v3 of
the OTL API which returns an HTTP error code 400 when the request for induced
subtree includes taxa that are not in the synthetic tree (fix #84)
# rotl 3.0.1
* Fix tests and vignette to reflect changes accompanying release 6.1 of the
synthetic tree
* Add section in vignette "How to use rotl?" about how to get the higher
taxonomy from a given taxon.
* Add `CITATION` file with MEE manuscript information (#82)
# rotl 3.0.0
* `rotl` now interacts with v3.0 of the Open Tree of Life APIs. The
documentation has been updated to reflect the associated changes. More
information about the v3.0 of the Open Tree of Life APIs can be found
[on their wiki](https://github.com/OpenTreeOfLife/germinator/wiki/Open-Tree-of-Life-Web-APIs).
## New features
* New methods: `tax_sources`, `is_suppressed`, `tax_rank`, `unique_name`,
`name`, `ott_id`, for objects returned by `tnrs_match_names()`,
`taxonomy_taxon_info()`, `taxonomy_taxon_mrca()`, `tol_node_info()`,
`tol_about()`, and `tol_mrca()`. Each of these methods have their own class.
* New method `tax_lineage()` to extract the higher taxonomy from an object
returned by `taxonomy_taxon_info()` (initially suggested by Matt Pennell, #57).
* New method `tol_lineage()` to extract the nodes towards the root of the tree.
* New print methods for `tol_node_info()` and `tol_mrca()`.
* New functions `study_external_IDs()` and `taxon_external_IDs()` that return
the external identifiers for a study and associated trees (e.g., DOI, TreeBase
ID); and the identifiers of taxon names in taxonomic databases. The vignette
"Data mashup" includes an example on how to use it.
* The function `strip_ott_id()` gains the argument `remove_underscores` to remove
underscores from tips in trees returned by OTL.
## Changes
* Rename method `ott_taxon_name()` to `tax_name()` for consistency.
* Rename method `synth_sources()` and `study_list()` to `source_list()`.
* Refactor how result of query is checked and parsed (invisible to the user).
## Bug fixes
* Fix bug in `studies_find_studies()`, the arguments `verbose` and `exact` were
ignored.
* The argument `only_current` has been dropped for the methods associated with
objects returned by `tnrs_match_names()`
* The print method for `tnrs_context()` duplicated some names.
* `inspect()`, `update()` and `synonyms()` methods for `tnrs_match_names()` did
not work if the query included unmatched taxa.
# rotl 0.5.0
* New vignette: `meta-analysis`
* Added arguments `include_lineage` and `list_terminal_descendants` to
`taxonomy_taxon()`
* Improve warning and format of the result if one of the taxa requested doesn't
match anything `tnrs_match_names`.
* In the data frame returned by `tnrs_match_names`, the columns
`approximate_match`, `is_synonym` and `is_deprecated` are now `logical`
(instead of `character`) [issue #54]
* New utility function `strip_ott_ids` removes OTT id information from
a character vector, making it easier to match tip labels in trees returned by
`tol_induced_subtree` to taxonomic names in other data sources. This function
can also remove underscores from the taxon names.
* New method `list_trees` returns a list of tree ids associated with
studies. The function takes the output of `studies_find_studies` or
`studies_find_trees`.
* `studies_find_studies` and `studies_find_trees` gain argument `detailed`
(default set to `TRUE`), that produces a data frame summarizing information
(title of the study, year of publication, DOI, ids of associated trees, ...)
about the studies matching the search criteria.
* `get_study_tree` gains argument `deduplicate`. When `TRUE`, if the tree
returned for a given study contains duplicated tip labels, they will be made
unique before being parsed by NCL by appending a suffix (`_1`, `_2`, `_3`,
etc.). (#46, reported by @bomeara)
* New method `get_study_year` for objects of class `study_meta` that returns the
year of publication of the study.
* A more robust approach is used by `get_tree_ids` to identify the tree ids in
the metadata returned by the API
# rotl 0.4.1
* Initial CRAN release on July, 24th 2015
rotl/MD5 0000644 0001762 0000144 00000013070 14442613417 011551 0 ustar ligges users ec305475fd716447e866235efe97efdf *DESCRIPTION
41ba68f62ff5f58f05f96a0dbd08b282 *LICENSE
06d664adfd9fbca93f72d9400ff8280f *NAMESPACE
0869ad8f281eaa4cd87e616a81377a16 *NEWS.md
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43115aed8b4fdac25a06760851d0fe76 *vignettes/meta-analysis.Rmd.orig
e8d7c6b7f750c816a25e2ab76d2147c7 *vignettes/rotl.Rmd
fa8ac9390987ec33a882c3573d48749b *vignettes/rotl.Rmd.orig
da8cb33974fa673158fea8ccf2d8bbac *vignettes/vignette.css
rotl/inst/ 0000755 0001762 0000144 00000000000 14442555441 012217 5 ustar ligges users rotl/inst/doc/ 0000755 0001762 0000144 00000000000 14442555441 012764 5 ustar ligges users rotl/inst/doc/meta-analysis.Rmd 0000644 0001762 0000144 00000052032 14442034213 016166 0 ustar ligges users ---
title: "Using the Open Tree synthesis in a comparative analysis"
author: "David Winter"
date: "2023-06-13"
output:
rmarkdown::html_vignette:
css: vignette.css
vignette: >
%\VignetteIndexEntry{Using the Open Tree synthesis in a comparative analysis}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
%\VignetteEncoding{UTF-8}
---
## Phylogenetic Comparative Methods
The development of phylogenetic comparative methods has made phylogenies and
important source of data in fields as diverse as ecology, genomic and medicine.
Comparative methods can be used to investigate patterns in the evolution of
traits or the diversification of lineages. In other cases a phylogeny is treated
as a "nuisance parameter", allowing with the autocorrelation created by the shared
evolutionary history of the different species included to be controlled for.
In many cases finding a tree that relates the species for which trait data are
available is a rate-limiting step in such comparative analyses. Here we show
how the synthetic tree provided by Open Tree of Life (and made available in R via
`rotl`) can help to fill this gap.
## A phylogenetic meta-analysis
To demonstrate the use of `rotl` in a comparative analysis, we will partially
reproduce the results of [Rutkowska _et al_ 2014](https://doi.org/10.1111/jeb.12282).
Very briefly, this study is a meta-analysis summarising the results of multiple
studies testing for systematic differences in the size of eggs which contain
male and female offspring. Such a difference might mean that birds invest more
heavily in one sex than the other.
Because this study involves data from 51 different species, Rutkowska _et al_
used a phylogenetic comparative approach to account for the shared evolutionary
history among some of the studied-species.
### Gather the data
If we are going to reproduce this analysis, we will first need to gather the
data. Thankfully, the data is available as supplementary material from the
publisher's website. We provide a copy of this data with the package:
```r
library(rotl)
```
```r
## This dataset is available from the publisher's study website:
egg_data <- read.csv(system.file("extdata", "egg.csv", package = "rotl"),
stringsAsFactors = FALSE
)
## }
head(egg_data)
```
```
## animal Spp Lndim Measure Neggs
## 1 Zonotrichia_leucophrys White-crowned sparrow 0.000000000 volume 294
## 2 Passer_domesticus House sparrow 0.009407469 volume 149
## 3 Serinus_canaria Canary 0.000000000 volume 52
## 4 Turdus_merula European blackbird 0.021189299 volume 82
## 5 Agelaius_phoeniceus Red-winged blackbird 0.218316086 volume 394
## 6 Quiscalus_mexicanus Great-tailed grackle 0.281894985 mass 822
## Nclutches ESr Type StudyID Year D EN Zr
## 1 73 0.14004594 stat Mead1987 1987 3.421918 85.91673 0.14097244
## 2 31 0.11175203 stat Cordero2000 2000 4.045161 36.83413 0.11222075
## 3 21 0.49679140 stat Leitner2006 2006 2.180952 23.84279 0.54503712
## 4 54 0.38598540 stat Martyka2010 2010 1.414815 57.95812 0.40707397
## 5 106 0.07410136 raw Weatherhead1985 1985 3.173585 124.14982 0.07423744
## 6 205 0.05178834 raw Teather1989 1989 3.407805 241.21099 0.05183471
## VZr
## 1 0.012060292
## 2 0.029555954
## 3 0.047978211
## 4 0.018195675
## 5 0.008254242
## 6 0.004197959
```
The most important variable in this dataset is `Zr`, which is a [normalized
effect size](https://en.wikipedia.org/wiki/Fisher_transformation) for difference
,in size between eggs that contain males and females. Values close to zero come
from studies that found the sex of an egg's inhabitant had little effect in its size,
while large positive or negative values correspond to studies with substantial
sex biases (towards males and females respectively). Since this is a
meta-analysis we should produce the classic [funnel plot](https://en.wikipedia.org/wiki/Funnel_plot)
with effects-size on the y-axis and precision (the inverse of the sample
standard error) on the x-axis. Here we calculate precision from the sample
variance (`Vzr`):
```r
plot(1 / sqrt(egg_data$VZr), egg_data$Zr,
pch = 16,
ylab = "Effect size (Zr)",
xlab = "Precision (1/SE)",
main = "Effect sizes for sex bias in egg size among 51 brid species"
)
```
In order to use this data later on we need to first convert it to a standard
`data.frame`. We can also convert the `animal` column (the species names) to
lower case, and remove the underscores in their names, which will make it easier to match names later on:
```r
egg_data <- as.data.frame(egg_data)
## Convert taxon names to lower case
egg_data$animal <- tolower(egg_data$animal)
## Let's remove the underscores (_) from the taxon names
egg_data$animal <- gsub("_", " ", egg_data$animal)
```
### Find the species in OTT
We can use the OTL synthesis tree to relate these species. To do so we first need to
find Open Tree Taxonomy (OTT) IDs for each species. We can do that with the
Taxonomic Name Resolution Service function `tnrs_match_names`:
```r
taxa <- tnrs_match_names(unique(egg_data$animal), context = "Animals")
```
```
## Warning in tnrs_match_names(unique(egg_data$animal), context = "Animals"):
## partial argument match of 'context' to 'context_name'
```
```r
head(taxa)
```
```
## search_string unique_name approximate_match ott_id
## 1 zonotrichia leucophrys Zonotrichia leucophrys FALSE 265553
## 2 passer domesticus Passer domesticus FALSE 745175
## 3 serinus canaria Serinus canaria FALSE 464865
## 4 turdus merula Turdus merula FALSE 568572
## 5 agelaius phoeniceus Agelaius phoeniceus FALSE 226605
## 6 quiscalus mexicanus Quiscalus mexicanus FALSE 743411
## is_synonym flags number_matches
## 1 FALSE 1
## 2 FALSE 1
## 3 FALSE sibling_higher 1
## 4 FALSE 1
## 5 FALSE 1
## 6 FALSE 1
```
All of these species are in OTT, but a few of them go by different names in the
Open Tree than we have in our data set. Because the tree `rotl` fetches
will have Open Tree names, we need to create a named vector that maps the names
we have for each species to the names Open Tree uses for them:
```r
taxon_map <- structure(taxa$search_string, names = taxa$unique_name)
```
Now we can use this map to retrieve "data set names" from "OTT names":
```r
taxon_map["Anser caerulescens"]
```
```
## Anser caerulescens
## "chen caerulescens"
```
### Get a tree
Now we can get the tree. There are really too many tips here to show nicely, so
we will leave them out of this plot
```r
tr <- tol_induced_subtree(ott_id(taxa)[is_in_tree(ott_id(taxa))])
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: mrcaott246ott5481, mrcaott246ott7145, mrcaott246ott928360,
## mrcaott246ott1858, mrcaott246ott2907, mrcaott246ott3600042, mrcaott246ott7113,
## Passeriformes ott1041547, mrcaott246ott3212, mrcaott246ott428578,
## mrcaott246ott44866, mrcaott246ott5929, mrcaott246ott32658,
## mrcaott246ott310390, mrcaott246ott176461, mrcaott246ott22325,
## mrcaott246ott10351, mrcaott246ott193904, mrcaott246ott5934, mrcaott246ott1566,
## mrcaott1566ott3598440, mrcaott1566ott496009, mrcaott1566ott59716,
## mrcaott1566ott32651, mrcaott1566ott24297, mrcaott1566ott45312,
## mrcaott1566ott22300, mrcaott22300ott35350, mrcaott22300ott547548,
## mrcaott22300ott909199, mrcaott22300ott67150, mrcaott22300ott130294,
## mrcaott22300ott3598245, mrcaott22300ott416087, mrcaott22300ott629342,
## mrcaott22300ott416089, mrcaott22300ott107840, mrcaott364210ott451069,
## mrcaott364210ott3598839, mrcaott3598839ott5341363, mrcaott19467ott431648,
## mrcaott19467ott252687, mrcaott19467ott401023, mrcaott19467ott233446,
## mrcaott19467ott168078, mrcaott19467ott1046624, mrcaott19467ott161293,
## mrcaott19467ott46396, mrcaott46396ott46398, mrcaott46398ott168083,
## mrcaott168083ott431676, mrcaott2175ott35326, mrcaott2175ott968664,
## mrcaott2175ott259082, mrcaott2175ott59905, mrcaott2175ott2224,
## mrcaott2224ott366470, mrcaott3364ott310375, mrcaott3364ott3370,
## mrcaott4083ott35042, mrcaott4083ott370807, mrcaott4083ott469177,
## mrcaott4083ott18592, mrcaott4083ott139823, mrcaott4083ott11712,
## mrcaott4083ott95949, Erythrura ott465905, mrcaott24017ott389884,
## mrcaott24017ott24025, mrcaott24025ott596763, mrcaott24025ott453058,
## mrcaott24025ott141501, mrcaott141501ott966119, mrcaott141501ott865472,
## mrcaott141501ott389883, mrcaott105913ott311555, mrcaott311555ott1082386,
## mrcaott311555ott708327, mrcaott708327ott966122, mrcaott4088ott8371,
## mrcaott4088ott5616, mrcaott5616ott28339, mrcaott5616ott6023,
## mrcaott6023ott243614, mrcaott6023ott101225, mrcaott6023ott125079,
## mrcaott125079ott463026, mrcaott125079ott765405, Zonotrichia (genus in
## domain Eukaryota) ott789032, mrcaott125079ott265547, mrcaott125079ott265554,
## mrcaott5620ott254662, mrcaott5620ott29804, mrcaott29804ott449580,
## mrcaott29804ott449562, mrcaott29804ott86894, mrcaott29804ott93045,
## mrcaott93045ott264496, mrcaott264496ott264500, mrcaott264500ott3597163,
## mrcaott264500ott283668, Quiscalus ott743410, mrcaott283673ott673386,
## mrcaott283673ott741944, mrcaott283673ott735243, mrcaott213448ott213452,
## mrcaott213448ott1009279, mrcaott213448ott213451, mrcaott213454ott430627,
## mrcaott430627ott498751, mrcaott430627ott617797, mrcaott430627ott3597159,
## mrcaott99175ott364331, Xanthocephalus ott364336, mrcaott6366ott28332,
## mrcaott6366ott88283, mrcaott6366ott341465, mrcaott6366ott157599,
## mrcaott6366ott178457, mrcaott6366ott405215, mrcaott6366ott238142,
## mrcaott6366ott6375, mrcaott6375ott119724, mrcaott6375ott238137,
## mrcaott238137ott328909, mrcaott238137ott464865, Haemorhous ott3601758,
## mrcaott9416ott840030, mrcaott9416ott96148, mrcaott9416ott7068473, Passer
## ott515158, mrcaott9416ott407769, mrcaott9416ott25628, mrcaott9416ott407764,
## mrcaott9416ott68955, mrcaott9416ott73636, mrcaott1488ott72472,
## mrcaott1488ott63797, mrcaott1488ott284404, mrcaott1488ott107463,
## mrcaott1488ott17016, mrcaott1488ott44217, mrcaott1488ott16185,
## mrcaott16185ott274931, Hirundinidae ott897681, mrcaott16185ott67916,
## mrcaott67916ott67921, mrcaott67916ott368059, mrcaott67916ott67920,
## Delichon ott922719, mrcaott107476ott177058, mrcaott107476ott271376,
## mrcaott107476ott337752, mrcaott337752ott7661935, mrcaott337752ott337762,
## mrcaott337752ott7068616, mrcaott2375ott73144, mrcaott2375ott124085,
## mrcaott2375ott71358, mrcaott2375ott814750, mrcaott2375ott61147,
## mrcaott84656ott123763, mrcaott123763ott728471, mrcaott123763ott258794,
## mrcaott4820ott409116, mrcaott4820ott17162, mrcaott4820ott20998,
## mrcaott4820ott20989, mrcaott4820ott58860, mrcaott4820ott23690,
## mrcaott4820ott20996, mrcaott4820ott11462, mrcaott4820ott140440,
## mrcaott4820ott197505, mrcaott4820ott75978, mrcaott4820ott11315,
## mrcaott4820ott5933, mrcaott5933ott60465, mrcaott5933ott25637,
## mrcaott25637ott199843, mrcaott25637ott473431, mrcaott25637ott686166,
## mrcaott25637ott111993, mrcaott25637ott183621, mrcaott183621ott501241,
## mrcaott501241ott3598010, mrcaott501241ott597018, mrcaott686165ott686168,
## mrcaott60456ott662804, mrcaott60456ott75990, Pica ott776480, Falconiformes
## ott212187, Falconidae ott212186, mrcaott47588ott225286, mrcaott47588ott748842,
## mrcaott47588ott201377, mrcaott201377ott773020, Cerchneis ott3596159,
## mrcaott179290ott352521, mrcaott179290ott624976, mrcaott179290ott624973,
## mrcaott179290ott624974, mrcaott179290ott432111, mrcaott5272ott92263,
## mrcaott5272ott24121, Scolopacidae ott887699, mrcaott24121ott217797,
## mrcaott24121ott45306, mrcaott24121ott654830, mrcaott24121ott214779,
## mrcaott24121ott651066, mrcaott651066ott1090732, mrcaott7639ott306220,
## mrcaott7639ott57833, mrcaott7639ott383929, mrcaott7639ott279504,
## mrcaott7639ott47401, mrcaott7639ott341047, mrcaott7639ott234666,
## mrcaott7639ott341034, mrcaott7639ott285543, Sterna ott16767,
## mrcaott285543ott341030, mrcaott285543ott738509, mrcaott285543ott966606,
## mrcaott285543ott966604, mrcaott22965ott738512, mrcaott22965ott80679,
## mrcaott22965ott558955, mrcaott22965ott154126, mrcaott22965ott241571,
## mrcaott22965ott282132, mrcaott22965ott414141, mrcaott22965ott704174,
## mrcaott22965ott704175, mrcaott22965ott324050, mrcaott22965ott353849,
## mrcaott22965ott75913, mrcaott75913ott335736, mrcaott306645ott450107,
## mrcaott425206ott887691, mrcaott425206ott515355, mrcaott119599ott993041,
## mrcaott119599ott509055, mrcaott698969ott4947414, mrcaott147723ott219032,
## Stercorariidae ott168297, Stercorarius ott742632, mrcaott742640ott742641,
## mrcaott57823ott57827, mrcaott57823ott242771, mrcaott57823ott112937,
## mrcaott112937ott129402, mrcaott112937ott454019, mrcaott112937ott242776,
## mrcaott242776ott313115, mrcaott242776ott704464, mrcaott242776ott331994,
## mrcaott331994ott413796, mrcaott9830ott86672, mrcaott9830ott90560,
## mrcaott9830ott324158, mrcaott9830ott55044, mrcaott9830ott285638,
## mrcaott9830ott117726, Sulidae ott452462, mrcaott170197ott403772, Sula ott160486,
## mrcaott170197ott5859716, mrcaott170197ott429615, mrcaott429615ott1030312,
## Procellariiformes ott452461, mrcaott18206ott31011, mrcaott31011ott71459,
## Diomedeidae ott85277, mrcaott71459ott320282, Phoebastria ott941509,
## mrcaott320282ott320284, Sphenisciformes ott494366, Spheniscidae ott494367,
## mrcaott60413ott3600120, mrcaott60413ott4130813, mrcaott60413ott4130835,
## mrcaott60413ott4130830, mrcaott60413ott3600127, mrcaott60413ott4130831,
## mrcaott60413ott3600124, mrcaott60413ott3600128, mrcaott60413ott3600129,
## mrcaott60413ott4130819, mrcaott60413ott4130817, mrcaott60413ott60417,
## mrcaott60413ott3600131, mrcaott60413ott917663, Eudyptes ott494364,
## mrcaott82219ott116946, mrcaott116946ott219197, mrcaott134466ott494361,
## Pygoscelis ott494365, mrcaott134466ott783352, mrcaott5021ott198671,
## mrcaott5021ott75792, Cuculiformes ott212171, mrcaott75792ott212172,
## mrcaott75792ott3601282, mrcaott75792ott119216, mrcaott119216ott212175,
## mrcaott119216ott169265, mrcaott169265ott550039, mrcaott169265ott462546,
## mrcaott462546ott3596355, mrcaott462546ott1050027, Cuculus ott1041429,
## mrcaott549514ott7068132, mrcaott549514ott3596308, mrcaott549514ott3596307,
## mrcaott549514ott3596306, mrcaott549514ott792626, mrcaott17146ott57819,
## Columbiformes ott363030, mrcaott17146ott45505, mrcaott45505ott50388,
## mrcaott45505ott506098, mrcaott45505ott51607, mrcaott51607ott277822,
## mrcaott51607ott67614, mrcaott51607ott244134, Columba ott938415,
## mrcaott51607ott277817, mrcaott277817ott320359, mrcaott320359ott493986,
## mrcaott320359ott767317, mrcaott320359ott921832, mrcaott320359ott938416,
## Galliformes ott837585, mrcaott4765ott6520194, mrcaott4765ott109888,
## mrcaott4765ott75785, mrcaott4765ott104461, mrcaott4765ott151684,
## mrcaott4765ott54193, mrcaott4765ott49319, mrcaott4765ott3596087,
## mrcaott4765ott415487, mrcaott4765ott51354, mrcaott4765ott446490, Tetraoninae
## ott999677, mrcaott4765ott55929, mrcaott4765ott539045, mrcaott4765ott234824,
## Centrocercus ott728081, mrcaott53700ott572162, mrcaott53700ott466627,
## mrcaott53700ott309383, mrcaott53700ott102722, mrcaott102722ott137547, Phasianus
## ott102718, mrcaott49310ott102705, mrcaott49310ott153554, mrcaott153554o
```
```r
plot(tr, show.tip.label = FALSE)
```
There are a few things to note here. First, the tree has no branch lengths.
At present this is true for the whole of the Open Tree synthetic tree. Some
comparative methods require either branch lengths or an ultrametric tree. Before
you can use one of those methods you will need to get a tree with branch
lengths. You could try looking for published trees made available by the Open
Tree with `studies_find_trees`. Alternatively, you could estimate branch lengths
from the toplogy of a phylogeny returned by `tol_induced_subtree`, perhaps by
downloading DNA sequences from the NCBI with `rentrez` or "hanging" the tree on
nodes of known-age using penalized likelihood method in `ape::chronos`.
In this case, we will use only the topology of the tree as input to our
comparative analysis, so we can skip these steps.
Second, the tip labels contain OTT IDs, which means they will not perfectly
match the species names in our dataset or the taxon map that we created earlier:
```r
tr$tip.label[1:4]
```
```
## [1] "Ficedula_albicollis_ott107840" "Luscinia_svecica_ott274225"
## [3] "Turdus_merula_ott568572" "Sturnus_unicolor_ott366470"
```
Finally, the tree contains node labels for those nodes that match a higher taxonomic
group, and empty character vectors (`""`) for all other nodes. Some
comparative methods either do no expect node labels at all, or require all
labeled nodes to have a unique name (meaning multiple "empty" labels will cause
and error).
We can deal with all these details easily. `rotl` provides the convenience
function `strip_ott_ids` to remove the extra information from the tip labels.
With the IDs removed, we can use our taxon map to replace the tip labels in the tree
with the species names from dataset.
```r
otl_tips <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label <- taxon_map[ otl_tips ]
```
Finally, we can remove the node labels by setting the `node.label` attribute of
the tree to `NULL`.
```r
tr$node.label <- NULL
```
```r
egg_data <- egg_data[egg_data$animal %in% tr$tip.label, ]
```
### Perform the meta-analysis
Now we have data and a tree, and we know the names in the tree match the ones in
the data. It's time to do the comparative analysis. Rutkowska _et al_. used `MCMCglmm`, a
Bayesian MCMC approach to fitting multi-level models,to perform their meta-analysis,
and we will do the same. Of course, to properly analyse these data you would
take some care in deciding on the appropriate priors to use and inspect the
results carefully. In this case, we are really interested in using this as a
demonstration, so we will just run a simple model.
Specifically we sill fit a model where the only variable that might explain the
values of `Zr` is the random factor `animal`, which corresponds to the
phylogenetic relationships among species. We also provide `Zvr` as the measurement
error variance, effectively adding extra weight to the results of more powerful
studies. Here's how we specify and fit that model with `MCMCglmm`:
```r
set.seed(123)
if (require(MCMCglmm, quietly = TRUE)) {
pr <- list(
R = list(V = 1, nu = 0.002),
G = list(G1 = list(V = 1, nu = 0.002))
)
model <- MCMCglmm(Zr ~ 1,
random = ~animal,
pedigree = tr,
mev = egg_data$VZr,
prior = pr,
data = egg_data,
verbose = FALSE
)
} else {
model <- readRDS(file = system.file("extdata", "mcmcglmm_model.rds", package = "rotl"))
}
```
```
## Warning in inverseA(pedigree = pedigree, scale = scale, nodes = nodes): no
## branch lengths: compute.brlen from ape has been used
```
```
## Warning in sing.rm$coef: partial match of 'coef' to 'coefficients'
```
```
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
```
Now that we have a result we can find out how much phylogenetic signal exists
for sex-biased differences in egg-size. In a multi-level model we can use variance
components to look at this, specifically the proportion of the total variance
that can be explained by phylogeny is called the phylogenetic reliability, _H_. Let's
calculate the _H_ for this model:
```r
var_comps <- colMeans(model$VCV)
var_comps["animal"] / sum(var_comps)
```
```
## animal
## 0.00283581
```
It appears there is almost no phylogenetic signal to the data.
The relationships among species explain much less that one percent of the total
variance in the data. If you were wondering, Rutkowska _et al_. report a similar result,
even after adding more predictors to their model most of the variance in `Zr`
was left unexplained.
## What other comparative methods can I use in R?
Here we have demonstrated just one comparative analysis that you might
do in R. There are an ever-growing number of packages that allow an
ever-growing number of analysis to performed in R. Some "classics"
like ancestral state reconstruction, phylogenetic independent
contrasts and lineage through time plots are implemented in
`ape`. Packages like `phytools`, `caper` and `diversitree` provide
extensions to these methods. The [CRAN Phylogenetics
Taskview](https://github.com/bomeara/PhylogeneticsTaskView/blob/main/Phylogenetics.md)
gives a good idea of the diversity of packages and analyses that can
be completed in R (note that this links to a draft of the next version
of the Taskview as it is currently unavailable from CRAN).
rotl/inst/doc/data_mashups.Rmd 0000644 0001762 0000144 00000055766 14442034175 016120 0 ustar ligges users ---
title: "Connecting data to Open Tree trees"
author: "David Winter"
date: "2023-06-13"
output:
rmarkdown::html_vignette:
css: vignette.css
vignette: >
%\VignetteIndexEntry{Connecting data to Open Tree trees}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
## Combining data from OToL and other sources.
One of the major goals of `rotl` is to help users combine data from other
sources with the phylogenetic trees in the Open Tree database. This examples
document describes some of the ways in which a user might connect data to trees
from Open Tree.
## Get Open Tree IDs to match your data.
Let's say you have a dataset where each row represents a measurement taken from
one species, and your goal is to put these measurements in some phylogenetic
context. Here's a small example: the best estimate of the mutation rate for a
set of unicellular Eukaryotes along with some other property of those species
which might explain the mutation rate:
```r
csv_path <- system.file("extdata", "protist_mutation_rates.csv", package = "rotl")
mu <- read.csv(csv_path, stringsAsFactors = FALSE)
mu
```
```
## species mu pop.size genome.size
## 1 Tetrahymena thermophila 7.61e-12 1.12e+08 1.04e+08
## 2 Paramecium tetraurelia 1.94e-11 1.24e+08 7.20e+07
## 3 Chlamydomonas reinhardtii 2.08e-10 1.00e+08 1.12e+08
## 4 Dictyostelium discoideum 2.90e-11 7.40e+06 3.40e+07
## 5 Saccharomyces cerevisiae 3.30e-10 1.00e+08 1.25e+08
## 6 Saccharomyces pombe 2.00e-10 1.00e+07 1.25e+08
```
If we want to get a tree for these species we need to start by finding the
unique ID for each of these species in the Open Tree database. We can use the
Taxonomic Name Resolution Service (`tnrs`) functions to do this. Before we do
that we should see if any of the taxonomic contexts, which can be used to narrow
a search and avoid conflicts between different codes, apply to our group of species:
```r
library(rotl)
tnrs_contexts()
```
```
## Possible contexts:
## Animals
## Birds, Tetrapods, Mammals, Amphibians, Vertebrates
## Arthropods, Molluscs, Nematodes, Platyhelminthes, Annelids
## Cnidarians, Arachnids, Insects
## Fungi
## Basidiomycetes, Ascomycetes
## All life
## Bacteria
## SAR group, Archaea, Excavata, Amoebozoa, Centrohelida
## Haptophyta, Apusozoa, Diatoms, Ciliates, Forams
## Land plants
## Hornworts, Mosses, Liverworts, Vascular plants, Club mosses
## Ferns, Seed plants, Flowering plants, Monocots, Eudicots
## Rosids, Asterids, Asterales, Asteraceae, Aster
## Symphyotrichum, Campanulaceae, Lobelia
```
Hmm, none of those groups contain all of our species. In this case we can
search using the `All life` context and the function `tnrs_match_names`:
```r
taxon_search <- tnrs_match_names(names = mu$species, context_name = "All life")
knitr::kable(taxon_search)
```
|search_string |unique_name |approximate_match | ott_id|is_synonym |flags | number_matches|
|:-------------------------|:-------------------------|:-----------------|------:|:----------|:-----|--------------:|
|tetrahymena thermophila |Tetrahymena thermophila |FALSE | 180195|FALSE | | 1|
|paramecium tetraurelia |Paramecium tetraurelia |FALSE | 568130|FALSE | | 1|
|chlamydomonas reinhardtii |Chlamydomonas reinhardtii |FALSE | 33153|FALSE | | 1|
|dictyostelium discoideum |Dictyostelium discoideum |FALSE | 160850|FALSE | | 1|
|saccharomyces cerevisiae |Saccharomyces cerevisiae |FALSE | 356221|FALSE | | 1|
|saccharomyces pombe |Schizosaccharomyces pombe |FALSE | 990004|TRUE | | 1|
Good, all of our species are known to Open Tree. Note, though, that one of the names
is a synonym. _Saccharomyces pombe_ is older name for what is now called
_Schizosaccharomyces pombe_. As the name suggests, the Taxonomic Name
Resolution Service is designed to deal with these problems (and similar ones
like misspellings), but it is always a good idea to check the results of
`tnrs_match_names` closely to ensure the results are what you expect.
In this case we have a good ID for each of our species so we can move on. Before
we do that, let's ensure we can match up our original data to the Open Tree
names and IDs by adding them to our `data.frame`:
```r
mu$ott_name <- unique_name(taxon_search)
mu$ott_id <- taxon_search$ott_id
```
## Find a tree with your taxa
Now let's find a tree. There are two possible options here: we can search for
published studies that include our taxa or we can use the 'synthetic tree' from
Open Tree. We can try both approaches.
### Published trees
Before we can search for published studies or trees, we should check out the
list of properties we can use to perform such searches:
```r
studies_properties()
```
```
## $tree_properties
## [1] "ot:specifiedRoot" "ot:unrootedTree"
## [3] "ot:nodeLabelTimeUnit" "xsi:type"
## [5] "ot:MRCAName" "ot:nodeLabelDescription"
## [7] "ot:nearestTaxonMRCAOttId" "ot:reasonsToExcludeFromSynthesis"
## [9] "ot:curatedType" "ot:nearestTaxonMRCAName"
## [11] "ot:studyId" "ot:nodeLabelMode"
## [13] "rootedge" "nodeById"
## [15] "tb:type.tree" "ot:branchLengthDescription"
## [17] "ot:messages" "ot:rootNodeId"
## [19] "ot:inGroupClade" "ot:ottId"
## [21] "ot:branchLengthMode" "ot:MRCAOttId"
## [23] "ot:ottTaxonName" "tb:quality.tree"
## [25] "tb:kind.tree" "ot:outGroupEdge"
## [27] "ntips" "edgeBySourceId"
## [29] "label" "meta"
## [31] "tb:ntax.tree" "ot:tag"
## [33] "ot:branchLengthTimeUnit"
##
## $study_properties
## [1] "tb:identifier.study.tb1" "id"
## [3] "ot:taxonLinkPrefixes" "ot:studyYear"
## [5] "prism:publicationName" "dc:title"
## [7] "prism:creationDate" "xmlns"
## [9] "skos:changeNote" "skos:historyNote"
## [11] "ot:notIntendedForSynthesis" "ot:otusElementOrder"
## [13] "dc:date" "xhtml:license"
## [15] "prism:startingPage" "prism:publicationDate"
## [17] "ot:focalCladeOTTTaxonName" "prism:section"
## [19] "prism:pageRange" "ot:messages"
## [21] "ot:studyPublicationReference" "ot:curatorName"
## [23] "ot:dataDeposit" "prism:number"
## [25] "ot:agents" "dc:publisher"
## [27] "treesById" "dcterms:bibliographicCitation"
## [29] "ot:treesElementOrder" "prism:modificationDate"
## [31] "tb:identifier.study" "about"
## [33] "ntrees" "nexmljson"
## [35] "ot:comment" "dc:contributor"
## [37] "dc:creator" "prism:volume"
## [39] "tb:title.study" "treebaseId"
## [41] "prism:endingPage" "generator"
## [43] "version" "ot:studyId"
## [45] "ot:candidateTreeForSynthesis" "otusById"
## [47] "prism:doi" "dc:subject"
## [49] "ot:studyPublication" "ot:focalClade"
## [51] "ot:annotationEvents" "ot:tag"
## [53] "nexml2json"
```
We have `ottIds` for our taxa, so let's use those IDs to search for trees that
contain them. Starting with our first species _Tetrahymena thermophila_ we can
use `studies_find_trees` to do this search.
```r
studies_find_trees(property = "ot:ottId", value = as.character(ott_id(taxon_search)[1]))
```
```
## study_ids n_trees tree_ids candidate study_year
## 1 ot_1587 1 tree1 2015
## 2 ot_1589 1 tree1 2015
## 3 ot_2037 4 tree10, tree11, tree12, tree13 2018
## 4 ot_409 2 tree1, tree2 tree2 2015
## 5 ot_564 1 Tr85317 Tr85317 2015
## 6 ot_579 1 Tr60046 2013
## 7 ot_700 1 tree1 tree1 2016
## 8 ot_73 1 tree1 tree1 2013
## 9 ot_766 1 Tr85440 2015
## 10 ot_767 1 tree1 tree1 2016
## 11 ot_87 1 Tr64119 Tr64119 2014
## title
## 1 'Phylogenomic analyses reveal subclass Scuticociliatia as the sister group of subclass Hymenostomatia within class Oligohymenophorea'
## 2 'Phylogenomic analyses reveal subclass Scuticociliatia as the sister group of subclass Hymenostomatia within class Oligohymenophorea'
## 3
## 4 Tree of life reveals clock-like speciation and diversification
## 5 'The alveolate translation initiation factor 4E family reveals a custom toolkit for translational control in core dinoflagellates'
## 6 'Convergent evolution of heat-inducibility during subfunctionalization of the Hsp70 gene family'
## 7 'A new view of the tree of life'
## 8 Deep relationships of Rhizaria revealed by phylogenomics: A farewell to Haeckel’s Radiolaria
## 9 'Bacterial proteins pinpoint a single eukaryotic root'
## 10 'Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida
## 11 'Dinoflagellate phylogeny revisited: Using ribosomal proteins to resolve deep branching dinoflagellate clades'
## study_doi n_matched_trees
## 1 http://dx.doi.org/10.1016/j.ympev.2015.05.007 1
## 2 http://dx.doi.org/10.1016/j.ympev.2015.05.007 1
## 3 http://dx.doi.org/10.1038/s41586-018-0708-8 3
## 4 http://dx.doi.org/10.1093/molbev/msv037 1
## 5 http://dx.doi.org/10.1186/s12862-015-0301-9 1
## 6 http://dx.doi.org/10.1186/1471-2148-13-49 1
## 7 http://dx.doi.org/10.1038/nmicrobiol.2016.48 1
## 8 http://dx.doi.org/10.1016/j.ympev.2012.12.011 1
## 9 http://dx.doi.org/10.1073/pnas.1420657112 1
## 10 http://dx.doi.org/10.1098/rspb.2015.2802 1
## 11 http://dx.doi.org/10.1016/j.ympev.2013.10.007 1
## match_tree_ids
## 1 tree1
## 2 tree1
## 3 tree10, tree13, tree11
## 4 tree2
## 5 Tr85317
## 6 Tr60046
## 7 tree1
## 8 tree1
## 9 Tr85440
## 10 tree1
## 11 Tr64119
## [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
```
Well... that's not very promising. We can repeat that process for all of the IDs
to see if the other species are better represented.
```r
hits <- lapply(mu$ott_id, studies_find_trees, property = "ot:ottId", detailed = FALSE)
sapply(hits, function(x) sum(x[["n_matched_trees"]]))
```
```
## [1] 51 51 128 71 17 88
```
OK, most of our species are not in any of the published trees available. You can
help fix this sort of problem by [making sure you submit your published trees to
Open Tree](https://tree.opentreeoflife.org/curator).
### A part of the synthesis tree
Thankfully, we can still use the complete Tree of Life made from the
combined results of all of the published trees and taxonomies that go into Open
Tree. The function `tol_induced_subtree` will fetch a tree relating a set of IDs.
Using the default arguments you can get a tree object into your R session:
```r
ott_in_tree <- ott_id(taxon_search)[is_in_tree(ott_id(taxon_search))]
tr <- tol_induced_subtree(ott_ids = ott_in_tree)
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: mrcaott2ott276, mrcaott2ott142555, mrcaott2ott1551, mrcaott2ott7623,
## Chloroplastida ott361838, Chlorophyta ott979501, mrcaott185ott42071,
## mrcaott185ott1426, mrcaott1426ott1544, mrcaott1544ott8659, mrcaott1544ott15345,
## mrcaott1544ott9282, mrcaott9389ott818260, mrcaott9389ott23557,
## mrcaott23557ott527099, mrcaott148ott902, SAR ott5246039, Alveolata ott266751,
## Ciliophora (phylum in subkingdom SAR) ott302424, Intramacronucleata
## ott340382, mrcaott1546ott1671, Conthreep ott5248773, mrcaott1671ott16129,
## Peniculia ott1002116, Paramecium (genus in subkingdom SAR) ott568126,
## mrcaott11752ott13570, Hymenostomatia ott5257367, Tetrahymena (genus in
## subkingdom SAR) ott47284, mrcaott295406ott523463, mrcaott295406ott523462,
## Amoebozoa ott1064655, mrcaott3973ott15653, mrcaott3973ott26103,
## mrcaott26103ott273110, mrcaott26103ott229626, Dictyostelia ott835575,
## mrcaott26103ott59686, Dictyosteliales ott4008839, Dictyosteliaceae ott4008841,
## Dictyostelium ott999665, Opisthokonta ott332573, Nucletmycea ott5246132,
## Fungi ott352914, mrcaott109ott3465, mrcaott109ott67172, mrcaott109ott1423,
## mrcaott109ott69881, mrcaott109ott55157, mrcaott109ott9352, h2007-1 ott5584405,
## Dikarya ott656316, mrcaott235ott1053, Saccharomycetales ott4085960,
## Saccharomycetaceae ott989994, Saccharomyces ott908546, Schizosaccharomycetes
## ott921286, Schizosaccharomycetidae ott5670481, Schizosaccharomycetales
## ott508517, Schizosaccharomycetaceae ott990009, Schizosaccharomyces ott990008
```
```r
plot(tr)
```
### Connect your data to the tips of your tree
Now we have a tree for of our species, how can we use the tree and the data
together?
The package `phylobase` provide an object class called `phylo4d`, which is
designed to represent a phylogeny and data associated with its tips. In order to
get our tree and data into one of these objects we have to make sure the labels
in the tree and in our data match exactly. That's not quite the case at the
moment (tree labels have underscores and IDs appended):
```r
mu$ott_name[1]
```
```
## $`Tetrahymena thermophila`
## [1] "Tetrahymena thermophila"
```
```r
tr$tip.label[4]
```
```
## [1] "Dictyostelium_discoideum_ott160850"
```
`rotl` provides a convenience function `strip_ott_ids` to deal with these.
```r
tr$tip.label <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label %in% mu$ott_name
```
```
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
```
Ok, now the tips are together we can make a new dataset. The `phylo4d()`
functions matches tip labels to the row names of a `data.frame`, so let's make
a new dataset that contains just the relevant data and has row names to match
the tree
```r
library(phylobase)
mu_numeric <- mu[, c("mu", "pop.size", "genome.size")]
rownames(mu_numeric) <- mu$ott_name
tree_data <- phylo4d(tr, mu_numeric)
```
And now we can plot the data and the tree together
```r
plot(tree_data)
```
## Find external data associated with studies, trees and taxa from Open Tree
In the above example we looked for a tree that related species in another dataset.
Now we will go the other way, and try to find data associated with Open Tree records
in other databases.
### Get external data from a study
Let's imagine you were interested in extending or reproducing the results of a
published study. If that study is included in Open Tree you can find it via
`studies_find_studies` or `studies_find_trees` and retrieve the published trees
with `get_study`. `rotl` will also help you find external. The function
`study_external_IDs` retrieves the DOI for a given study, and uses that to
gather some more data:
```r
extra_data <- try(study_external_IDs("pg_1980"), silent = TRUE)
if (!inherits(extra_data, "try-error")) {
extra_data
}
```
```
## External data identifiers for study
## $doi: 10.1016/j.ympev.2006.04.016
## $pubmed_id: 16762568
## $nucleotide_ids: vector of 58 IDs
## $external_data_url http://purl.org/phylo/treebase/phylows/study/TB2:S1575
```
Here the returned object contains an `external_data_url` (in this case a link to
the study in Treebase), a pubmed ID for the paper and a vector IDs for the
NCBI's nucleotide database. The packages `treebase` and `rentrez` provide
functions to make use of these IDs within R.
As an example, let's use `rentrez` to download the first two DNA seqences and
print them.
```r
library(rentrez)
seqs <- try(entrez_fetch(db = "nucleotide", id = extra_data$nucleotide_ids[1:2], rettype = "fasta"), silent = TRUE)
if (inherits(seqs, "try-error")) {
cat("NCBI temporarily down.")
} else {
cat(seqs)
}
```
```
## >AM181011.1 Plectroninia neocaledoniense partial 28S rRNA gene, specimen voucher G316300 (Queensland Museum)
## GCTAGCAAGCGCGTCGGTGGTTCAGCCGGCTGGTCTCGTCGAGTTGTCGGTGTGCGGATCCGAACGGACC
## GCGGCCGATGGCGTCGGCGGGCAAGCTGTGGTGCACTCTGTCGGCGTGCGCGTCAGCGTCGGTTTCGGCC
## GGACGACGAGGCGCTCGGGGAAGGTAGCTGGACCGGTCTTCGGTGCAGTGTTATAGCCCTGGGCCGCTGG
## GTTCGGCGTTTGGGACCGAGGAGAGAGATGATCGCTGCAGCGCCTGTCTCCCTCTCGAGGGGGGCTAGCC
## AGCCGCTGTTTGGGTGGCGTCACTGGCGGAGGACTGCACGCAGTGCTTCGCCGGTGGTCGTGTCCAGGCG
## GGCGGTGTGGGTATAGAGGCGCTTAGGACGCTGGCGTCCAAATGGCCGTGCGCGACCCGTCTTGAAACAC
## GGACCAAGGAGTCTAGCATGTGCGCGAGTCTTAGGGTGTGGAAGCCCTCGGGCGCAATGAAAGTGAAGGG
## CCGTCGTCTCTCGGGGCTGCGGTGTGAGGTGAGAGCCGTCGCCGTCGGGTGGCGGTGCATCATCGGCCGG
## TCCATCCTGCTCTCAGGAGGATCTGCGCAAGAGCGTGTTTGCTGGGACCCGAAAGATGGTGAACTATGCC
## TGAATAGGGTGAAGCCAGAGGAAACTCTGGTGGAGGCTCGTAGCGGTTCTGACGTGCAAATCGATCGTCA
## AATTTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGG
## ATAGCTGGAACTCGTCTTGACACAGTTTTATCAGGTAAAGCGAATGATTAGAGGTCTTGGGGGTGAAACA
## CCCTCAACCTATTCTCAAACTTTAAATAGGTAAGAAGCGCGACTTGCTCAATTGAAGTGGCGCGCAGTGA
## ATGTGAGTTCCAAGTGGGCCATTTTTGGTAAGCAGAACTGGCGATGCGGGATGAACCGAACGCTCGGTTA
## AGGTGCCCAAGTCGACGCTCATCAGACCCCAGAAAAGGTGTTGGTCGATATAGACAGCAGGACGGTGGCC
## ATGGAAGTCGGAATCCGCTAAGGAGTGTGTAACAACTCACCTGCCGAATCAACTAGCCCTGAAAATGGAT
## GGCGCTCAAGCGTCGCACCTATACCGAGCCGTCGTGGTAAATGCCAGGCCACGACGAGTAGGAGGGCGCG
## GTGGTCGTGACGCAGCCCTTGGCGCGAGCCTGGGCGAAACGGCCTCCGGTGCAGATCTTGGTGGTAGTAG
## CAAATATTCAAATGAGAGCTTTGAAGACCGAAGTGGAGAAAGGTTCCATGTGAACAGCAGTTGGACATGG
## GTTAGTCGATCCTAAGAGATAGGGAAGTTCCGTGTGAAAGTGCGCAATGCGCTTCTGTGCTGCGCGCCTC
## CTATCGAAAGGGAATCGGGTTAATATTCCCGAACCGGAAGGCGGATATCTCTGGCTCTCGGGTCAGGAGC
## GGCAACGCAAGCGTACTGCGAGACGTCGGCGGGGGCTCCGGGAAGAGTTGTCTTTTCTTTTTAACGCAGT
## CGCCATCCCTGGAATCGGTTTGCCCGGAGATAGGGTTGGCTGGCTCGGTAAAGCAGCACACTTCATGTGC
## TGTCCGGTGCGCTCTCGACGGCCCTTGAAAATCGCAGGTGTGCATCGATTCTCGCATCCGGTCGTACTCA
## TAACCGCATCAGGTCTCCAAGGT
##
## >AM181010.1 Eilhardia schulzei partial 28S rRNA gene, specimen voucher G316071 (Queensland Museum)
## GCTAGTAATGTACGTTGGTGGTTCAGCCGGCTAGTCTTGTCGAGTCGTCGTGTGGTGGATCCGACTGGAC
## CGTCCGCGGTGGTGTCGGCGGGCGAGCTGTGGTGCACTCTACGGACGTGCGCGTCAGCGTCGGTTCTCGA
## TGGGCGATAAGGTGCGTGGGGGAAGGTGGCTCGGTCCTTGGGAACTGAGTGTTACAGACCCTGGTGCTGG
## GCTCGTCGTGGGACCGAGGAGAGAGAGAGATGATCGCTGCGGCACCTGCCCCGTTGTCATTTTTCGGGGC
## TAGCCAGCCGTTTGTCAGGTGTGCGTCGGACGTTGAGGACTGCACGCAGTGCTGGACGTGGAGGCGTGAT
## CTGATGGCGGTGTGGGCATTAGAGGTGCCTAGGACGCTGGCGTCCAAATGGCCGTGCGCGACCCGTCTTG
## AAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCTTAGGGTGTGCAAGCCCTCGGGCGCAATGAAAGT
## GAAGGCTCGGCGGCGCTAGTCGAGCTGAGGTGAGAGCCGTGGCCGTTGCATGTGGCGGCGGCGGCGCATC
## ATCGGCCGGTCCATCCTGCTCTCAGGGGGATCCGAGCAAGAGCGTATTTGTTGGGACCCGAAAGATGGTG
## AACTATGCCTGAATAGGGTGAAGCCAGAGGAAACTCTGGTGGAGGCTCGTAGCGATTCTGACGTGCAAAT
## CGATCGTCAAATTTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTT
## TCCCTCAGGATAGCTGGAGCTCTTGGACACAGTTTTATCAGGTAAAGCGAATGATCAGAGGTCTTGGGGG
## TGAAACACCCTCAACCTATTCTCAAACTTTAAATCGGTAAGAAGCGCGACTTGCTGAATTGAAGCCGCGC
## GCAAGCAATGTGAGTTCCAAGTGGGCCATTTTTGGTAAGCAGAACTGGCGATGCGGGATGAACCGAACGC
## TGGGTTAAGGTGCCAAAGTCGACGCTCATCAGACCCCAGAAAAGGTGTTGGTTGATATAGACAGCAGGAC
## GATGGCCATGGAAGTCGGAATCCGCTAAGGAGTGTGTAACAACTCACCTGCCGAATCAACTAGCCCTGAA
## AATGGATGGCGCTCAAGCGTCGCACCTATACCGGGCCGTCGTCGCAAATGCCAGGCGACGACGAGTAGGA
## GGGCGCAGTGGTCGTCATGCAGCCCTTGGCGTGAGCCTGGGTCAAACGGCCTCTGGTGCAGATCTTGGTG
## GTAGTAGCAAATATTCAAATGAGAGCTTTGAAGACCGAAGTGGAGAAAGGTTCCATGTGAACAGCAGTTG
## GACATGGGTTAGTCGATCCTAAGTGATAGGGGAGCTCCGTATGAAAGTGCGCAATCGGCCCTGCTTGTGT
## CGCCTTGCGCCACCTATCGAAAGGGAATCGGGTTAATATTCCCGAACCGGAAGGCGGATTTTCTCTGGCT
## CTCGGGTCAGGAGCGGCAACGCTAGCGAACCGCGAGACGTCGGCGGGGGCTCCGGGAAGAGTTGTCTTTT
## CTTTTTAACGCAGTCGCCATCCCTGGAATCGGTTTGCCCGGAGATAGGGTTGGCTGGCTCGGTAAAGCAG
## CACACTTCATGTGCTGTCCGGTGCGCTCTCGACGGCCCTTGAAAATCGCGGCGAGTGTAGTCTGATTTTC
## GCATCCGTTCGTACTCATAACCGCATCAGGTCTCCAAGGT
```
You could further process these sequences in R with the function `read.dna` from
`ape` or save them to disk by specifying a file name with `cat`.
### Find a OTT taxon in another taxonomic database
It is also possible map an Open Tree taxon to a record in another taxonomic
database. For instance, if we wanted to search for data about one of the tips of
the sub-tree we fetched in the example above we could do so using
`taxon_external_IDs`:
```r
Tt_ids <- taxon_external_IDs(mu$ott_id[2])
Tt_ids
```
```
## source id
## 1 silva AY102613
## 2 ncbi 5888
## 3 gbif 7415807
```
A user could then use `rgbif` to find locality records using the gbif ID or
`rentrez` to get genetic or bibliometric data about from the NCBI's databases.
## What next
The demonstration gets you to the point of visualizing your data in a
phylogenetic context. But there's a lot more you do with this sort of data in R.
For instance, you could use packages like `ape`, `caper`, `phytools` and
`mcmcGLMM` to perform phylogenetic comparative analyses of your data. You could
gather more data on your species using packages that connect to
trait databases like `rfishbase`, `AntWeb` or `rnpn` which provides data from
the US National Phenology Network. You could also use `rentrez` to find genetic
data for each of your species, and use that data to generate branch lengths for
the phylogeny.
rotl/inst/doc/rotl.Rmd 0000644 0001762 0000144 00000062147 14442034227 014414 0 ustar ligges users ---
title: "How to use rotl?"
author: "François Michonneau"
date: "2023-06-13"
output:
rmarkdown::html_vignette:
css: vignette.css
vignette: >
%\VignetteIndexEntry{How to use rotl?}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
\usepackage[utf8]{inputenc}
---
`rotl` provides an interface to the Open Tree of Life (OTL) API and allows users
to query the API, retrieve parts of the Tree of Life and integrate these parts
with other R packages.
The OTL API provides services to access:
* the **Tree of Life** a.k.a. TOL (the synthetic tree): a single draft tree that is
a combination of **the OTL taxonomy** and the **source trees** (studies)
* the **Taxonomic name resolution services** a.k.a. TNRS: the methods for
resolving taxonomic names to the internal identifiers used by the TOL and the
GOL (the `ott ids`).
* the **Taxonomy** a.k.a. OTT (for Open Tree Taxonomy): which represents the
synthesis of the different taxonomies used as a backbone of the TOL when no
studies are available.
* the **Studies** containing the source trees used to build the TOL, and
extracted from the scientific literature.
In `rotl`, each of these services correspond to functions with different
prefixes:
| Service | `rotl` prefix |
|---------------|---------------|
| Tree of Life | `tol_` |
| TNRS | `tnrs_` |
| Taxonomy | `taxonomy_` |
| Studies | `studies_` |
`rotl` also provides a few other functions and methods that can be used to
extract relevant information from the objects returned by these functions.
## Demonstration of a basic workflow
The most common use for `rotl` is probably to start from a list of species and
get the relevant parts of the tree for these species. This is a two step
process:
1. the species names need to be matched to their `ott_id` (the Open Tree
Taxonomy identifiers) using the Taxonomic name resolution services (TNRS)
1. these `ott_id` will then be used to retrieve the relevant parts of the Tree
of Life.
### Step 1: Matching taxonomy to the `ott_id`
Let's start by doing a search on a diverse group of taxa: a tree frog (genus
_Hyla_), a fish (genus _Salmo_), a sea urchin (genus _Diadema_), and a nautilus
(genus _Nautilus_).
```r
library(rotl)
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
```
It's always a good idea to check that the resolved names match what you
intended:
|search_string |unique_name |approximate_match | ott_id|is_synonym |flags | number_matches|
|:-------------|:-----------|:-----------------|-------:|:----------|:-----|--------------:|
|hyla |Hyla |FALSE | 1062216|FALSE | | 1|
|salmo |Salmo |FALSE | 982359|FALSE | | 1|
|diadema |Diadema |FALSE | 4930522|FALSE | | 4|
|nautilus |Nautilus |FALSE | 616358|FALSE | | 1|
The column `unique_name` sometimes indicates the higher taxonomic level
associated with the name. The column `number_matches` indicates the number of
`ott_id` that corresponds to a given name. In this example, our search on
_Diadema_ returns 2 matches, and the one returned by default is indeed the sea
urchin that we want for our query. The argument `context_name` allows you to
limit the taxonomic scope of your search. _Diadema_ is also the genus name of a
fungus. To ensure that our search is limited to animal names, we could do:
```r
resolved_names <- tnrs_match_names(taxa, context_name = "Animals")
```
If you are trying to build a tree with deeply divergent taxa that the argument
`context_name` cannot fix, see "How to change the ott ids assigned to my taxa?"
in the FAQ below.
### Step 2: Getting the tree corresponding to our taxa
Now that we have the correct `ott_id` for our taxa, we can ask for the tree
using the `tol_induced_subtree()` function. By default, the object returned by
`tol_induced_subtree` is a phylo object (from the
[ape](https://cran.r-project.org/package=ape) package), so we can plot it
directly.
```r
my_tree <- tol_induced_subtree(ott_ids = resolved_names$ott_id)
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: Chordata ott125642, mrcaott42ott658, Craniata (subphylum in
## Deuterostomia) ott947318, Vertebrata (subphylum in Deuterostomia) ott801601,
## Gnathostomata (superclass in phylum Chordata) ott278114, Teleostomi
## ott114656, Sarcopterygii ott458402, Dipnotetrapodomorpha ott4940726,
## Tetrapoda ott229562, Amphibia ott544595, Batrachia ott471197, Anura ott991547,
## mrcaott114ott3129, mrcaott114ott37876, mrcaott114ott18818, Neobatrachia
## ott535804, mrcaott114ott309463, mrcaott114ott177, mrcaott177ott7464,
## mrcaott177ott2199, mrcaott177ott29310, mrcaott177ott1321, Hylidae ott535782,
## mrcaott177ott17126, mrcaott177ott43017, mrcaott177ott2732, mrcaott2732ott3289,
## mrcaott3289ott40328, mrcaott3289ott173489, mrcaott3289ott110534,
## mrcaott3289ott489758, mrcaott3289ott59160, Actinopterygii ott773483,
## Actinopteri ott285821, Neopterygii ott471203, Teleostei ott212201,
## Osteoglossocephalai ott5506109, Clupeocephala ott285819, Euteleosteomorpha
## ott5517919, mrcaott274ott392, mrcaott274ott595, Protacanthopterygii ott1024043,
## mrcaott274ott3887, mrcaott3887ott9371, Salmoniformes ott216171, Salmonidae
## ott739933, mrcaott3887ott28511, Salmoninae ott936925, mrcaott31485ott79094,
## mrcaott360ott10516, Echinodermata ott451020, Eleutherozoa ott317277, Echinozoa
## ott669475, Echinoidea ott669472, mrcaott360ott26831, mrcaott360ott3985,
## mrcaott360ott367, mrcaott360ott362, Acroechinoidea ott5677511, Diadematoida
## ott631174, Diadematidae ott631173, Protostomia ott189832, mrcaott49ott6612,
## Lophotrochozoa ott155737, mrcaott56ott519, mrcaott56ott5497, Mollusca ott802117,
## mrcaott56ott1881, mrcaott1881ott102410, Cephalopoda ott7368, Nautiloidea
## ott854446, Nautilida ott854452, Nautilidae ott616361
```
```r
plot(my_tree, no.margin = TRUE)
```
## FAQ
### How to change the ott ids assigned to my taxa?
If you realize that `tnrs_match_names` assigns the incorrect taxonomic group to
your name (e.g., because of synonymy) and changing the `context_name` does not
help, you can use the function `inspect`. This function takes the object
resulting from `tnrs_match_names()`, and either the row number, the taxon name
(you used in your search in lowercase), or the `ott_id` returned by the initial
query.
To illustrate this, let's re-use the previous query but this time pretending that
we are interested in the fungus _Diadema_ and not the sea urchin:
```r
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
resolved_names
```
```
## search_string unique_name approximate_match ott_id is_synonym flags
## 1 hyla Hyla FALSE 1062216 FALSE
## 2 salmo Salmo FALSE 982359 FALSE
## 3 diadema Diadema FALSE 4930522 FALSE
## 4 nautilus Nautilus FALSE 616358 FALSE
## number_matches
## 1 1
## 2 1
## 3 4
## 4 1
```
```r
inspect(resolved_names, taxon_name = "diadema")
```
```
## search_string unique_name approximate_match ott_id
## 1 diadema Diadema FALSE 4930522
## 2 diadema Diademoides FALSE 4024672
## 3 diadema Garrettia (genus in Opisthokonta) FALSE 631176
## 4 diadema Hypolimnas FALSE 643831
## is_synonym flags number_matches
## 1 FALSE 4
## 2 TRUE sibling_higher 4
## 3 TRUE 4
## 4 TRUE 4
```
In our case, we want the second row in this data frame to replace the
information that initially matched for _Diadema_. We can now use the `update()`
function, to change to the correct taxa (the fungus not the sea urchin):
```r
resolved_names <- update(resolved_names,
taxon_name = "diadema",
new_row_number = 2
)
## we could also have used the ott_id to replace this taxon:
## resolved_names <- update(resolved_names, taxon_name = "diadema",
## new_ott_id = 4930522)
```
And now our `resolved_names` data frame includes the taxon we want:
|search_string |unique_name |approximate_match | ott_id|is_synonym |flags | number_matches|
|:-------------|:-----------|:-----------------|-------:|:----------|:--------------|--------------:|
|hyla |Hyla |FALSE | 1062216|FALSE | | 1|
|salmo |Salmo |FALSE | 982359|FALSE | | 1|
|diadema |Diademoides |FALSE | 4024672|TRUE |sibling_higher | 4|
|nautilus |Nautilus |FALSE | 616358|FALSE | | 1|
### How do I know that the taxa I'm asking for is the correct one?
The function `taxonomy_taxon_info()` takes `ott_ids` as arguments and returns
taxonomic information about the taxa. This output can be passed to some helpers
functions to extract the relevant information. Let's illustrate this with our
_Diadema_ example
```r
diadema_info <- taxonomy_taxon_info(631176)
tax_rank(diadema_info)
```
```
## $`Garrettia (genus in Opisthokonta)`
## [1] "genus"
##
## attr(,"class")
## [1] "otl_rank" "list"
```
```r
synonyms(diadema_info)
```
```
## $`Garrettia (genus in Opisthokonta)`
## [1] "Centrechinus" "Diadema" "Diamema"
##
## attr(,"class")
## [1] "otl_synonyms" "list"
```
```r
tax_name(diadema_info)
```
```
## $`Garrettia (genus in Opisthokonta)`
## [1] "Garrettia"
##
## attr(,"class")
## [1] "otl_name" "list"
```
In some cases, it might also be useful to investigate the taxonomic tree
descending from an `ott_id` to check that it's the correct taxon and to
determine the species included in the Open Tree Taxonomy:
```r
diadema_tax_tree <- taxonomy_subtree(631176)
diadema_tax_tree
```
```
## $tip_label
## [1] "Garrettia_parva_ott6356094"
## [2] "Garrettia_rotella_ott6356095"
## [3] "Diadema_savignyi_ott395692"
## [4] "Diadema_palmeri_ott836860"
## [5] "Diadema_setosum_ott631175"
## [6] "Diadema_paucispinum_ott312263"
## [7] "unclassified_Diadema_ott7669081"
## [8] "Diadema_africanum_ott4147369"
## [9] "Diadema_antillarum_scensionis_ott220009"
## [10] "Diadema_antillarum_antillarum_ott4147370"
## [11] "Diadema_mexicanum_ott639130"
## [12] "Diademasp.SP04-BIO_4_JGLCO_AYott7072105"
## [13] "Diademasp.SP03-BIO_3_JGLCO_AYott7072104"
## [14] "Diademasp.SP02-BIO_2_JGLCO_AYott7072103"
## [15] "Diadema_sp._DSM1_ott219999"
## [16] "Diadema_sp._DSM6_ott771059"
## [17] "Diademasp.ACOSTI-NIOTSU3ott7072098"
## [18] "Diademasp.LI03-BIO_JGLCO_AYott7072102"
## [19] "Diademasp.LI02-BIO_JGLCO_AYott7072101"
## [20] "Diademasp.LI01-BIO_JGLCO_AYott7072100"
## [21] "Diademasp.ACOSTI-NIOTSU4ott7072099"
## [22] "Diadema_sp._seto35_ott66618"
## [23] "Diadema_sp._seto18_ott66623"
## [24] "Diadema_sp._seto19_ott66624"
## [25] "Diadema_sp._seto38_ott66625"
## [26] "Diadema_sp._DJN9_ott66626"
## [27] "Diademasp.CS-2014ott5502179"
## [28] "Diadema_sp._seto17_ott587478"
## [29] "Diadema_sp._SETO15_ott587479"
## [30] "Diadema_sp._dsm5_ott587480"
## [31] "Diadema_sp._DSM4_ott587481"
## [32] "Diadema_sp._DSM3_ott587482"
## [33] "Diadema_sp._DSM2_ott587483"
## [34] "Diadema_sp._seto10_ott587484"
## [35] "Diadema_sp._seto9_ott587485"
## [36] "Diadema_sp._DSM8_ott587486"
## [37] "Diadema_sp._DSM7_ott587487"
## [38] "Diadema_sp._seto16_ott312262"
## [39] "Diadema_africana_ott5502180"
## [40] "Diadema_principeana_ott5725746"
## [41] "Diadema_vetus_ott5725747"
## [42] "Diadema_regnyi_ott7669077"
## [43] "Diadema_amalthei_ott7669073"
## [44] "Diadema_affine_ott7669072"
## [45] "Diadema_subcomplanatum_ott7669079"
## [46] "Diadema_ruppelii_ott7669078"
## [47] "Diadema_calloviensis_ott7669074"
## [48] "Diadema_megastoma_ott7669075"
## [49] "Diadema_priscum_ott7669076"
## [50] "Garrettia_biangulata_ott7669080"
## [51] "Diadema_ascensionis_ott4950423"
## [52] "Diadema_lobatum_ott4950422"
##
## $edge_label
## [1] "Diadema_antillarum_ott1022356" "'Garrettia(genusinOpisthokonta"
## [3] "ott631176'"
```
By default, this function return all taxa (including self, and internal)
descending from this `ott_id` but it also possible to return `phylo` object.
### How do I get the tree for a particular taxonomic group?
If you are looking to get the tree for a particular taxonomic group, you need to
first identify it by its node id or ott id, and then use the `tol_subtree()`
function:
```r
mono_id <- tnrs_match_names("Monotremata")
mono_tree <- tol_subtree(ott_id = ott_id(mono_id))
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Tachyglossus ott16047, Tachyglossus aculeatus ott16038, Ornithorhynchus
## ott962391, Obdurodon ott3615207, Steropodon ott3615209, Teinolophos ott4119276
```
```r
plot(mono_tree)
```
### How do I find trees from studies focused on my favourite taxa?
The function `studies_find_trees()` allows the user to search for studies
matching a specific criteria. The function `studies_properties()` returns the
list of properties that can be used in the search.
```r
furry_studies <- studies_find_studies(property = "ot:focalCladeOTTTaxonName", value = "Mammalia")
furry_ids <- furry_studies$study_ids
```
Now that we know the `study_id`, we can ask for the meta data information
associated with this study:
```r
furry_meta <- get_study_meta("pg_2550")
get_publication(furry_meta) ## The citation for the source of the study
```
```
## [1] "O'Leary, Maureen A., Marc Allard, Michael J. Novacek, Jin Meng, and John Gatesy. 2004. \"Building the mammalian sector of the tree of life: Combining different data and a discussion of divergence times for placental mammals.\" In: Cracraft J., & Donoghue M., eds. Assembling the Tree of Life. pp. 490-516. Oxford, United Kingdom, Oxford University Press."
## attr(,"DOI")
## [1] ""
```
```r
get_tree_ids(furry_meta) ## This study has 10 trees associated with it
```
```
## [1] "tree5513" "tree5515" "tree5516" "tree5517" "tree5518" "tree5519"
## [7] "tree5520" "tree5521" "tree5522" "tree5523"
```
```r
candidate_for_synth(furry_meta) ## None of these trees are yet included in the OTL
```
```
## NULL
```
Using `get_study("pg_2550")` would returns a `multiPhylo` object (default) with
all the trees associated with this particular study, while
`get_study_tree("pg_2550", "tree5513")` would return one of these trees.
### The tree returned by the API has duplicated tip labels, how can I work around it?
You may encounter the following error message:
```
Error in rncl(file = file, ...) : Taxon number 39 (coded by the token Pratia
angulata) has already been encountered in this tree. Duplication of taxa in a
tree is prohibited.
```
This message occurs as duplicate labels are not allowed in the NEXUS format and
it is stricly enforced by the part of the code used by `rotl` to import the
trees in memory.
If you use a version of `rotl` more recent than 0.4.1, this should not happen by
default for the function `get_study_tree`. If it happens with another function,
please [let us know](https://github.com/ropensci/rotl/issues).
The easiest way to work around this is to save the tree in a file, and use APE
to read it in memory:
```r
get_study_tree(
study_id = "pg_710", tree_id = "tree1277",
tip_label = "ott_taxon_name", file = "/tmp/tree.tre",
file_format = "newick"
)
tr <- ape::read.tree(file = "/tmp/tree.tre")
```
### How do I get the higher taxonomy for a given taxa?
If you encounter a taxon name you are not familiar with, it might be useful to
obtain its higher taxonomy to see where it fits in the tree of life. We can
combine several taxonomy methods to extract this information easily.
```r
giant_squid <- tnrs_match_names("Architeuthis")
tax_lineage(taxonomy_taxon_info(ott_id(giant_squid), include_lineage = TRUE))
```
```
## $`564394`
## rank name unique_name ott_id
## 1 family Architeuthidae Architeuthidae 564393
## 2 order Oegopsida Oegopsida 43352
## 3 superorder Decapodiformes Decapodiformes 854107
## 4 infraclass Neocoleoidea Neocoleoidea 329546
## 5 subclass Coleoidea Coleoidea 7371
## 6 class Cephalopoda Cephalopoda 7368
## 7 phylum Mollusca Mollusca 802117
## 8 no rank Lophotrochozoa Lophotrochozoa 155737
## 9 no rank Protostomia Protostomia 189832
## 10 no rank Bilateria Bilateria 117569
## 11 no rank Eumetazoa Eumetazoa 641038
## 12 kingdom Metazoa Metazoa 691846
## 13 no rank Holozoa Holozoa 5246131
## 14 no rank Opisthokonta Opisthokonta 332573
## 15 domain Eukaryota Eukaryota 304358
## 16 no rank cellular organisms cellular organisms 93302
## 17 no rank life life 805080
```
### Why are OTT IDs discovered with `rotl` missing from an induced subtree?
Some taxonomic names that can be retrieved through the taxonomic name
resolution service are not part of the Open Tree's synthesis tree. These are
usually traditional higher-level taxa that have been found to be paraphyletic.
For instance, if you wanted to fetch a tree relating the three birds that go
into a [Turkducken](https://en.wikipedia.org/wiki/Turducken) as well as the pork
used for stuffing, you might search for the turkey, duck, chicken, and pork
genera:
```r
turducken <- c("Meleagris", "Anas", "Gallus", "Sus")
taxa <- tnrs_match_names(turducken, context = "Animals")
```
```
## Warning in tnrs_match_names(turducken, context = "Animals"): partial argument
## match of 'context' to 'context_name'
```
```r
taxa
```
```
## search_string unique_name approximate_match ott_id is_synonym flags
## 1 meleagris Meleagris FALSE 446481 FALSE
## 2 anas Anas FALSE 765185 FALSE
## 3 gallus Gallus FALSE 153562 FALSE
## 4 sus Sus FALSE 730021 FALSE
## number_matches
## 1 2
## 2 1
## 3 3
## 4 1
```
We have the OTT ids for each genus, however, if we tried to get the induced
subtree from these results, we would get an error:
```r
tr <- tol_induced_subtree(ott_id(taxa))
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Mammalia ott244265, Theria (subclass in Deuterostomia) ott229558,
## Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383, mrcaott42ott3607429,
## mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria ott5334778, Laurasiatheria
## ott392223, mrcaott1548ott4697, mrcaott1548ott6790, mrcaott1548ott3607484,
## mrcaott1548ott4942380, mrcaott1548ott4942547, mrcaott1548ott3021, Artiodactyla
## ott622916, mrcaott1548ott21987, Suina ott916745, Suidae ott730008, Sauropsida
## ott639642, Sauria ott329823, mrcaott246ott2982, mrcaott246ott31216,
## mrcaott246ott3602822, mrcaott246ott4143599, mrcaott246ott3600976,
## mrcaott246ott4132107, Aves ott81461, Neognathae ott241846, Galliformes
## ott837585, mrcaott4765ott6520194, mrcaott4765ott109888, mrcaott4765ott75785,
## mrcaott4765ott104461, mrcaott4765ott151684, mrcaott4765ott54193,
## mrcaott4765ott49319, mrcaott4765ott3596087, mrcaott4765ott415487,
## mrcaott4765ott51354, mrcaott4765ott53700, mrcaott4765ott446490, Meleagridinae
## ott781250, mrcaott49310ott102705, mrcaott49310ott51349, mrcaott49310ott153554,
## mrcaott153554ott867027, Anseriformes ott241841, mrcaott30843ott714464, Anatidae
## ott765193, mrcaott30843ott75874, mrcaott30843ott962771, mrcaott30843ott145504,
## mrcaott30843ott30847, mrcaott30843ott196654, mrcaott30843ott30845
```
As the error message suggests, some of the taxa are not found in the synthetic
tree. This occurs for 2 main reasons: either the taxa is invalid, or it is part
of a group that is not monophyletic in the synthetic tree. There are two ways to
get around this issue: (1) removing the taxa that are not part of the Open Tree;
(2) using the complete species name.
#### Removing the taxa missing from the synthetic tree
To help with this situation, `rotl` provides a way to identify the OTT ids that
are not part of the synthetic tree. The function `is_in_tree()` takes the output
of the `ott_id()` function and returns a vector of logical indicating whether
the taxa are part of the synthetic tree. We can then use to only keep the taxa that appear in the synthetic tree:
```r
in_tree <- is_in_tree(ott_id(taxa))
in_tree
```
```
## Meleagris Anas Gallus Sus
## TRUE FALSE TRUE TRUE
```
```r
tr <- tol_induced_subtree(ott_id(taxa)[in_tree])
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Mammalia ott244265, Theria (subclass in Deuterostomia) ott229558,
## Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383, mrcaott42ott3607429,
## mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria ott5334778, Laurasiatheria
## ott392223, mrcaott1548ott4697, mrcaott1548ott6790, mrcaott1548ott3607484,
## mrcaott1548ott4942380, mrcaott1548ott4942547, mrcaott1548ott3021, Artiodactyla
## ott622916, mrcaott1548ott21987, Suina ott916745, Suidae ott730008, Sauropsida
## ott639642, Sauria ott329823, mrcaott246ott2982, mrcaott246ott31216,
## mrcaott246ott3602822, mrcaott246ott4143599, mrcaott246ott3600976,
## mrcaott246ott4132107, Aves ott81461, Neognathae ott241846, Galloanserae
## ott5839486, Galliformes ott837585, mrcaott4765ott6520194, mrcaott4765ott109888,
## mrcaott4765ott75785, mrcaott4765ott104461, mrcaott4765ott151684,
## mrcaott4765ott54193, mrcaott4765ott49319, mrcaott4765ott3596087,
## mrcaott4765ott415487, mrcaott4765ott51354, mrcaott4765ott53700,
## mrcaott4765ott446490, Meleagridinae ott781250, mrcaott49310ott102705,
## mrcaott49310ott51349, mrcaott49310ott153554, mrcaott153554ott867027
```
#### Using the full taxonomic names
The best way to avoid these problems is to specify complete species names
(species being the lowest level of classification in the Open Tree taxonomy they
are guaranteed to be monophyletic):
```r
turducken_spp <- c("Meleagris gallopavo", "Anas platyrhynchos", "Gallus gallus", "Sus scrofa")
taxa <- tnrs_match_names(turducken_spp, context = "Animals")
```
```
## Warning in tnrs_match_names(turducken_spp, context = "Animals"): partial
## argument match of 'context' to 'context_name'
```
```r
tr <- tol_induced_subtree(ott_id(taxa))
```
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: Mammalia ott244265, Theria (subclass in Deuterostomia)
## ott229558, Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383,
## mrcaott42ott3607429, mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria
## ott5334778, Laurasiatheria ott392223, mrcaott1548ott4697, mrcaott1548ott6790,
## mrcaott1548ott3607484, mrcaott1548ott4942380, mrcaott1548ott4942547,
## mrcaott1548ott3021, Artiodactyla ott622916, mrcaott1548ott21987, Suina
## ott916745, Suidae ott730008, Sus ott730021, Sauropsida ott639642, Sauria
## ott329823, mrcaott246ott2982, mrcaott246ott31216, mrcaott246ott3602822,
## mrcaott246ott4143599, mrcaott246ott3600976, mrcaott246ott4132107, Aves
## ott81461, Neognathae ott241846, Galliformes ott837585, mrcaott4765ott6520194,
## mrcaott4765ott109888, mrcaott4765ott75785, mrcaott4765ott104461,
## mrcaott4765ott151684, mrcaott4765ott54193, mrcaott4765ott49319,
## mrcaott4765ott3596087, mrcaott4765ott415487, mrcaott4765ott51354,
## mrcaott4765ott53700, mrcaott4765ott446490, Meleagridinae ott781250, Meleagris
## ott446481, mrcaott49310ott102705, mrcaott49310ott51349, mrcaott49310ott153554,
## mrcaott153554ott867027, Gallus ott153562, mrcaott153554ott153572,
## mrcaott153572ott240568, Anseriformes ott241841, mrcaott30843ott714464, Anatidae
## ott765193, mrcaott30843ott75874, mrcaott30843ott962771, mrcaott30843ott145504,
## mrcaott30843ott30847, mrcaott30843ott196654, mrcaott30843ott30845,
## mrcaott30845ott30850, mrcaott30850ott82420, mrcaott30850ott82414,
## mrcaott30850ott30858, mrcaott30850ott30855, mrcaott30850ott604172,
## mrcaott30850ott82410, mrcaott82410ott339494, mrcaott82410ott604175,
## mrcaott82410ott604182, mrcaott82410ott190881
```
```r
plot(tr)
```
rotl/inst/doc/rotl.html 0000644 0001762 0000144 00000314711 14442555441 014641 0 ustar ligges users
How to use rotl?
How to use rotl?
François Michonneau
2023-06-13
rotl provides an interface to the Open Tree of Life
(OTL) API and allows users to query the API, retrieve parts of the Tree
of Life and integrate these parts with other R packages.
The OTL API provides services to access:
- the Tree of Life a.k.a. TOL (the synthetic tree): a
single draft tree that is a combination of the OTL
taxonomy and the source trees (studies)
- the Taxonomic name resolution services a.k.a. TNRS:
the methods for resolving taxonomic names to the internal identifiers
used by the TOL and the GOL (the
ott ids).
- the Taxonomy a.k.a. OTT (for Open Tree Taxonomy):
which represents the synthesis of the different taxonomies used as a
backbone of the TOL when no studies are available.
- the Studies containing the source trees used to
build the TOL, and extracted from the scientific literature.
In rotl, each of these services correspond to functions
with different prefixes:
| Tree of Life |
tol_ |
| TNRS |
tnrs_ |
| Taxonomy |
taxonomy_ |
| Studies |
studies_ |
rotl also provides a few other functions and methods
that can be used to extract relevant information from the objects
returned by these functions.
Demonstration of a basic workflow
The most common use for rotl is probably to start from a
list of species and get the relevant parts of the tree for these
species. This is a two step process:
- the species names need to be matched to their
ott_id
(the Open Tree Taxonomy identifiers) using the Taxonomic name resolution
services (TNRS)
- these
ott_id will then be used to retrieve the relevant
parts of the Tree of Life.
Step 1: Matching taxonomy to the ott_id
Let’s start by doing a search on a diverse group of taxa: a tree frog
(genus Hyla), a fish (genus Salmo), a sea urchin
(genus Diadema), and a nautilus (genus Nautilus).
library(rotl)
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
It’s always a good idea to check that the resolved names match what
you intended:
| hyla |
Hyla |
FALSE |
1062216 |
FALSE |
|
1 |
| salmo |
Salmo |
FALSE |
982359 |
FALSE |
|
1 |
| diadema |
Diadema |
FALSE |
4930522 |
FALSE |
|
4 |
| nautilus |
Nautilus |
FALSE |
616358 |
FALSE |
|
1 |
The column unique_name sometimes indicates the higher
taxonomic level associated with the name. The column
number_matches indicates the number of ott_id
that corresponds to a given name. In this example, our search on
Diadema returns 2 matches, and the one returned by default is
indeed the sea urchin that we want for our query. The argument
context_name allows you to limit the taxonomic scope of
your search. Diadema is also the genus name of a fungus. To
ensure that our search is limited to animal names, we could do:
resolved_names <- tnrs_match_names(taxa, context_name = "Animals")
If you are trying to build a tree with deeply divergent taxa that the
argument context_name cannot fix, see “How to change the
ott ids assigned to my taxa?” in the FAQ below.
Step 2: Getting the tree corresponding to our taxa
Now that we have the correct ott_id for our taxa, we can
ask for the tree using the tol_induced_subtree() function.
By default, the object returned by tol_induced_subtree is a
phylo object (from the ape package), so we
can plot it directly.
my_tree <- tol_induced_subtree(ott_ids = resolved_names$ott_id)
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: Chordata ott125642, mrcaott42ott658, Craniata (subphylum in
## Deuterostomia) ott947318, Vertebrata (subphylum in Deuterostomia) ott801601,
## Gnathostomata (superclass in phylum Chordata) ott278114, Teleostomi
## ott114656, Sarcopterygii ott458402, Dipnotetrapodomorpha ott4940726,
## Tetrapoda ott229562, Amphibia ott544595, Batrachia ott471197, Anura ott991547,
## mrcaott114ott3129, mrcaott114ott37876, mrcaott114ott18818, Neobatrachia
## ott535804, mrcaott114ott309463, mrcaott114ott177, mrcaott177ott7464,
## mrcaott177ott2199, mrcaott177ott29310, mrcaott177ott1321, Hylidae ott535782,
## mrcaott177ott17126, mrcaott177ott43017, mrcaott177ott2732, mrcaott2732ott3289,
## mrcaott3289ott40328, mrcaott3289ott173489, mrcaott3289ott110534,
## mrcaott3289ott489758, mrcaott3289ott59160, Actinopterygii ott773483,
## Actinopteri ott285821, Neopterygii ott471203, Teleostei ott212201,
## Osteoglossocephalai ott5506109, Clupeocephala ott285819, Euteleosteomorpha
## ott5517919, mrcaott274ott392, mrcaott274ott595, Protacanthopterygii ott1024043,
## mrcaott274ott3887, mrcaott3887ott9371, Salmoniformes ott216171, Salmonidae
## ott739933, mrcaott3887ott28511, Salmoninae ott936925, mrcaott31485ott79094,
## mrcaott360ott10516, Echinodermata ott451020, Eleutherozoa ott317277, Echinozoa
## ott669475, Echinoidea ott669472, mrcaott360ott26831, mrcaott360ott3985,
## mrcaott360ott367, mrcaott360ott362, Acroechinoidea ott5677511, Diadematoida
## ott631174, Diadematidae ott631173, Protostomia ott189832, mrcaott49ott6612,
## Lophotrochozoa ott155737, mrcaott56ott519, mrcaott56ott5497, Mollusca ott802117,
## mrcaott56ott1881, mrcaott1881ott102410, Cephalopoda ott7368, Nautiloidea
## ott854446, Nautilida ott854452, Nautilidae ott616361
plot(my_tree, no.margin = TRUE)
FAQ
How to change the ott ids assigned to my taxa?
If you realize that tnrs_match_names assigns the
incorrect taxonomic group to your name (e.g., because of synonymy) and
changing the context_name does not help, you can use the
function inspect. This function takes the object resulting
from tnrs_match_names(), and either the row number, the
taxon name (you used in your search in lowercase), or the
ott_id returned by the initial query.
To illustrate this, let’s re-use the previous query but this time
pretending that we are interested in the fungus Diadema and not
the sea urchin:
taxa <- c("Hyla", "Salmo", "Diadema", "Nautilus")
resolved_names <- tnrs_match_names(taxa)
resolved_names
## search_string unique_name approximate_match ott_id is_synonym flags
## 1 hyla Hyla FALSE 1062216 FALSE
## 2 salmo Salmo FALSE 982359 FALSE
## 3 diadema Diadema FALSE 4930522 FALSE
## 4 nautilus Nautilus FALSE 616358 FALSE
## number_matches
## 1 1
## 2 1
## 3 4
## 4 1
inspect(resolved_names, taxon_name = "diadema")
## search_string unique_name approximate_match ott_id
## 1 diadema Diadema FALSE 4930522
## 2 diadema Diademoides FALSE 4024672
## 3 diadema Garrettia (genus in Opisthokonta) FALSE 631176
## 4 diadema Hypolimnas FALSE 643831
## is_synonym flags number_matches
## 1 FALSE 4
## 2 TRUE sibling_higher 4
## 3 TRUE 4
## 4 TRUE 4
In our case, we want the second row in this data frame to replace the
information that initially matched for Diadema. We can now use
the update() function, to change to the correct taxa (the
fungus not the sea urchin):
resolved_names <- update(resolved_names,
taxon_name = "diadema",
new_row_number = 2
)
## we could also have used the ott_id to replace this taxon:
## resolved_names <- update(resolved_names, taxon_name = "diadema",
## new_ott_id = 4930522)
And now our resolved_names data frame includes the taxon
we want:
| hyla |
Hyla |
FALSE |
1062216 |
FALSE |
|
1 |
| salmo |
Salmo |
FALSE |
982359 |
FALSE |
|
1 |
| diadema |
Diademoides |
FALSE |
4024672 |
TRUE |
sibling_higher |
4 |
| nautilus |
Nautilus |
FALSE |
616358 |
FALSE |
|
1 |
How do I know that the taxa I’m asking for is the correct one?
The function taxonomy_taxon_info() takes
ott_ids as arguments and returns taxonomic information
about the taxa. This output can be passed to some helpers functions to
extract the relevant information. Let’s illustrate this with our
Diadema example
diadema_info <- taxonomy_taxon_info(631176)
tax_rank(diadema_info)
## $`Garrettia (genus in Opisthokonta)`
## [1] "genus"
##
## attr(,"class")
## [1] "otl_rank" "list"
## $`Garrettia (genus in Opisthokonta)`
## [1] "Centrechinus" "Diadema" "Diamema"
##
## attr(,"class")
## [1] "otl_synonyms" "list"
## $`Garrettia (genus in Opisthokonta)`
## [1] "Garrettia"
##
## attr(,"class")
## [1] "otl_name" "list"
In some cases, it might also be useful to investigate the taxonomic
tree descending from an ott_id to check that it’s the
correct taxon and to determine the species included in the Open Tree
Taxonomy:
diadema_tax_tree <- taxonomy_subtree(631176)
diadema_tax_tree
## $tip_label
## [1] "Garrettia_parva_ott6356094"
## [2] "Garrettia_rotella_ott6356095"
## [3] "Diadema_savignyi_ott395692"
## [4] "Diadema_palmeri_ott836860"
## [5] "Diadema_setosum_ott631175"
## [6] "Diadema_paucispinum_ott312263"
## [7] "unclassified_Diadema_ott7669081"
## [8] "Diadema_africanum_ott4147369"
## [9] "Diadema_antillarum_scensionis_ott220009"
## [10] "Diadema_antillarum_antillarum_ott4147370"
## [11] "Diadema_mexicanum_ott639130"
## [12] "Diademasp.SP04-BIO_4_JGLCO_AYott7072105"
## [13] "Diademasp.SP03-BIO_3_JGLCO_AYott7072104"
## [14] "Diademasp.SP02-BIO_2_JGLCO_AYott7072103"
## [15] "Diadema_sp._DSM1_ott219999"
## [16] "Diadema_sp._DSM6_ott771059"
## [17] "Diademasp.ACOSTI-NIOTSU3ott7072098"
## [18] "Diademasp.LI03-BIO_JGLCO_AYott7072102"
## [19] "Diademasp.LI02-BIO_JGLCO_AYott7072101"
## [20] "Diademasp.LI01-BIO_JGLCO_AYott7072100"
## [21] "Diademasp.ACOSTI-NIOTSU4ott7072099"
## [22] "Diadema_sp._seto35_ott66618"
## [23] "Diadema_sp._seto18_ott66623"
## [24] "Diadema_sp._seto19_ott66624"
## [25] "Diadema_sp._seto38_ott66625"
## [26] "Diadema_sp._DJN9_ott66626"
## [27] "Diademasp.CS-2014ott5502179"
## [28] "Diadema_sp._seto17_ott587478"
## [29] "Diadema_sp._SETO15_ott587479"
## [30] "Diadema_sp._dsm5_ott587480"
## [31] "Diadema_sp._DSM4_ott587481"
## [32] "Diadema_sp._DSM3_ott587482"
## [33] "Diadema_sp._DSM2_ott587483"
## [34] "Diadema_sp._seto10_ott587484"
## [35] "Diadema_sp._seto9_ott587485"
## [36] "Diadema_sp._DSM8_ott587486"
## [37] "Diadema_sp._DSM7_ott587487"
## [38] "Diadema_sp._seto16_ott312262"
## [39] "Diadema_africana_ott5502180"
## [40] "Diadema_principeana_ott5725746"
## [41] "Diadema_vetus_ott5725747"
## [42] "Diadema_regnyi_ott7669077"
## [43] "Diadema_amalthei_ott7669073"
## [44] "Diadema_affine_ott7669072"
## [45] "Diadema_subcomplanatum_ott7669079"
## [46] "Diadema_ruppelii_ott7669078"
## [47] "Diadema_calloviensis_ott7669074"
## [48] "Diadema_megastoma_ott7669075"
## [49] "Diadema_priscum_ott7669076"
## [50] "Garrettia_biangulata_ott7669080"
## [51] "Diadema_ascensionis_ott4950423"
## [52] "Diadema_lobatum_ott4950422"
##
## $edge_label
## [1] "Diadema_antillarum_ott1022356" "'Garrettia(genusinOpisthokonta"
## [3] "ott631176'"
By default, this function return all taxa (including self, and
internal) descending from this ott_id but it also possible
to return phylo object.
How do I get the tree for a particular taxonomic group?
If you are looking to get the tree for a particular taxonomic group,
you need to first identify it by its node id or ott id, and then use the
tol_subtree() function:
mono_id <- tnrs_match_names("Monotremata")
mono_tree <- tol_subtree(ott_id = ott_id(mono_id))
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Tachyglossus ott16047, Tachyglossus aculeatus ott16038, Ornithorhynchus
## ott962391, Obdurodon ott3615207, Steropodon ott3615209, Teinolophos ott4119276
How do I find trees from studies focused on my favourite taxa?
The function studies_find_trees() allows the user to
search for studies matching a specific criteria. The function
studies_properties() returns the list of properties that
can be used in the search.
furry_studies <- studies_find_studies(property = "ot:focalCladeOTTTaxonName", value = "Mammalia")
furry_ids <- furry_studies$study_ids
Now that we know the study_id, we can ask for the meta
data information associated with this study:
furry_meta <- get_study_meta("pg_2550")
get_publication(furry_meta) ## The citation for the source of the study
## [1] "O'Leary, Maureen A., Marc Allard, Michael J. Novacek, Jin Meng, and John Gatesy. 2004. \"Building the mammalian sector of the tree of life: Combining different data and a discussion of divergence times for placental mammals.\" In: Cracraft J., & Donoghue M., eds. Assembling the Tree of Life. pp. 490-516. Oxford, United Kingdom, Oxford University Press."
## attr(,"DOI")
## [1] ""
get_tree_ids(furry_meta) ## This study has 10 trees associated with it
## [1] "tree5513" "tree5515" "tree5516" "tree5517" "tree5518" "tree5519"
## [7] "tree5520" "tree5521" "tree5522" "tree5523"
candidate_for_synth(furry_meta) ## None of these trees are yet included in the OTL
## NULL
Using get_study("pg_2550") would returns a
multiPhylo object (default) with all the trees associated
with this particular study, while
get_study_tree("pg_2550", "tree5513") would return one of
these trees.
The tree returned by the API has duplicated tip labels, how can I
work around it?
You may encounter the following error message:
Error in rncl(file = file, ...) : Taxon number 39 (coded by the token Pratia
angulata) has already been encountered in this tree. Duplication of taxa in a
tree is prohibited.
This message occurs as duplicate labels are not allowed in the NEXUS
format and it is stricly enforced by the part of the code used by
rotl to import the trees in memory.
If you use a version of rotl more recent than 0.4.1,
this should not happen by default for the function
get_study_tree. If it happens with another function, please
let us know.
The easiest way to work around this is to save the tree in a file,
and use APE to read it in memory:
get_study_tree(
study_id = "pg_710", tree_id = "tree1277",
tip_label = "ott_taxon_name", file = "/tmp/tree.tre",
file_format = "newick"
)
tr <- ape::read.tree(file = "/tmp/tree.tre")
How do I get the higher taxonomy for a given taxa?
If you encounter a taxon name you are not familiar with, it might be
useful to obtain its higher taxonomy to see where it fits in the tree of
life. We can combine several taxonomy methods to extract this
information easily.
giant_squid <- tnrs_match_names("Architeuthis")
tax_lineage(taxonomy_taxon_info(ott_id(giant_squid), include_lineage = TRUE))
## $`564394`
## rank name unique_name ott_id
## 1 family Architeuthidae Architeuthidae 564393
## 2 order Oegopsida Oegopsida 43352
## 3 superorder Decapodiformes Decapodiformes 854107
## 4 infraclass Neocoleoidea Neocoleoidea 329546
## 5 subclass Coleoidea Coleoidea 7371
## 6 class Cephalopoda Cephalopoda 7368
## 7 phylum Mollusca Mollusca 802117
## 8 no rank Lophotrochozoa Lophotrochozoa 155737
## 9 no rank Protostomia Protostomia 189832
## 10 no rank Bilateria Bilateria 117569
## 11 no rank Eumetazoa Eumetazoa 641038
## 12 kingdom Metazoa Metazoa 691846
## 13 no rank Holozoa Holozoa 5246131
## 14 no rank Opisthokonta Opisthokonta 332573
## 15 domain Eukaryota Eukaryota 304358
## 16 no rank cellular organisms cellular organisms 93302
## 17 no rank life life 805080
Why are OTT IDs discovered with rotl missing from an
induced subtree?
Some taxonomic names that can be retrieved through the taxonomic name
resolution service are not part of the Open Tree’s synthesis tree. These
are usually traditional higher-level taxa that have been found to be
paraphyletic.
For instance, if you wanted to fetch a tree relating the three birds
that go into a Turkducken as well as
the pork used for stuffing, you might search for the turkey, duck,
chicken, and pork genera:
turducken <- c("Meleagris", "Anas", "Gallus", "Sus")
taxa <- tnrs_match_names(turducken, context = "Animals")
## Warning in tnrs_match_names(turducken, context = "Animals"): partial argument
## match of 'context' to 'context_name'
## search_string unique_name approximate_match ott_id is_synonym flags
## 1 meleagris Meleagris FALSE 446481 FALSE
## 2 anas Anas FALSE 765185 FALSE
## 3 gallus Gallus FALSE 153562 FALSE
## 4 sus Sus FALSE 730021 FALSE
## number_matches
## 1 2
## 2 1
## 3 3
## 4 1
We have the OTT ids for each genus, however, if we tried to get the
induced subtree from these results, we would get an error:
tr <- tol_induced_subtree(ott_id(taxa))
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Mammalia ott244265, Theria (subclass in Deuterostomia) ott229558,
## Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383, mrcaott42ott3607429,
## mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria ott5334778, Laurasiatheria
## ott392223, mrcaott1548ott4697, mrcaott1548ott6790, mrcaott1548ott3607484,
## mrcaott1548ott4942380, mrcaott1548ott4942547, mrcaott1548ott3021, Artiodactyla
## ott622916, mrcaott1548ott21987, Suina ott916745, Suidae ott730008, Sauropsida
## ott639642, Sauria ott329823, mrcaott246ott2982, mrcaott246ott31216,
## mrcaott246ott3602822, mrcaott246ott4143599, mrcaott246ott3600976,
## mrcaott246ott4132107, Aves ott81461, Neognathae ott241846, Galliformes
## ott837585, mrcaott4765ott6520194, mrcaott4765ott109888, mrcaott4765ott75785,
## mrcaott4765ott104461, mrcaott4765ott151684, mrcaott4765ott54193,
## mrcaott4765ott49319, mrcaott4765ott3596087, mrcaott4765ott415487,
## mrcaott4765ott51354, mrcaott4765ott53700, mrcaott4765ott446490, Meleagridinae
## ott781250, mrcaott49310ott102705, mrcaott49310ott51349, mrcaott49310ott153554,
## mrcaott153554ott867027, Anseriformes ott241841, mrcaott30843ott714464, Anatidae
## ott765193, mrcaott30843ott75874, mrcaott30843ott962771, mrcaott30843ott145504,
## mrcaott30843ott30847, mrcaott30843ott196654, mrcaott30843ott30845
As the error message suggests, some of the taxa are not found in the
synthetic tree. This occurs for 2 main reasons: either the taxa is
invalid, or it is part of a group that is not monophyletic in the
synthetic tree. There are two ways to get around this issue: (1)
removing the taxa that are not part of the Open Tree; (2) using the
complete species name.
Removing the taxa missing from the synthetic tree
To help with this situation, rotl provides a way to
identify the OTT ids that are not part of the synthetic tree. The
function is_in_tree() takes the output of the
ott_id() function and returns a vector of logical
indicating whether the taxa are part of the synthetic tree. We can then
use to only keep the taxa that appear in the synthetic tree:
in_tree <- is_in_tree(ott_id(taxa))
in_tree
## Meleagris Anas Gallus Sus
## TRUE FALSE TRUE TRUE
tr <- tol_induced_subtree(ott_id(taxa)[in_tree])
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: Mammalia ott244265, Theria (subclass in Deuterostomia) ott229558,
## Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383, mrcaott42ott3607429,
## mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria ott5334778, Laurasiatheria
## ott392223, mrcaott1548ott4697, mrcaott1548ott6790, mrcaott1548ott3607484,
## mrcaott1548ott4942380, mrcaott1548ott4942547, mrcaott1548ott3021, Artiodactyla
## ott622916, mrcaott1548ott21987, Suina ott916745, Suidae ott730008, Sauropsida
## ott639642, Sauria ott329823, mrcaott246ott2982, mrcaott246ott31216,
## mrcaott246ott3602822, mrcaott246ott4143599, mrcaott246ott3600976,
## mrcaott246ott4132107, Aves ott81461, Neognathae ott241846, Galloanserae
## ott5839486, Galliformes ott837585, mrcaott4765ott6520194, mrcaott4765ott109888,
## mrcaott4765ott75785, mrcaott4765ott104461, mrcaott4765ott151684,
## mrcaott4765ott54193, mrcaott4765ott49319, mrcaott4765ott3596087,
## mrcaott4765ott415487, mrcaott4765ott51354, mrcaott4765ott53700,
## mrcaott4765ott446490, Meleagridinae ott781250, mrcaott49310ott102705,
## mrcaott49310ott51349, mrcaott49310ott153554, mrcaott153554ott867027
Using the full taxonomic names
The best way to avoid these problems is to specify complete species
names (species being the lowest level of classification in the Open Tree
taxonomy they are guaranteed to be monophyletic):
turducken_spp <- c("Meleagris gallopavo", "Anas platyrhynchos", "Gallus gallus", "Sus scrofa")
taxa <- tnrs_match_names(turducken_spp, context = "Animals")
## Warning in tnrs_match_names(turducken_spp, context = "Animals"): partial
## argument match of 'context' to 'context_name'
tr <- tol_induced_subtree(ott_id(taxa))
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: Mammalia ott244265, Theria (subclass in Deuterostomia)
## ott229558, Eutheria (in Deuterostomia) ott683263, mrcaott42ott3607383,
## mrcaott42ott3607429, mrcaott42ott3607455, mrcaott42ott72667, Boreoeutheria
## ott5334778, Laurasiatheria ott392223, mrcaott1548ott4697, mrcaott1548ott6790,
## mrcaott1548ott3607484, mrcaott1548ott4942380, mrcaott1548ott4942547,
## mrcaott1548ott3021, Artiodactyla ott622916, mrcaott1548ott21987, Suina
## ott916745, Suidae ott730008, Sus ott730021, Sauropsida ott639642, Sauria
## ott329823, mrcaott246ott2982, mrcaott246ott31216, mrcaott246ott3602822,
## mrcaott246ott4143599, mrcaott246ott3600976, mrcaott246ott4132107, Aves
## ott81461, Neognathae ott241846, Galliformes ott837585, mrcaott4765ott6520194,
## mrcaott4765ott109888, mrcaott4765ott75785, mrcaott4765ott104461,
## mrcaott4765ott151684, mrcaott4765ott54193, mrcaott4765ott49319,
## mrcaott4765ott3596087, mrcaott4765ott415487, mrcaott4765ott51354,
## mrcaott4765ott53700, mrcaott4765ott446490, Meleagridinae ott781250, Meleagris
## ott446481, mrcaott49310ott102705, mrcaott49310ott51349, mrcaott49310ott153554,
## mrcaott153554ott867027, Gallus ott153562, mrcaott153554ott153572,
## mrcaott153572ott240568, Anseriformes ott241841, mrcaott30843ott714464, Anatidae
## ott765193, mrcaott30843ott75874, mrcaott30843ott962771, mrcaott30843ott145504,
## mrcaott30843ott30847, mrcaott30843ott196654, mrcaott30843ott30845,
## mrcaott30845ott30850, mrcaott30850ott82420, mrcaott30850ott82414,
## mrcaott30850ott30858, mrcaott30850ott30855, mrcaott30850ott604172,
## mrcaott30850ott82410, mrcaott82410ott339494, mrcaott82410ott604175,
## mrcaott82410ott604182, mrcaott82410ott190881
rotl/inst/doc/meta-analysis.html 0000644 0001762 0000144 00000165006 14442555441 016431 0 ustar ligges users
Using the Open Tree synthesis in a comparative analysis
Using the Open Tree synthesis in a
comparative analysis
David Winter
2023-06-13
Phylogenetic Comparative Methods
The development of phylogenetic comparative methods has made
phylogenies and important source of data in fields as diverse as
ecology, genomic and medicine. Comparative methods can be used to
investigate patterns in the evolution of traits or the diversification
of lineages. In other cases a phylogeny is treated as a “nuisance
parameter”, allowing with the autocorrelation created by the shared
evolutionary history of the different species included to be controlled
for.
In many cases finding a tree that relates the species for which trait
data are available is a rate-limiting step in such comparative analyses.
Here we show how the synthetic tree provided by Open Tree of Life (and
made available in R via rotl) can help to fill this
gap.
A phylogenetic meta-analysis
To demonstrate the use of rotl in a comparative
analysis, we will partially reproduce the results of Rutkowska et al
2014. Very briefly, this study is a meta-analysis summarising the
results of multiple studies testing for systematic differences in the
size of eggs which contain male and female offspring. Such a difference
might mean that birds invest more heavily in one sex than the other.
Because this study involves data from 51 different species, Rutkowska
et al used a phylogenetic comparative approach to account for
the shared evolutionary history among some of the studied-species.
Gather the data
If we are going to reproduce this analysis, we will first need to
gather the data. Thankfully, the data is available as supplementary
material from the publisher’s website. We provide a copy of this data
with the package:
## This dataset is available from the publisher's study website:
egg_data <- read.csv(system.file("extdata", "egg.csv", package = "rotl"),
stringsAsFactors = FALSE
)
## }
head(egg_data)
## animal Spp Lndim Measure Neggs
## 1 Zonotrichia_leucophrys White-crowned sparrow 0.000000000 volume 294
## 2 Passer_domesticus House sparrow 0.009407469 volume 149
## 3 Serinus_canaria Canary 0.000000000 volume 52
## 4 Turdus_merula European blackbird 0.021189299 volume 82
## 5 Agelaius_phoeniceus Red-winged blackbird 0.218316086 volume 394
## 6 Quiscalus_mexicanus Great-tailed grackle 0.281894985 mass 822
## Nclutches ESr Type StudyID Year D EN Zr
## 1 73 0.14004594 stat Mead1987 1987 3.421918 85.91673 0.14097244
## 2 31 0.11175203 stat Cordero2000 2000 4.045161 36.83413 0.11222075
## 3 21 0.49679140 stat Leitner2006 2006 2.180952 23.84279 0.54503712
## 4 54 0.38598540 stat Martyka2010 2010 1.414815 57.95812 0.40707397
## 5 106 0.07410136 raw Weatherhead1985 1985 3.173585 124.14982 0.07423744
## 6 205 0.05178834 raw Teather1989 1989 3.407805 241.21099 0.05183471
## VZr
## 1 0.012060292
## 2 0.029555954
## 3 0.047978211
## 4 0.018195675
## 5 0.008254242
## 6 0.004197959
The most important variable in this dataset is Zr, which
is a normalized
effect size for difference ,in size between eggs that contain males
and females. Values close to zero come from studies that found the sex
of an egg’s inhabitant had little effect in its size, while large
positive or negative values correspond to studies with substantial sex
biases (towards males and females respectively). Since this is a
meta-analysis we should produce the classic funnel plot with
effects-size on the y-axis and precision (the inverse of the sample
standard error) on the x-axis. Here we calculate precision from the
sample variance (Vzr):
plot(1 / sqrt(egg_data$VZr), egg_data$Zr,
pch = 16,
ylab = "Effect size (Zr)",
xlab = "Precision (1/SE)",
main = "Effect sizes for sex bias in egg size among 51 brid species"
)
In order to use this data later on we need to first convert it to a
standard data.frame. We can also convert the
animal column (the species names) to lower case, and remove
the underscores in their names, which will make it easier to match names
later on:
egg_data <- as.data.frame(egg_data)
## Convert taxon names to lower case
egg_data$animal <- tolower(egg_data$animal)
## Let's remove the underscores (_) from the taxon names
egg_data$animal <- gsub("_", " ", egg_data$animal)
Find the species in OTT
We can use the OTL synthesis tree to relate these species. To do so
we first need to find Open Tree Taxonomy (OTT) IDs for each species. We
can do that with the Taxonomic Name Resolution Service function
tnrs_match_names:
taxa <- tnrs_match_names(unique(egg_data$animal), context = "Animals")
## Warning in tnrs_match_names(unique(egg_data$animal), context = "Animals"):
## partial argument match of 'context' to 'context_name'
## search_string unique_name approximate_match ott_id
## 1 zonotrichia leucophrys Zonotrichia leucophrys FALSE 265553
## 2 passer domesticus Passer domesticus FALSE 745175
## 3 serinus canaria Serinus canaria FALSE 464865
## 4 turdus merula Turdus merula FALSE 568572
## 5 agelaius phoeniceus Agelaius phoeniceus FALSE 226605
## 6 quiscalus mexicanus Quiscalus mexicanus FALSE 743411
## is_synonym flags number_matches
## 1 FALSE 1
## 2 FALSE 1
## 3 FALSE sibling_higher 1
## 4 FALSE 1
## 5 FALSE 1
## 6 FALSE 1
All of these species are in OTT, but a few of them go by different
names in the Open Tree than we have in our data set. Because the tree
rotl fetches will have Open Tree names, we need to create a
named vector that maps the names we have for each species to the names
Open Tree uses for them:
taxon_map <- structure(taxa$search_string, names = taxa$unique_name)
Now we can use this map to retrieve “data set names” from “OTT
names”:
taxon_map["Anser caerulescens"]
## Anser caerulescens
## "chen caerulescens"
Get a tree
Now we can get the tree. There are really too many tips here to show
nicely, so we will leave them out of this plot
tr <- tol_induced_subtree(ott_id(taxa)[is_in_tree(ott_id(taxa))])
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: mrcaott246ott5481, mrcaott246ott7145, mrcaott246ott928360,
## mrcaott246ott1858, mrcaott246ott2907, mrcaott246ott3600042, mrcaott246ott7113,
## Passeriformes ott1041547, mrcaott246ott3212, mrcaott246ott428578,
## mrcaott246ott44866, mrcaott246ott5929, mrcaott246ott32658,
## mrcaott246ott310390, mrcaott246ott176461, mrcaott246ott22325,
## mrcaott246ott10351, mrcaott246ott193904, mrcaott246ott5934, mrcaott246ott1566,
## mrcaott1566ott3598440, mrcaott1566ott496009, mrcaott1566ott59716,
## mrcaott1566ott32651, mrcaott1566ott24297, mrcaott1566ott45312,
## mrcaott1566ott22300, mrcaott22300ott35350, mrcaott22300ott547548,
## mrcaott22300ott909199, mrcaott22300ott67150, mrcaott22300ott130294,
## mrcaott22300ott3598245, mrcaott22300ott416087, mrcaott22300ott629342,
## mrcaott22300ott416089, mrcaott22300ott107840, mrcaott364210ott451069,
## mrcaott364210ott3598839, mrcaott3598839ott5341363, mrcaott19467ott431648,
## mrcaott19467ott252687, mrcaott19467ott401023, mrcaott19467ott233446,
## mrcaott19467ott168078, mrcaott19467ott1046624, mrcaott19467ott161293,
## mrcaott19467ott46396, mrcaott46396ott46398, mrcaott46398ott168083,
## mrcaott168083ott431676, mrcaott2175ott35326, mrcaott2175ott968664,
## mrcaott2175ott259082, mrcaott2175ott59905, mrcaott2175ott2224,
## mrcaott2224ott366470, mrcaott3364ott310375, mrcaott3364ott3370,
## mrcaott4083ott35042, mrcaott4083ott370807, mrcaott4083ott469177,
## mrcaott4083ott18592, mrcaott4083ott139823, mrcaott4083ott11712,
## mrcaott4083ott95949, Erythrura ott465905, mrcaott24017ott389884,
## mrcaott24017ott24025, mrcaott24025ott596763, mrcaott24025ott453058,
## mrcaott24025ott141501, mrcaott141501ott966119, mrcaott141501ott865472,
## mrcaott141501ott389883, mrcaott105913ott311555, mrcaott311555ott1082386,
## mrcaott311555ott708327, mrcaott708327ott966122, mrcaott4088ott8371,
## mrcaott4088ott5616, mrcaott5616ott28339, mrcaott5616ott6023,
## mrcaott6023ott243614, mrcaott6023ott101225, mrcaott6023ott125079,
## mrcaott125079ott463026, mrcaott125079ott765405, Zonotrichia (genus in
## domain Eukaryota) ott789032, mrcaott125079ott265547, mrcaott125079ott265554,
## mrcaott5620ott254662, mrcaott5620ott29804, mrcaott29804ott449580,
## mrcaott29804ott449562, mrcaott29804ott86894, mrcaott29804ott93045,
## mrcaott93045ott264496, mrcaott264496ott264500, mrcaott264500ott3597163,
## mrcaott264500ott283668, Quiscalus ott743410, mrcaott283673ott673386,
## mrcaott283673ott741944, mrcaott283673ott735243, mrcaott213448ott213452,
## mrcaott213448ott1009279, mrcaott213448ott213451, mrcaott213454ott430627,
## mrcaott430627ott498751, mrcaott430627ott617797, mrcaott430627ott3597159,
## mrcaott99175ott364331, Xanthocephalus ott364336, mrcaott6366ott28332,
## mrcaott6366ott88283, mrcaott6366ott341465, mrcaott6366ott157599,
## mrcaott6366ott178457, mrcaott6366ott405215, mrcaott6366ott238142,
## mrcaott6366ott6375, mrcaott6375ott119724, mrcaott6375ott238137,
## mrcaott238137ott328909, mrcaott238137ott464865, Haemorhous ott3601758,
## mrcaott9416ott840030, mrcaott9416ott96148, mrcaott9416ott7068473, Passer
## ott515158, mrcaott9416ott407769, mrcaott9416ott25628, mrcaott9416ott407764,
## mrcaott9416ott68955, mrcaott9416ott73636, mrcaott1488ott72472,
## mrcaott1488ott63797, mrcaott1488ott284404, mrcaott1488ott107463,
## mrcaott1488ott17016, mrcaott1488ott44217, mrcaott1488ott16185,
## mrcaott16185ott274931, Hirundinidae ott897681, mrcaott16185ott67916,
## mrcaott67916ott67921, mrcaott67916ott368059, mrcaott67916ott67920,
## Delichon ott922719, mrcaott107476ott177058, mrcaott107476ott271376,
## mrcaott107476ott337752, mrcaott337752ott7661935, mrcaott337752ott337762,
## mrcaott337752ott7068616, mrcaott2375ott73144, mrcaott2375ott124085,
## mrcaott2375ott71358, mrcaott2375ott814750, mrcaott2375ott61147,
## mrcaott84656ott123763, mrcaott123763ott728471, mrcaott123763ott258794,
## mrcaott4820ott409116, mrcaott4820ott17162, mrcaott4820ott20998,
## mrcaott4820ott20989, mrcaott4820ott58860, mrcaott4820ott23690,
## mrcaott4820ott20996, mrcaott4820ott11462, mrcaott4820ott140440,
## mrcaott4820ott197505, mrcaott4820ott75978, mrcaott4820ott11315,
## mrcaott4820ott5933, mrcaott5933ott60465, mrcaott5933ott25637,
## mrcaott25637ott199843, mrcaott25637ott473431, mrcaott25637ott686166,
## mrcaott25637ott111993, mrcaott25637ott183621, mrcaott183621ott501241,
## mrcaott501241ott3598010, mrcaott501241ott597018, mrcaott686165ott686168,
## mrcaott60456ott662804, mrcaott60456ott75990, Pica ott776480, Falconiformes
## ott212187, Falconidae ott212186, mrcaott47588ott225286, mrcaott47588ott748842,
## mrcaott47588ott201377, mrcaott201377ott773020, Cerchneis ott3596159,
## mrcaott179290ott352521, mrcaott179290ott624976, mrcaott179290ott624973,
## mrcaott179290ott624974, mrcaott179290ott432111, mrcaott5272ott92263,
## mrcaott5272ott24121, Scolopacidae ott887699, mrcaott24121ott217797,
## mrcaott24121ott45306, mrcaott24121ott654830, mrcaott24121ott214779,
## mrcaott24121ott651066, mrcaott651066ott1090732, mrcaott7639ott306220,
## mrcaott7639ott57833, mrcaott7639ott383929, mrcaott7639ott279504,
## mrcaott7639ott47401, mrcaott7639ott341047, mrcaott7639ott234666,
## mrcaott7639ott341034, mrcaott7639ott285543, Sterna ott16767,
## mrcaott285543ott341030, mrcaott285543ott738509, mrcaott285543ott966606,
## mrcaott285543ott966604, mrcaott22965ott738512, mrcaott22965ott80679,
## mrcaott22965ott558955, mrcaott22965ott154126, mrcaott22965ott241571,
## mrcaott22965ott282132, mrcaott22965ott414141, mrcaott22965ott704174,
## mrcaott22965ott704175, mrcaott22965ott324050, mrcaott22965ott353849,
## mrcaott22965ott75913, mrcaott75913ott335736, mrcaott306645ott450107,
## mrcaott425206ott887691, mrcaott425206ott515355, mrcaott119599ott993041,
## mrcaott119599ott509055, mrcaott698969ott4947414, mrcaott147723ott219032,
## Stercorariidae ott168297, Stercorarius ott742632, mrcaott742640ott742641,
## mrcaott57823ott57827, mrcaott57823ott242771, mrcaott57823ott112937,
## mrcaott112937ott129402, mrcaott112937ott454019, mrcaott112937ott242776,
## mrcaott242776ott313115, mrcaott242776ott704464, mrcaott242776ott331994,
## mrcaott331994ott413796, mrcaott9830ott86672, mrcaott9830ott90560,
## mrcaott9830ott324158, mrcaott9830ott55044, mrcaott9830ott285638,
## mrcaott9830ott117726, Sulidae ott452462, mrcaott170197ott403772, Sula ott160486,
## mrcaott170197ott5859716, mrcaott170197ott429615, mrcaott429615ott1030312,
## Procellariiformes ott452461, mrcaott18206ott31011, mrcaott31011ott71459,
## Diomedeidae ott85277, mrcaott71459ott320282, Phoebastria ott941509,
## mrcaott320282ott320284, Sphenisciformes ott494366, Spheniscidae ott494367,
## mrcaott60413ott3600120, mrcaott60413ott4130813, mrcaott60413ott4130835,
## mrcaott60413ott4130830, mrcaott60413ott3600127, mrcaott60413ott4130831,
## mrcaott60413ott3600124, mrcaott60413ott3600128, mrcaott60413ott3600129,
## mrcaott60413ott4130819, mrcaott60413ott4130817, mrcaott60413ott60417,
## mrcaott60413ott3600131, mrcaott60413ott917663, Eudyptes ott494364,
## mrcaott82219ott116946, mrcaott116946ott219197, mrcaott134466ott494361,
## Pygoscelis ott494365, mrcaott134466ott783352, mrcaott5021ott198671,
## mrcaott5021ott75792, Cuculiformes ott212171, mrcaott75792ott212172,
## mrcaott75792ott3601282, mrcaott75792ott119216, mrcaott119216ott212175,
## mrcaott119216ott169265, mrcaott169265ott550039, mrcaott169265ott462546,
## mrcaott462546ott3596355, mrcaott462546ott1050027, Cuculus ott1041429,
## mrcaott549514ott7068132, mrcaott549514ott3596308, mrcaott549514ott3596307,
## mrcaott549514ott3596306, mrcaott549514ott792626, mrcaott17146ott57819,
## Columbiformes ott363030, mrcaott17146ott45505, mrcaott45505ott50388,
## mrcaott45505ott506098, mrcaott45505ott51607, mrcaott51607ott277822,
## mrcaott51607ott67614, mrcaott51607ott244134, Columba ott938415,
## mrcaott51607ott277817, mrcaott277817ott320359, mrcaott320359ott493986,
## mrcaott320359ott767317, mrcaott320359ott921832, mrcaott320359ott938416,
## Galliformes ott837585, mrcaott4765ott6520194, mrcaott4765ott109888,
## mrcaott4765ott75785, mrcaott4765ott104461, mrcaott4765ott151684,
## mrcaott4765ott54193, mrcaott4765ott49319, mrcaott4765ott3596087,
## mrcaott4765ott415487, mrcaott4765ott51354, mrcaott4765ott446490, Tetraoninae
## ott999677, mrcaott4765ott55929, mrcaott4765ott539045, mrcaott4765ott234824,
## Centrocercus ott728081, mrcaott53700ott572162, mrcaott53700ott466627,
## mrcaott53700ott309383, mrcaott53700ott102722, mrcaott102722ott137547, Phasianus
## ott102718, mrcaott49310ott102705, mrcaott49310ott153554, mrcaott153554o
plot(tr, show.tip.label = FALSE)
There are a few things to note here. First, the tree has no branch
lengths. At present this is true for the whole of the Open Tree
synthetic tree. Some comparative methods require either branch lengths
or an ultrametric tree. Before you can use one of those methods you will
need to get a tree with branch lengths. You could try looking for
published trees made available by the Open Tree with
studies_find_trees. Alternatively, you could estimate
branch lengths from the toplogy of a phylogeny returned by
tol_induced_subtree, perhaps by downloading DNA sequences
from the NCBI with rentrez or “hanging” the tree on nodes
of known-age using penalized likelihood method in
ape::chronos. In this case, we will use only the topology
of the tree as input to our comparative analysis, so we can skip these
steps.
Second, the tip labels contain OTT IDs, which means they will not
perfectly match the species names in our dataset or the taxon map that
we created earlier:
## [1] "Ficedula_albicollis_ott107840" "Luscinia_svecica_ott274225"
## [3] "Turdus_merula_ott568572" "Sturnus_unicolor_ott366470"
Finally, the tree contains node labels for those nodes that match a
higher taxonomic group, and empty character vectors ("")
for all other nodes. Some comparative methods either do no expect node
labels at all, or require all labeled nodes to have a unique name
(meaning multiple “empty” labels will cause and error).
We can deal with all these details easily. rotl provides
the convenience function strip_ott_ids to remove the extra
information from the tip labels. With the IDs removed, we can use our
taxon map to replace the tip labels in the tree with the species names
from dataset.
otl_tips <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label <- taxon_map[ otl_tips ]
Finally, we can remove the node labels by setting the
node.label attribute of the tree to NULL.
egg_data <- egg_data[egg_data$animal %in% tr$tip.label, ]
Perform the meta-analysis
Now we have data and a tree, and we know the names in the tree match
the ones in the data. It’s time to do the comparative analysis.
Rutkowska et al. used MCMCglmm, a Bayesian MCMC
approach to fitting multi-level models,to perform their meta-analysis,
and we will do the same. Of course, to properly analyse these data you
would take some care in deciding on the appropriate priors to use and
inspect the results carefully. In this case, we are really interested in
using this as a demonstration, so we will just run a simple model.
Specifically we sill fit a model where the only variable that might
explain the values of Zr is the random factor
animal, which corresponds to the phylogenetic relationships
among species. We also provide Zvr as the measurement error
variance, effectively adding extra weight to the results of more
powerful studies. Here’s how we specify and fit that model with
MCMCglmm:
set.seed(123)
if (require(MCMCglmm, quietly = TRUE)) {
pr <- list(
R = list(V = 1, nu = 0.002),
G = list(G1 = list(V = 1, nu = 0.002))
)
model <- MCMCglmm(Zr ~ 1,
random = ~animal,
pedigree = tr,
mev = egg_data$VZr,
prior = pr,
data = egg_data,
verbose = FALSE
)
} else {
model <- readRDS(file = system.file("extdata", "mcmcglmm_model.rds", package = "rotl"))
}
## Warning in inverseA(pedigree = pedigree, scale = scale, nodes = nodes): no
## branch lengths: compute.brlen from ape has been used
## Warning in sing.rm$coef: partial match of 'coef' to 'coefficients'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
## Warning in x$n: partial match of 'n' to 'nu'
Now that we have a result we can find out how much phylogenetic
signal exists for sex-biased differences in egg-size. In a multi-level
model we can use variance components to look at this, specifically the
proportion of the total variance that can be explained by phylogeny is
called the phylogenetic reliability, H. Let’s calculate the
H for this model:
var_comps <- colMeans(model$VCV)
var_comps["animal"] / sum(var_comps)
## animal
## 0.00283581
It appears there is almost no phylogenetic signal to the data. The
relationships among species explain much less that one percent of the
total variance in the data. If you were wondering, Rutkowska et
al. report a similar result, even after adding more predictors to
their model most of the variance in Zr was left
unexplained.
What other comparative methods can I use in R?
Here we have demonstrated just one comparative analysis that you
might do in R. There are an ever-growing number of packages that allow
an ever-growing number of analysis to performed in R. Some “classics”
like ancestral state reconstruction, phylogenetic independent contrasts
and lineage through time plots are implemented in ape.
Packages like phytools, caper and
diversitree provide extensions to these methods. The CRAN
Phylogenetics Taskview gives a good idea of the diversity of
packages and analyses that can be completed in R (note that this links
to a draft of the next version of the Taskview as it is currently
unavailable from CRAN).
rotl/inst/doc/data_mashups.html 0000644 0001762 0000144 00000274344 14442555441 016341 0 ustar ligges users
Connecting data to Open Tree trees
Connecting data to Open Tree trees
David Winter
2023-06-13
Combining data from OToL and other sources.
One of the major goals of rotl is to help users combine
data from other sources with the phylogenetic trees in the Open Tree
database. This examples document describes some of the ways in which a
user might connect data to trees from Open Tree.
Get Open Tree IDs to match your data.
Let’s say you have a dataset where each row represents a measurement
taken from one species, and your goal is to put these measurements in
some phylogenetic context. Here’s a small example: the best estimate of
the mutation rate for a set of unicellular Eukaryotes along with some
other property of those species which might explain the mutation
rate:
csv_path <- system.file("extdata", "protist_mutation_rates.csv", package = "rotl")
mu <- read.csv(csv_path, stringsAsFactors = FALSE)
mu
## species mu pop.size genome.size
## 1 Tetrahymena thermophila 7.61e-12 1.12e+08 1.04e+08
## 2 Paramecium tetraurelia 1.94e-11 1.24e+08 7.20e+07
## 3 Chlamydomonas reinhardtii 2.08e-10 1.00e+08 1.12e+08
## 4 Dictyostelium discoideum 2.90e-11 7.40e+06 3.40e+07
## 5 Saccharomyces cerevisiae 3.30e-10 1.00e+08 1.25e+08
## 6 Saccharomyces pombe 2.00e-10 1.00e+07 1.25e+08
If we want to get a tree for these species we need to start by
finding the unique ID for each of these species in the Open Tree
database. We can use the Taxonomic Name Resolution Service
(tnrs) functions to do this. Before we do that we should
see if any of the taxonomic contexts, which can be used to narrow a
search and avoid conflicts between different codes, apply to our group
of species:
library(rotl)
tnrs_contexts()
## Possible contexts:
## Animals
## Birds, Tetrapods, Mammals, Amphibians, Vertebrates
## Arthropods, Molluscs, Nematodes, Platyhelminthes, Annelids
## Cnidarians, Arachnids, Insects
## Fungi
## Basidiomycetes, Ascomycetes
## All life
## Bacteria
## SAR group, Archaea, Excavata, Amoebozoa, Centrohelida
## Haptophyta, Apusozoa, Diatoms, Ciliates, Forams
## Land plants
## Hornworts, Mosses, Liverworts, Vascular plants, Club mosses
## Ferns, Seed plants, Flowering plants, Monocots, Eudicots
## Rosids, Asterids, Asterales, Asteraceae, Aster
## Symphyotrichum, Campanulaceae, Lobelia
Hmm, none of those groups contain all of our species. In this case we
can search using the All life context and the function
tnrs_match_names:
taxon_search <- tnrs_match_names(names = mu$species, context_name = "All life")
knitr::kable(taxon_search)
| tetrahymena thermophila |
Tetrahymena thermophila |
FALSE |
180195 |
FALSE |
|
1 |
| paramecium tetraurelia |
Paramecium tetraurelia |
FALSE |
568130 |
FALSE |
|
1 |
| chlamydomonas reinhardtii |
Chlamydomonas reinhardtii |
FALSE |
33153 |
FALSE |
|
1 |
| dictyostelium discoideum |
Dictyostelium discoideum |
FALSE |
160850 |
FALSE |
|
1 |
| saccharomyces cerevisiae |
Saccharomyces cerevisiae |
FALSE |
356221 |
FALSE |
|
1 |
| saccharomyces pombe |
Schizosaccharomyces pombe |
FALSE |
990004 |
TRUE |
|
1 |
Good, all of our species are known to Open Tree. Note, though, that
one of the names is a synonym. Saccharomyces pombe is older
name for what is now called Schizosaccharomyces pombe. As the
name suggests, the Taxonomic Name Resolution Service is designed to deal
with these problems (and similar ones like misspellings), but it is
always a good idea to check the results of tnrs_match_names
closely to ensure the results are what you expect.
In this case we have a good ID for each of our species so we can move
on. Before we do that, let’s ensure we can match up our original data to
the Open Tree names and IDs by adding them to our
data.frame:
mu$ott_name <- unique_name(taxon_search)
mu$ott_id <- taxon_search$ott_id
Find a tree with your taxa
Now let’s find a tree. There are two possible options here: we can
search for published studies that include our taxa or we can use the
‘synthetic tree’ from Open Tree. We can try both approaches.
Published trees
Before we can search for published studies or trees, we should check
out the list of properties we can use to perform such searches:
## $tree_properties
## [1] "ot:specifiedRoot" "ot:unrootedTree"
## [3] "ot:nodeLabelTimeUnit" "xsi:type"
## [5] "ot:MRCAName" "ot:nodeLabelDescription"
## [7] "ot:nearestTaxonMRCAOttId" "ot:reasonsToExcludeFromSynthesis"
## [9] "ot:curatedType" "ot:nearestTaxonMRCAName"
## [11] "ot:studyId" "ot:nodeLabelMode"
## [13] "rootedge" "nodeById"
## [15] "tb:type.tree" "ot:branchLengthDescription"
## [17] "ot:messages" "ot:rootNodeId"
## [19] "ot:inGroupClade" "ot:ottId"
## [21] "ot:branchLengthMode" "ot:MRCAOttId"
## [23] "ot:ottTaxonName" "tb:quality.tree"
## [25] "tb:kind.tree" "ot:outGroupEdge"
## [27] "ntips" "edgeBySourceId"
## [29] "label" "meta"
## [31] "tb:ntax.tree" "ot:tag"
## [33] "ot:branchLengthTimeUnit"
##
## $study_properties
## [1] "tb:identifier.study.tb1" "id"
## [3] "ot:taxonLinkPrefixes" "ot:studyYear"
## [5] "prism:publicationName" "dc:title"
## [7] "prism:creationDate" "xmlns"
## [9] "skos:changeNote" "skos:historyNote"
## [11] "ot:notIntendedForSynthesis" "ot:otusElementOrder"
## [13] "dc:date" "xhtml:license"
## [15] "prism:startingPage" "prism:publicationDate"
## [17] "ot:focalCladeOTTTaxonName" "prism:section"
## [19] "prism:pageRange" "ot:messages"
## [21] "ot:studyPublicationReference" "ot:curatorName"
## [23] "ot:dataDeposit" "prism:number"
## [25] "ot:agents" "dc:publisher"
## [27] "treesById" "dcterms:bibliographicCitation"
## [29] "ot:treesElementOrder" "prism:modificationDate"
## [31] "tb:identifier.study" "about"
## [33] "ntrees" "nexmljson"
## [35] "ot:comment" "dc:contributor"
## [37] "dc:creator" "prism:volume"
## [39] "tb:title.study" "treebaseId"
## [41] "prism:endingPage" "generator"
## [43] "version" "ot:studyId"
## [45] "ot:candidateTreeForSynthesis" "otusById"
## [47] "prism:doi" "dc:subject"
## [49] "ot:studyPublication" "ot:focalClade"
## [51] "ot:annotationEvents" "ot:tag"
## [53] "nexml2json"
We have ottIds for our taxa, so let’s use those IDs to
search for trees that contain them. Starting with our first species
Tetrahymena thermophila we can use
studies_find_trees to do this search.
studies_find_trees(property = "ot:ottId", value = as.character(ott_id(taxon_search)[1]))
## study_ids n_trees tree_ids candidate study_year
## 1 ot_1587 1 tree1 2015
## 2 ot_1589 1 tree1 2015
## 3 ot_2037 4 tree10, tree11, tree12, tree13 2018
## 4 ot_409 2 tree1, tree2 tree2 2015
## 5 ot_564 1 Tr85317 Tr85317 2015
## 6 ot_579 1 Tr60046 2013
## 7 ot_700 1 tree1 tree1 2016
## 8 ot_73 1 tree1 tree1 2013
## 9 ot_766 1 Tr85440 2015
## 10 ot_767 1 tree1 tree1 2016
## 11 ot_87 1 Tr64119 Tr64119 2014
## title
## 1 'Phylogenomic analyses reveal subclass Scuticociliatia as the sister group of subclass Hymenostomatia within class Oligohymenophorea'
## 2 'Phylogenomic analyses reveal subclass Scuticociliatia as the sister group of subclass Hymenostomatia within class Oligohymenophorea'
## 3
## 4 Tree of life reveals clock-like speciation and diversification
## 5 'The alveolate translation initiation factor 4E family reveals a custom toolkit for translational control in core dinoflagellates'
## 6 'Convergent evolution of heat-inducibility during subfunctionalization of the Hsp70 gene family'
## 7 'A new view of the tree of life'
## 8 Deep relationships of Rhizaria revealed by phylogenomics: A farewell to Haeckel’s Radiolaria
## 9 'Bacterial proteins pinpoint a single eukaryotic root'
## 10 'Untangling the early diversification of eukaryotes: a phylogenomic study of the evolutionary origins of Centrohelida
## 11 'Dinoflagellate phylogeny revisited: Using ribosomal proteins to resolve deep branching dinoflagellate clades'
## study_doi n_matched_trees
## 1 http://dx.doi.org/10.1016/j.ympev.2015.05.007 1
## 2 http://dx.doi.org/10.1016/j.ympev.2015.05.007 1
## 3 http://dx.doi.org/10.1038/s41586-018-0708-8 3
## 4 http://dx.doi.org/10.1093/molbev/msv037 1
## 5 http://dx.doi.org/10.1186/s12862-015-0301-9 1
## 6 http://dx.doi.org/10.1186/1471-2148-13-49 1
## 7 http://dx.doi.org/10.1038/nmicrobiol.2016.48 1
## 8 http://dx.doi.org/10.1016/j.ympev.2012.12.011 1
## 9 http://dx.doi.org/10.1073/pnas.1420657112 1
## 10 http://dx.doi.org/10.1098/rspb.2015.2802 1
## 11 http://dx.doi.org/10.1016/j.ympev.2013.10.007 1
## match_tree_ids
## 1 tree1
## 2 tree1
## 3 tree10, tree13, tree11
## 4 tree2
## 5 Tr85317
## 6 Tr60046
## 7 tree1
## 8 tree1
## 9 Tr85440
## 10 tree1
## 11 Tr64119
## [ reached 'max' / getOption("max.print") -- omitted 16 rows ]
Well… that’s not very promising. We can repeat that process for all
of the IDs to see if the other species are better represented.
hits <- lapply(mu$ott_id, studies_find_trees, property = "ot:ottId", detailed = FALSE)
sapply(hits, function(x) sum(x[["n_matched_trees"]]))
## [1] 51 51 128 71 17 88
OK, most of our species are not in any of the published trees
available. You can help fix this sort of problem by making sure you submit
your published trees to Open Tree.
A part of the synthesis tree
Thankfully, we can still use the complete Tree of Life made from the
combined results of all of the published trees and taxonomies that go
into Open Tree. The function tol_induced_subtree will fetch
a tree relating a set of IDs.
Using the default arguments you can get a tree object into your R
session:
ott_in_tree <- ott_id(taxon_search)[is_in_tree(ott_id(taxon_search))]
tr <- tol_induced_subtree(ott_ids = ott_in_tree)
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: mrcaott2ott276, mrcaott2ott142555, mrcaott2ott1551, mrcaott2ott7623,
## Chloroplastida ott361838, Chlorophyta ott979501, mrcaott185ott42071,
## mrcaott185ott1426, mrcaott1426ott1544, mrcaott1544ott8659, mrcaott1544ott15345,
## mrcaott1544ott9282, mrcaott9389ott818260, mrcaott9389ott23557,
## mrcaott23557ott527099, mrcaott148ott902, SAR ott5246039, Alveolata ott266751,
## Ciliophora (phylum in subkingdom SAR) ott302424, Intramacronucleata
## ott340382, mrcaott1546ott1671, Conthreep ott5248773, mrcaott1671ott16129,
## Peniculia ott1002116, Paramecium (genus in subkingdom SAR) ott568126,
## mrcaott11752ott13570, Hymenostomatia ott5257367, Tetrahymena (genus in
## subkingdom SAR) ott47284, mrcaott295406ott523463, mrcaott295406ott523462,
## Amoebozoa ott1064655, mrcaott3973ott15653, mrcaott3973ott26103,
## mrcaott26103ott273110, mrcaott26103ott229626, Dictyostelia ott835575,
## mrcaott26103ott59686, Dictyosteliales ott4008839, Dictyosteliaceae ott4008841,
## Dictyostelium ott999665, Opisthokonta ott332573, Nucletmycea ott5246132,
## Fungi ott352914, mrcaott109ott3465, mrcaott109ott67172, mrcaott109ott1423,
## mrcaott109ott69881, mrcaott109ott55157, mrcaott109ott9352, h2007-1 ott5584405,
## Dikarya ott656316, mrcaott235ott1053, Saccharomycetales ott4085960,
## Saccharomycetaceae ott989994, Saccharomyces ott908546, Schizosaccharomycetes
## ott921286, Schizosaccharomycetidae ott5670481, Schizosaccharomycetales
## ott508517, Schizosaccharomycetaceae ott990009, Schizosaccharomyces ott990008
Connect your data to the tips of your tree
Now we have a tree for of our species, how can we use the tree and
the data together?
The package phylobase provide an object class called
phylo4d, which is designed to represent a phylogeny and
data associated with its tips. In order to get our tree and data into
one of these objects we have to make sure the labels in the tree and in
our data match exactly. That’s not quite the case at the moment (tree
labels have underscores and IDs appended):
## $`Tetrahymena thermophila`
## [1] "Tetrahymena thermophila"
## [1] "Dictyostelium_discoideum_ott160850"
rotl provides a convenience function
strip_ott_ids to deal with these.
tr$tip.label <- strip_ott_ids(tr$tip.label, remove_underscores = TRUE)
tr$tip.label %in% mu$ott_name
## [1] TRUE TRUE TRUE TRUE TRUE TRUE
Ok, now the tips are together we can make a new dataset. The
phylo4d() functions matches tip labels to the row names of
a data.frame, so let’s make a new dataset that contains
just the relevant data and has row names to match the tree
library(phylobase)
mu_numeric <- mu[, c("mu", "pop.size", "genome.size")]
rownames(mu_numeric) <- mu$ott_name
tree_data <- phylo4d(tr, mu_numeric)
And now we can plot the data and the tree together
Find external data associated with studies, trees and taxa from Open
Tree
In the above example we looked for a tree that related species in
another dataset. Now we will go the other way, and try to find data
associated with Open Tree records in other databases.
Get external data from a study
Let’s imagine you were interested in extending or reproducing the
results of a published study. If that study is included in Open Tree you
can find it via studies_find_studies or
studies_find_trees and retrieve the published trees with
get_study. rotl will also help you find
external. The function study_external_IDs retrieves the DOI
for a given study, and uses that to gather some more data:
extra_data <- try(study_external_IDs("pg_1980"), silent = TRUE)
if (!inherits(extra_data, "try-error")) {
extra_data
}
## External data identifiers for study
## $doi: 10.1016/j.ympev.2006.04.016
## $pubmed_id: 16762568
## $nucleotide_ids: vector of 58 IDs
## $external_data_url http://purl.org/phylo/treebase/phylows/study/TB2:S1575
Here the returned object contains an external_data_url
(in this case a link to the study in Treebase), a pubmed ID for the
paper and a vector IDs for the NCBI’s nucleotide database. The packages
treebase and rentrez provide functions to make
use of these IDs within R.
As an example, let’s use rentrez to download the first
two DNA seqences and print them.
library(rentrez)
seqs <- try(entrez_fetch(db = "nucleotide", id = extra_data$nucleotide_ids[1:2], rettype = "fasta"), silent = TRUE)
if (inherits(seqs, "try-error")) {
cat("NCBI temporarily down.")
} else {
cat(seqs)
}
## >AM181011.1 Plectroninia neocaledoniense partial 28S rRNA gene, specimen voucher G316300 (Queensland Museum)
## GCTAGCAAGCGCGTCGGTGGTTCAGCCGGCTGGTCTCGTCGAGTTGTCGGTGTGCGGATCCGAACGGACC
## GCGGCCGATGGCGTCGGCGGGCAAGCTGTGGTGCACTCTGTCGGCGTGCGCGTCAGCGTCGGTTTCGGCC
## GGACGACGAGGCGCTCGGGGAAGGTAGCTGGACCGGTCTTCGGTGCAGTGTTATAGCCCTGGGCCGCTGG
## GTTCGGCGTTTGGGACCGAGGAGAGAGATGATCGCTGCAGCGCCTGTCTCCCTCTCGAGGGGGGCTAGCC
## AGCCGCTGTTTGGGTGGCGTCACTGGCGGAGGACTGCACGCAGTGCTTCGCCGGTGGTCGTGTCCAGGCG
## GGCGGTGTGGGTATAGAGGCGCTTAGGACGCTGGCGTCCAAATGGCCGTGCGCGACCCGTCTTGAAACAC
## GGACCAAGGAGTCTAGCATGTGCGCGAGTCTTAGGGTGTGGAAGCCCTCGGGCGCAATGAAAGTGAAGGG
## CCGTCGTCTCTCGGGGCTGCGGTGTGAGGTGAGAGCCGTCGCCGTCGGGTGGCGGTGCATCATCGGCCGG
## TCCATCCTGCTCTCAGGAGGATCTGCGCAAGAGCGTGTTTGCTGGGACCCGAAAGATGGTGAACTATGCC
## TGAATAGGGTGAAGCCAGAGGAAACTCTGGTGGAGGCTCGTAGCGGTTCTGACGTGCAAATCGATCGTCA
## AATTTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTTTCCCTCAGG
## ATAGCTGGAACTCGTCTTGACACAGTTTTATCAGGTAAAGCGAATGATTAGAGGTCTTGGGGGTGAAACA
## CCCTCAACCTATTCTCAAACTTTAAATAGGTAAGAAGCGCGACTTGCTCAATTGAAGTGGCGCGCAGTGA
## ATGTGAGTTCCAAGTGGGCCATTTTTGGTAAGCAGAACTGGCGATGCGGGATGAACCGAACGCTCGGTTA
## AGGTGCCCAAGTCGACGCTCATCAGACCCCAGAAAAGGTGTTGGTCGATATAGACAGCAGGACGGTGGCC
## ATGGAAGTCGGAATCCGCTAAGGAGTGTGTAACAACTCACCTGCCGAATCAACTAGCCCTGAAAATGGAT
## GGCGCTCAAGCGTCGCACCTATACCGAGCCGTCGTGGTAAATGCCAGGCCACGACGAGTAGGAGGGCGCG
## GTGGTCGTGACGCAGCCCTTGGCGCGAGCCTGGGCGAAACGGCCTCCGGTGCAGATCTTGGTGGTAGTAG
## CAAATATTCAAATGAGAGCTTTGAAGACCGAAGTGGAGAAAGGTTCCATGTGAACAGCAGTTGGACATGG
## GTTAGTCGATCCTAAGAGATAGGGAAGTTCCGTGTGAAAGTGCGCAATGCGCTTCTGTGCTGCGCGCCTC
## CTATCGAAAGGGAATCGGGTTAATATTCCCGAACCGGAAGGCGGATATCTCTGGCTCTCGGGTCAGGAGC
## GGCAACGCAAGCGTACTGCGAGACGTCGGCGGGGGCTCCGGGAAGAGTTGTCTTTTCTTTTTAACGCAGT
## CGCCATCCCTGGAATCGGTTTGCCCGGAGATAGGGTTGGCTGGCTCGGTAAAGCAGCACACTTCATGTGC
## TGTCCGGTGCGCTCTCGACGGCCCTTGAAAATCGCAGGTGTGCATCGATTCTCGCATCCGGTCGTACTCA
## TAACCGCATCAGGTCTCCAAGGT
##
## >AM181010.1 Eilhardia schulzei partial 28S rRNA gene, specimen voucher G316071 (Queensland Museum)
## GCTAGTAATGTACGTTGGTGGTTCAGCCGGCTAGTCTTGTCGAGTCGTCGTGTGGTGGATCCGACTGGAC
## CGTCCGCGGTGGTGTCGGCGGGCGAGCTGTGGTGCACTCTACGGACGTGCGCGTCAGCGTCGGTTCTCGA
## TGGGCGATAAGGTGCGTGGGGGAAGGTGGCTCGGTCCTTGGGAACTGAGTGTTACAGACCCTGGTGCTGG
## GCTCGTCGTGGGACCGAGGAGAGAGAGAGATGATCGCTGCGGCACCTGCCCCGTTGTCATTTTTCGGGGC
## TAGCCAGCCGTTTGTCAGGTGTGCGTCGGACGTTGAGGACTGCACGCAGTGCTGGACGTGGAGGCGTGAT
## CTGATGGCGGTGTGGGCATTAGAGGTGCCTAGGACGCTGGCGTCCAAATGGCCGTGCGCGACCCGTCTTG
## AAACACGGACCAAGGAGTCTAACATGTGCGCGAGTCTTAGGGTGTGCAAGCCCTCGGGCGCAATGAAAGT
## GAAGGCTCGGCGGCGCTAGTCGAGCTGAGGTGAGAGCCGTGGCCGTTGCATGTGGCGGCGGCGGCGCATC
## ATCGGCCGGTCCATCCTGCTCTCAGGGGGATCCGAGCAAGAGCGTATTTGTTGGGACCCGAAAGATGGTG
## AACTATGCCTGAATAGGGTGAAGCCAGAGGAAACTCTGGTGGAGGCTCGTAGCGATTCTGACGTGCAAAT
## CGATCGTCAAATTTGGGTATAGGGGCGAAAGACTAATCGAACCATCTAGTAGCTGGTTCCCTCCGAAGTT
## TCCCTCAGGATAGCTGGAGCTCTTGGACACAGTTTTATCAGGTAAAGCGAATGATCAGAGGTCTTGGGGG
## TGAAACACCCTCAACCTATTCTCAAACTTTAAATCGGTAAGAAGCGCGACTTGCTGAATTGAAGCCGCGC
## GCAAGCAATGTGAGTTCCAAGTGGGCCATTTTTGGTAAGCAGAACTGGCGATGCGGGATGAACCGAACGC
## TGGGTTAAGGTGCCAAAGTCGACGCTCATCAGACCCCAGAAAAGGTGTTGGTTGATATAGACAGCAGGAC
## GATGGCCATGGAAGTCGGAATCCGCTAAGGAGTGTGTAACAACTCACCTGCCGAATCAACTAGCCCTGAA
## AATGGATGGCGCTCAAGCGTCGCACCTATACCGGGCCGTCGTCGCAAATGCCAGGCGACGACGAGTAGGA
## GGGCGCAGTGGTCGTCATGCAGCCCTTGGCGTGAGCCTGGGTCAAACGGCCTCTGGTGCAGATCTTGGTG
## GTAGTAGCAAATATTCAAATGAGAGCTTTGAAGACCGAAGTGGAGAAAGGTTCCATGTGAACAGCAGTTG
## GACATGGGTTAGTCGATCCTAAGTGATAGGGGAGCTCCGTATGAAAGTGCGCAATCGGCCCTGCTTGTGT
## CGCCTTGCGCCACCTATCGAAAGGGAATCGGGTTAATATTCCCGAACCGGAAGGCGGATTTTCTCTGGCT
## CTCGGGTCAGGAGCGGCAACGCTAGCGAACCGCGAGACGTCGGCGGGGGCTCCGGGAAGAGTTGTCTTTT
## CTTTTTAACGCAGTCGCCATCCCTGGAATCGGTTTGCCCGGAGATAGGGTTGGCTGGCTCGGTAAAGCAG
## CACACTTCATGTGCTGTCCGGTGCGCTCTCGACGGCCCTTGAAAATCGCGGCGAGTGTAGTCTGATTTTC
## GCATCCGTTCGTACTCATAACCGCATCAGGTCTCCAAGGT
You could further process these sequences in R with the function
read.dna from ape or save them to disk by
specifying a file name with cat.
Find a OTT taxon in another taxonomic database
It is also possible map an Open Tree taxon to a record in another
taxonomic database. For instance, if we wanted to search for data about
one of the tips of the sub-tree we fetched in the example above we could
do so using taxon_external_IDs:
Tt_ids <- taxon_external_IDs(mu$ott_id[2])
Tt_ids
## source id
## 1 silva AY102613
## 2 ncbi 5888
## 3 gbif 7415807
A user could then use rgbif to find locality records
using the gbif ID or rentrez to get genetic or bibliometric
data about from the NCBI’s databases.
What next
The demonstration gets you to the point of visualizing your data in a
phylogenetic context. But there’s a lot more you do with this sort of
data in R. For instance, you could use packages like ape,
caper, phytools and mcmcGLMM to
perform phylogenetic comparative analyses of your data. You could gather
more data on your species using packages that connect to trait databases
like rfishbase, AntWeb or rnpn
which provides data from the US National Phenology Network. You could
also use rentrez to find genetic data for each of your
species, and use that data to generate branch lengths for the
phylogeny.
rotl/inst/extdata/ 0000755 0001762 0000144 00000000000 14441553346 013652 5 ustar ligges users rotl/inst/extdata/egg.csv 0000644 0001762 0000144 00000021115 14441553346 015131 0 ustar ligges users animal,Spp,Lndim,Measure,Neggs,Nclutches,ESr,Type,StudyID,Year,D,EN,Zr,VZr
Zonotrichia_leucophrys,White-crowned sparrow,0,volume,294,73,0.140045943,stat,Mead1987,1987,3.421917808,85.91673339,0.140972438,0.012060292
Passer_domesticus,House sparrow,0.009407469,volume,149,31,0.11175203,stat,Cordero2000,2000,4.04516129,36.83413078,0.112220753,0.029555954
Serinus_canaria,Canary,0,volume,52,21,0.4967914,stat,Leitner2006,2006,2.180952381,23.84279476,0.545037117,0.047978211
Turdus_merula,European blackbird,0.021189299,volume,82,54,0.3859854,stat,Martyka2010,2010,1.414814815,57.95811518,0.40707397,0.018195675
Agelaius_phoeniceus,Red-winged blackbird,0.218316086,volume,394,106,0.07410136,raw,Weatherhead1985,1985,3.173584906,124.1498216,0.074237439,0.008254242
Quiscalus_mexicanus,Great-tailed grackle,0.281894985,mass,822,205,0.051788336,raw,Teather1989,1989,3.407804878,241.2109934,0.05183471,0.004197959
Taeniopygia_guttata,Zebra finch,-0.010812869,mass,116,24,-0.05636213,stat,Rutkowska2005,2005,4.066666667,28.52459016,-0.056421926,0.039177906
Taeniopygia_guttata,Zebra finch,-0.010812869,mass,90,20,0,stat,Rutkowska2002,2002,3.8,23.68421053,0,0.048346056
Vanellus_vanellus,Northern lapwing,-0.029825984,volume,114,32,0.03014961,stat,Lislevand2005,2005,3.05,37.37704918,0.03015875,0.029089175
Philomachus_pugnax,Ruff,0.22184875,volume,120,30,0.03462025,stat,Thuman2003,2003,3.4,35.29411765,0.034634091,0.030965392
Luscinia_svecica,Bluethroat,0,volume,102,18,-0.1468127,stat,Lifjeld2005,2005,4.733333333,21.54929577,-0.147881353,0.053910402
Sturnus_unicolor,Spotless starling,0.025305865,mass,153,34,-0.133824538,stat,Cordero2001,2001,3.8,40.26315789,-0.134632122,0.026836158
Branta_canadensis,Canada goose,0.061028185,mass,242,44,-0.006674089,stat,Leblanc1987,1987,4.6,52.60869565,-0.006674188,0.020157756
Falco_tinnunculus,Eurasian kestrel,-0.080479586,mass,132,33,-0.1910071,stat,Martinez-Padilla2007,2007,3.4,38.82352941,-0.193382195,0.027914614
Falco_tinnunculus,Eurasian kestrel,-0.080479586,mass,108,28,0.2165249,stat,Blanco2003,2003,3.285714286,32.86956522,0.220007175,0.033478894
Larus_michahellis,Yellow-legged gull,0.091409863,mass,1068,669,-0.06718163,stat,Rubolini2009,2009,1.477130045,723.0236794,-0.067282976,0.001388843
Cincloramphus_cruralis,Brown songlark,0.363356588,mass,44,17,0.1439293,stat,Isaksson2010,2010,2.270588235,19.37823834,0.144935702,0.061056628
Pavo_cristatus,Peafowl,0.119205592,mass,3313,205,0.005428755,stat,Petrie2001,2001,13.12878049,252.3463625,0.005428808,0.004010486
Parus_caeruleus,Blue tit,0.010299957,mass,192,21,0.07261821,stat,Cichon2003,2003,7.514285714,25.5513308,0.072746264,0.044343281
Larus_argentatus,Herring gull,0.049687784,mass,79,30,0.1160999,stat,Bogdanova2006,2006,2.306666667,34.24855491,0.116625804,0.03200148
Larus_fuscus,Lesser black-backed gull,0.064503231,mass,60,22,0.124354,stat,Bogdanova2005,2005,2.381818182,25.19083969,0.125001014,0.045063639
Corvus_monedula,Jackdaw,0.041392685,mass,226,70,0.2033713,stat,Arnold2003,2003,2.782857143,81.21149897,0.206246808,0.012785844
Carpodacus_mexicanus,House finch,0.002809678,volume,378,84,-0.075561895,raw,Badyaev2006,2006,3.8,99.47368421,-0.075706199,0.010365521
Carpodacus_mexicanus,House finch,0,volume,304,64,-0.072529696,raw,Badyaev2006,2006,4,76,-0.072657281,0.01369863
Sterna_hirundo,Common Tern,0.008600172,volume,427,158,0,raw,Fletcher2004,2004,2.362025316,180.7770632,0,0.005625023
Lonchura_striata,Bengalese finch,-0.021189299,mass,116,34,0.09239892,stat,Soma2007,2007,2.929411765,39.59839357,0.092663229,0.027323604
Molothrus_bonariensis,Ring-billed gull,0.078874433,mass,90,30,-0.000377536,stat,Chin2012,2012,2.6,34.61538462,-0.000377536,0.03163017
Pica_pica,Magpie,0.063358906,volume,43,8,-0.036738337,raw,Slagsvold1992,1992,4.5,9.555555556,-0.036754879,0.152542373
Corvus_corone,Hooded crow,0.049218023,volume,103,31,0.073794034,raw,Slagsvold1992,1992,2.858064516,36.03837472,0.073928423,0.030267833
Taeniopygia_guttata,Zebra finch,-0.010812869,mass,133,22,-0.03994883,stat,Pariser2012,2012,5.036363636,26.40794224,-0.039970102,0.042720543
Delichon_urbicum,House martin,0,mass,90,35,0.03076685,stat,Gil2006,2006,2.257142857,39.87341772,0.030776563,0.027119808
Xanthocephalus_xanthocephalus,Yellow-headed blackbird,0.247321812,mass,90,23,-0.088294337,raw,Richter1983,1983,3.330434783,27.02349869,-0.088524861,0.04162591
Larus_delawarensis,Ring-billed gull,0.06069784,mass,110,37,0.053696485,raw,Meathrel1987,1987,2.578378378,42.66247379,0.053748182,0.025212749
Cincloramphus_cruralis,Brown songlark,0.363356588,volume,95,40,-0.3293106,stat,Magrath2003,2003,2.1,45.23809524,-0.342054801,0.02367531
Larus_ridibundus,Black-headed gull,0,mass,60,20,0.1391671,stat,Groothuis2006,2006,2.6,23.07692308,0.140076126,0.049808429
Anseranas_semipalmata,Magpie goose,0.125672077,mass,60,16,-0.110406595,raw,Whitehead1990,1990,3.2,18.75,-0.11085851,0.063492063
Sterna_hirundo,Common Tern,0,volume,108,40,0.073872636,raw,Gonzalez-Solis2005,2005,2.36,45.76271186,0.074007456,0.023384859
Falco_tinnunculus,Eurasian kestrel,-0.080479586,mass,80,16,0.097815848,raw,Wu2010,2010,4.2,19.04761905,0.098129617,0.06231454
Larus_delawarensis,Shiny cowbird,0.035472318,volume,90,90,-0.07294093,stat,Tuero2012,2012,1,90,-0.073070702,0.011494253
Pygoscelis_antarcticus,Chinstrap penguin,0.058509856,volume,264,132,0.03094903,stat,Fargallo2006,2006,1.8,146.6666667,0.030958917,0.006960557
Phoebastria_irrorata,Waved albatross,0.091157684,volume,224,224,-0.088469783,stat,Awkerman2007,2007,1,224,-0.088701688,0.004524887
Passer_domesticus,House sparrow,0.009407469,volume,77,19,0.05470045,stat,Wetzel2012,2012,3.442105263,22.37003058,0.054755105,0.051626145
Sula_nebouxii,Blue-footed booby,-0.147287056,mass,76,56,-0.2611557,stat,D'Alba2007,2007,1.285714286,59.11111111,-0.267348297,0.017821782
Stercorarius_parasiticus,Parasitic jaeger,-0.063477845,volume,534,267,0.1397023,stat,Janssen2006,2006,1.8,296.6666667,0.140621937,0.003405221
Ficedula_albicollis,Collared flycatcher,0.013679697,volume,1162,198,0.04804496,stat,Bowers2013,2013,4.894949495,237.3875361,0.048081979,0.004266438
Sterna_dougallii,Roseate tern,0,mass,440,146,-0.062868359,raw,Szczys2005,2005,2.610958904,168.5204617,-0.062951384,0.006041549
Centrocercus_urophasianus,Greater sage-grouse,0.261995252,volume,146,20,0.06614164,stat,Atamian2010,2010,6.04,24.17218543,0.066238344,0.04723178
Phasianus_colchicus,Ring-necked pheasant,0.140492874,mass,106,15,0.4531655,stat,Rubolini2007,2007,5.853333333,18.10933941,0.488676681,0.06618423
Taeniopygia_guttata,Zebra finch,-0.010812869,mass,43,15,0,raw,Clotfelter1996,1996,2.493333333,17.2459893,0,0.070195195
Larus_fuscus,Lesser black-backed gull,-0.010812869,volume,304,101,-0.00990453,raw,Bradbury1999,1999,2.607920792,116.5679575,-0.009904854,0.008805301
Sturnus_vulgaris,European starling,0.01616166,mass,354,69,0.04996099,stat,Love2011,2011,4.304347826,82.24242424,0.050002622,0.012619503
Erythrura_gouldiae,Gouldian finch,0,volume,1473,324,-0.019559388,stat,Pryke2009,2009,3.837037037,383.8899614,-0.019561883,0.00262543
Larus_ridibundus,Black-headed gull,0,volume,147,49,0.05453834,stat,Lezalova2005,2005,2.6,56.53846154,0.05459251,0.018678161
Aythya_ferina,Common pochard,0,volume,185,26,0.1957707,stat,Lezalova2013a,2014,5.892307692,31.39686684,0.198330883,0.035215153
Aythya_fuligula,Tufted duck,0.026629385,volume,46,7,-0.04923335,stat,Lezalova2013b,2014,5.457142857,8.429319372,-0.049273187,0.184185149
Aythya_affinis,Lesser scaup,0.031791834,volume,38,5,-0.143657133,raw,Dawson1996,1996,6.28,6.050955414,-0.144657788,0.32776618
Chen_caerulescens,Lesser snow goose,0.037500891,mass,85,22,-0.089455106,raw,Ankney1980,1980,3.290909091,25.82872928,-0.089694871,0.043804453
Falco_sparverius,American kestrels,-0.036212173,volume,170,34,0.093153037,raw,Anderson1997,1997,4.2,40.47619048,0.093423893,0.026683609
Columba_livia,Domestic pigeon,0,mass,63,16,0.3301501,stat,Pike2005,2005,3.35,18.80597015,0.342996707,0.063267233
Zonotrichia_leucophrys,White-crowned sparrow,0,mass,38,11,0.06348392,stat,Bonier2007,2007,2.963636364,12.82208589,0.063569411,0.101811368
Quiscalus_major,Boat-tailed grackle,0.268544242,mass,122,41,-0.056059589,raw,Bancroft1984,1984,2.580487805,47.2778828,-0.056118426,0.022584639
Cuculus_canorus,Common cuckoo,0.042879996,volume,71,38,0.1527316,stat,Fossoy2012,2012,1.694736842,41.89440994,0.153936091,0.025710636
Eudyptes_chrysocome,Rockhopper penguin,0.037301411,mass,213,194,0.1191936,stat,Poisbleau2010,2010,1.078350515,197.5239006,0.119762927,0.005140756
Larus_michahellis,Yellow-legged gull,0.091409863,volume,124,48,-0.003408011,stat,Perez2006,2006,2.266666667,54.70588235,-0.003408024,0.019340159
Gallus_gallus,Red Junglefowl,0.080943092,volume,220,71,-0.08467843,stat,Parker2005,2005,2.678873239,82.12407992,-0.084881699,0.012638378
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rotl/inst/CITATION 0000644 0001762 0000144 00000003271 14442035604 013351 0 ustar ligges users c(
bibentry(
header = "To cite rotl in publications use:",
bibtype = "Article",
title = "{rotl}: an R package to interact with the Open Tree of Life data",
author = c(
as.person("Francois Michonneau"),
as.person("Joseph W. Brown"),
as.person("David J. Winter")
),
journal = "Methods in Ecology and Evolution",
year = "2016",
volume = "7",
number = "12",
pages = "1476-1481",
doi = "10.1111/2041-210X.12593",
textVersion =
paste("Michonneau, F., Brown, J. W. and Winter, D. J. (2016),
rotl: an R package to interact with the Open Tree of Life data.
Methods in Ecology and Evolution. 7(12):1476-1481. doi:10.1111/2041-210X.12593")
),
bibentry(
header = "You should also cite the source data from the Open Tree of Life",
title = "Open Tree of Life Synthetic Tree",
bibtype = "misc",
author = c(
as.person("OpenTreeOfLife"),
as.person("Benjamin Redelings"),
as.person("Luna Luisa Sanchez Reyes"),
as.person("Karen A. Cranston"),
as.person("Jim Allman"),
as.person("Mark T. Holder"),
as.person("Emily Jane McTavish")),
year = "2019",
version = "12.3",
publisher = "Zenodo",
doi = "10.5281/zenodo.3937741",
url = "https://doi.org/10.5281/zenodo.3937741",
textVersion =
paste("OpenTree et al. Open Tree of Life Synthetic Tree.
doi:0.5281/zenodo.39377411")
)
)
`rotl` is an R package to interact with the Open Tree of Life data APIs.
It was initially developed as part of the NESCENT/OpenTree/Arbor
hackathon.
Client libraries to interact with the Open Tree of Life API also exists
for [Python](https://github.com/OpenTreeOfLife/pyopentree) and
[Ruby](https://github.com/SpeciesFileGroup/bark).
## Installation
The current stable version is available from CRAN, and can be installed
by typing the following at the prompt in R:
``` r
install.packages("rotl")
```
If you want to test the development version, you first need to install
the `remotes` package.
``` r
install.packages("remotes")
```
Then you can install `rotl` using:
``` r
remotes::install_github("ropensci/rotl")
```
## Vignettes
There are three vignettes:
- Start by checking out the “How to use `rotl`?” by typing:
`vignette("rotl", package="rotl")` after installing the package.
- Then explore how you can use `rotl` with other packages to combine
your data with trees from the Open Tree of Life project by typing:
`vignette("data_mashups", package="rotl")`.
- The vignette “Using the Open Tree Synthesis in a comparative
analysis” demonstrates how you can reproduce an analysis of a
published paper by downloading the tree they used, and data from the
supplementary material: `vignette("meta-analysis", package="rotl")`.
The vignettes are also available from CRAN: [How to use
`rotl`?](https://cran.r-project.org/package=rotl/vignettes/rotl.html),
[Data
mashups](https://cran.r-project.org/package=rotl/vignettes/data_mashups.html),
and [Using the Open Tree synthesis in a comparative
analysis](https://cran.r-project.org/package=rotl/vignettes/meta-analysis.html).
## Quick start
### Get a little bit of the big Open Tree tree
Taxonomic names are represented in the Open Tree by numeric identifiers,
the `ott_ids` (Open Tree Taxonomy identifiers). To extract a portion of
a tree from the Open Tree, you first need to find `ott_ids` for a set of
names using the `tnrs_match_names` function:
``` r
library(rotl)
apes <- c("Pongo", "Pan", "Gorilla", "Hoolock", "Homo")
(resolved_names <- tnrs_match_names(apes))
```
## search_string unique_name approximate_match score ott_id is_synonym
## 1 pongo Pongo FALSE 1 417949 FALSE
## 2 pan Pan FALSE 1 417957 FALSE
## 3 gorilla Gorilla FALSE 1 417969 FALSE
## 4 hoolock Hoolock FALSE 1 712902 FALSE
## 5 homo Homo FALSE 1 770309 FALSE
## flags number_matches
## 1 2
## 2 sibling_higher 2
## 3 sibling_higher 1
## 4 1
## 5 sibling_higher 1
Now we can get the tree with just those tips:
``` r
tr <- tol_induced_subtree(ott_ids = ott_id(resolved_names))
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: mrcaott83926ott6145147, mrcaott83926ott3607728, mrcaott83926ott3607876,
## mrcaott83926ott3607873, mrcaott83926ott3607687, mrcaott83926ott3607716,
## mrcaott83926ott3607689, mrcaott83926ott3607732, mrcaott770295ott3607719,
## mrcaott770295ott3607692, Ponginae ott1082538, Hylobatidae ott166544
``` r
plot(tr)
```
The code above can be summarized in a single pipe:
``` r
library(magrittr)
## or expressed as a pipe:
c("Pongo", "Pan", "Gorilla", "Hoolock", "Homo") %>%
tnrs_match_names() %>%
ott_id() %>%
tol_induced_subtree() %>%
plot()
```
## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes with
## labels: mrcaott83926ott6145147, mrcaott83926ott3607728, mrcaott83926ott3607876,
## mrcaott83926ott3607873, mrcaott83926ott3607687, mrcaott83926ott3607716,
## mrcaott83926ott3607689, mrcaott83926ott3607732, mrcaott770295ott3607719,
## mrcaott770295ott3607692, Ponginae ott1082538, Hylobatidae ott166544
## Citation and Manuscript
To cite `rotl` in publications pleases use:
> Michonneau, F., Brown, J. W. and Winter, D. J. (2016), rotl: an R
> package to interact with the Open Tree of Life data. Methods in
> Ecology and Evolution. 7(12):1476-1481. doi:
> [10.1111/2041-210X.12593](https://doi.org/10.1111/2041-210X.12593)
You may also want to cite the paper for the Open Tree of Life
> Hinchliff, C. E., et al. (2015). Synthesis of phylogeny and taxonomy
> into a comprehensive tree of life. Proceedings of the National Academy
> of Sciences 112.41 (2015): 12764-12769 doi:
> [10.1073/pnas.1423041112](https://doi.org/10.1073/pnas.1423041112)
The manuscript in *Methods in Ecology and Evolution* includes additional
examples on how to use the package. The manuscript and the code it
contains are also hosted on GitHub at: