Title: | Identify Rogue Taxa in Sets of Phylogenetic Trees |
---|---|
Description: | Rogue ("wildcard") taxa are leaves with uncertain phylogenetic position. Their position may vary from tree to tree under inference methods that yield a tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony). The presence of rogue taxa in a tree set can potentially remove all information from a consensus tree. The information content of a consensus tree - a function of its resolution and branch support values - can often be increased by removing rogue taxa. 'Rogue' provides an explicitly information-theoretic approach to rogue detection (Smith 2022) <doi:10.1093/sysbio/syab099>, and an interface to 'RogueNaRok' (Aberer et al. 2013) <doi:10.1093/sysbio/sys078>. |
Authors: | Martin R. Smith [aut, cre, cph] , Andre J. Aberer [aut, cph] |
Maintainer: | Martin R. Smith <[email protected]> |
License: | GPL (>= 3) |
Version: | 2.1.6 |
Built: | 2024-10-31 09:19:23 UTC |
Source: | https://github.com/ms609/Rogue |
RogueTaxa()
finds wildcard leaves whose removal increases the resolution
or branch support values of a consensus tree, using the relative
bipartition, shared phylogenetic, or mutual clustering concepts of
information.
RogueTaxa( trees, info = c("spic", "scic", "fspic", "fscic", "rbic"), return = c("taxa", "tree"), bestTree = NULL, computeSupport = TRUE, dropsetSize = 1, neverDrop = character(0), labelPenalty = 0, mreOptimization = FALSE, threshold = 50, verbose = FALSE ) QuickRogue( trees, info = "phylogenetic", p = 0.5, log = TRUE, average = "median", deviation = "mad", neverDrop, fullSeq = FALSE, parallel = FALSE ) C_RogueNaRok( bootTrees = "", runId = "tmp", treeFile = "", computeSupport = TRUE, dropsetSize = 1, excludeFile = "", workDir = "", labelPenalty = 0, mreOptimization = FALSE, threshold = 50 )
RogueTaxa( trees, info = c("spic", "scic", "fspic", "fscic", "rbic"), return = c("taxa", "tree"), bestTree = NULL, computeSupport = TRUE, dropsetSize = 1, neverDrop = character(0), labelPenalty = 0, mreOptimization = FALSE, threshold = 50, verbose = FALSE ) QuickRogue( trees, info = "phylogenetic", p = 0.5, log = TRUE, average = "median", deviation = "mad", neverDrop, fullSeq = FALSE, parallel = FALSE ) C_RogueNaRok( bootTrees = "", runId = "tmp", treeFile = "", computeSupport = TRUE, dropsetSize = 1, excludeFile = "", workDir = "", labelPenalty = 0, mreOptimization = FALSE, threshold = 50 )
trees |
List of trees to analyse. |
info |
Concept of information to employ; see details. |
return |
If |
computeSupport |
Logical: If |
dropsetSize |
Integer specifying maximum size of dropset per iteration.
If |
neverDrop |
Tip labels that should not be dropped from the consensus. |
labelPenalty |
A weight factor to penalize for dropset size when
|
threshold , mreOptimization
|
A threshold or mode for the consensus tree
that is optimized. Specify a value between 50 (majority rule consensus,
the default) and 100 (strict consensus), or set |
verbose |
Logical specifying whether to display output from RogueNaRok.
If |
p |
Proportion of trees that must contain a split before it is included in the consensus under consideration. 0.5, the default, corresponds to a majority rule tree; 1.0 will maximize the information content of the strict consensus. |
log |
Logical specifying whether to log-transform distances when calculating leaf stability. |
average |
Character specifying whether to use |
deviation |
Character specifying whether to use |
fullSeq |
Logical specifying whether to list all taxa ( |
parallel |
Logical specifying whether parallel execution should take place in C++. |
bootTrees |
Path to a file containing a collection of bootstrap trees. |
runId |
An identifier for this run, appended to output files. |
treeFile , bestTree
|
If a single best-known tree (such as an ML or MP tree)
is provided, RogueNaRok optimizes the bootstrap support in this
best-known tree (still drawn from the bootstrap trees);
the |
excludeFile |
Taxa in this file (one taxon per line) will not be considered for pruning. |
workDir |
Path to a working directory where output files are created. |
"Rogue" or (loosely) "wildcard" taxa (Nixon and Wheeler 1992) are leaves whose position in a tree is poorly constrained, typically because much of the phylogenetic data associated with the taxon is either missing or in conflict with other data (Kearney 2002).
These functions use heuristic methods to identify rogue taxa whose removal improves the information content of a consensus tree, by the definitions of information discussed below.
RogueTaxa()
returns a data.frame
. Each row after the first,
which describes the starting tree, describes a dropset operation.
Columns describe:
num
: Sequential index of the drop operation
taxNum
: Numeric identifier of the dropped leaves
taxon
: Text identifier of dropped leaves
rawImprovement
: Improvement in score obtained by this operation
IC
: Information content of tree after dropping all leaves so far,
by the measure indicated by info
.
QuickRogue()
returns a data.frame
in the same format as
RogueTaxa()
.
C_RogueNaRok()
returns 0
if successful; -1
on error.
QuickRogue()
: Shortcut to "fast" heuristic, with option to return
evaluation of all taxa using fullSeq = TRUE
.
The splitwise phylogenetic information content measure produces the best results (Smith 2022). It uses the splitwise information content as a shortcut, which involves double counting of some information (which may or may not be desirable). The same holds for the mutual clustering information measure; this measure is less obviously suited to the detection of rogues. This measure interprets split frequency as a proxy for the probability that a split is true, which is a valid interpretation of a Bayesian posterior sample (Holder et al. 2008), a reasonable but imperfect interpretation of a bootstrap sample (Berry and Gascuel 1996), and a bad interpretation of a sample of most parsimonious trees.
The "relative bipartition information criterion" (RBIC) is the sum of all support values divided by the maximum possible support in a fully bifurcating tree with the initial set of taxa. The relative bipartition information content approach employs the 'RogueNaRok' implementation (Aberer et al. 2013), which can handle large trees relatively quickly. The RBIC is is not strictly a measure of information and can produce undesirable results (Wilkinson and Crotti 2017).
C_RogueNaRok()
directly interfaces the 'RogueNaRok' C implementation,
with no input checking; be aware that invalid input will cause undefined
behaviour and is likely to crash R.
Martin R. Smith ([email protected]), linking to RogueNaRok C library by Andre Aberer (<andre.aberer at googlemail.com>)
Aberer AJ, Krompass D, Stamatakis A (2013).
“Pruning rogue taxa improves phylogenetic accuracy: an efficient algorithm and webservice.”
Systematic Biology, 62(1), 162–166.
doi:10.1093/sysbio/sys078.
Berry V, Gascuel O (1996).
“On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain.”
Molecular Biology and Evolution, 13(7), 999–1011.
doi:10.1093/molbev/13.7.999.
Holder MT, Sukumaran J, Lewis PO (2008).
“A justification for reporting the majority-rule consensus tree in Bayesian phylogenetics.”
Systematic Biology, 57(5), 814–821.
doi:10.1080/10635150802422308.
Kearney M (2002).
“Fragmentary taxa, missing data, and ambiguity: mistaken assumptions and conclusions.”
Systematic Biology, 51(2), 369–381.
doi:10.1080/10635150252899824.
Nixon KC, Wheeler QD (1992).
“Extinction and the origin of species.”
In Novacek MJ, Wheeler QD (eds.), Extinction and phylogeny, chapter Extinction and the origin of species, 119-143.
Columbia University Press, New York.
Smith MR (2022).
“Using information theory to detect rogue taxa and improve consensus trees.”
Systematic Biology, 71(5), 986–1008.
doi:10.1093/sysbio/syab099.
Wilkinson M, Crotti M (2017).
“Comments on detecting rogue taxa using RogueNaRok.”
Systematics and Biodiversity, 15(4), 291–295.
doi:10.1080/14772000.2016.1252440.
library("TreeTools", warn.conflicts = FALSE) trees <- list(read.tree(text = ("(a, (b, (c, (d, (e, (X1, X2))))));")), read.tree(text = ("((a, (X1, X2)), (b, (c, (d, e))));"))) RogueTaxa(trees, dropsetSize = 2) trees <- list( read.tree(text = "((a, y), (b, (c, (z, ((d, e), (f, (g, x)))))));"), read.tree(text = "(a, (b, (c, (z, (((d, y), e), (f, (g, x)))))));"), read.tree(text = "(a, (b, ((c, z), ((d, (e, y)), ((f, x), g)))));"), read.tree(text = "(a, (b, ((c, z), ((d, (e, x)), (f, (g, y))))));"), read.tree(text = "(a, ((b, x), ((c, z), ((d, e), (f, (g, y))))));") ) cons <- consensus(trees, p = 0.5) plot(cons) LabelSplits(cons, SplitFrequency(cons, trees) / length(trees)) reduced <- RogueTaxa(trees, info = "phylogenetic", ret = "tree") plot(reduced) LabelSplits(reduced, SplitFrequency(reduced, trees) / length(trees)) QuickRogue(trees, fullSeq = TRUE) bootTrees <- system.file("example/150.bs", package = "Rogue") tmpDir <- tempdir() XX <- capture.output( # Don't print verbose run details to console C_RogueNaRok(bootTrees, workDir = tmpDir) ) # Results have been written to our temporary directory oldwd <- setwd(tmpDir) head(read.table("RogueNaRok_droppedRogues.tmp", header = TRUE)) # Delete temporary files file.remove("RogueNaRok_droppedRogues.tmp") file.remove("RogueNaRok_info.tmp") setwd(oldwd)
library("TreeTools", warn.conflicts = FALSE) trees <- list(read.tree(text = ("(a, (b, (c, (d, (e, (X1, X2))))));")), read.tree(text = ("((a, (X1, X2)), (b, (c, (d, e))));"))) RogueTaxa(trees, dropsetSize = 2) trees <- list( read.tree(text = "((a, y), (b, (c, (z, ((d, e), (f, (g, x)))))));"), read.tree(text = "(a, (b, (c, (z, (((d, y), e), (f, (g, x)))))));"), read.tree(text = "(a, (b, ((c, z), ((d, (e, y)), ((f, x), g)))));"), read.tree(text = "(a, (b, ((c, z), ((d, (e, x)), (f, (g, y))))));"), read.tree(text = "(a, ((b, x), ((c, z), ((d, e), (f, (g, y))))));") ) cons <- consensus(trees, p = 0.5) plot(cons) LabelSplits(cons, SplitFrequency(cons, trees) / length(trees)) reduced <- RogueTaxa(trees, info = "phylogenetic", ret = "tree") plot(reduced) LabelSplits(reduced, SplitFrequency(reduced, trees) / length(trees)) QuickRogue(trees, fullSeq = TRUE) bootTrees <- system.file("example/150.bs", package = "Rogue") tmpDir <- tempdir() XX <- capture.output( # Don't print verbose run details to console C_RogueNaRok(bootTrees, workDir = tmpDir) ) # Results have been written to our temporary directory oldwd <- setwd(tmpDir) head(read.table("RogueNaRok_droppedRogues.tmp", header = TRUE)) # Delete temporary files file.remove("RogueNaRok_droppedRogues.tmp") file.remove("RogueNaRok_info.tmp") setwd(oldwd)
TipInstability()
calculates the instability of each leaf in a tree.
Unstable leaves are likely to display roguish behaviour.
TipInstability( trees, log = TRUE, average = "mean", deviation = "sd", checkTips = TRUE, parallel = FALSE ) ColByStability( trees, log = TRUE, average = "mean", deviation = "sd", pal = hcl.colors(131, "inferno")[1:101] )
TipInstability( trees, log = TRUE, average = "mean", deviation = "sd", checkTips = TRUE, parallel = FALSE ) ColByStability( trees, log = TRUE, average = "mean", deviation = "sd", pal = hcl.colors(131, "inferno")[1:101] )
trees |
List of trees to analyse. |
log |
Logical specifying whether to log-transform distances when calculating leaf stability. |
average |
Character specifying whether to use |
deviation |
Character specifying whether to use |
checkTips |
Logical specifying whether to check that tips are numbered consistently. |
parallel |
Logical specifying whether parallel execution should take place in C++. |
pal |
A vector listing a sequence of colours to be used for plotting. The earliest entries will be assigned to the most stable tips. |
Smith (2022) defines the instability of a pair of leaves as the median absolute divergence in the graph geodesic (the number of edges in the shortest path between the leaves) across all trees, normalized against the mean graph geodesic. The instability of a single leaf is the mean instability of all pairs that include that leaf; higher values characterise leaves whose position is more variable between trees.
Other concepts of leaf instability include
The "taxonomic instability index", as implemented in Mesquite:
described by Thomson and Shaffer (2010) as
,
where
is the patristic distance (i.e. length of edges)
between leaves
and
in tree
.
the average stability of triplets (i.e. quartets including the root) that include the leaf (Thorley and Wilkinson 1999), implemented in "Phyutility" (Smith and Dunn 2008); and related to "positional congruence" measures (Estabrook 1992; Pol and Escapa 2009).
ColByStability()
returns a named character vector that assigns a
colour to each leaf in trees
according to their stability.
Martin R. Smith ([email protected])
Estabrook GF (1992).
“Evaluating undirected positional congruence of individual taxa between two estimates of the phylogenetic tree for a group of taxa.”
Systematic Biology, 41(2), 172–177.
doi:10.1093/sysbio/41.2.172.
Pol D, Escapa IH (2009).
“Unstable taxa in cladistic analysis: identification and the assessment of relevant characters.”
Cladistics, 25(5), 515–527.
doi:10.1111/j.1096-0031.2009.00258.x.
Smith MR (2022).
“Using information theory to detect rogue taxa and improve consensus trees.”
Systematic Biology, 71(5), 986–1008.
doi:10.1093/sysbio/syab099.
Smith SA, Dunn CW (2008).
“Phyutility: a phyloinformatics tool for trees, alignments and molecular data.”
Bioinformatics, 24(5), 715–716.
doi:10.1093/bioinformatics/btm619.
Thomson RC, Shaffer HB (2010).
“Sparse supermatrices for phylogenetic inference: taxonomy, alignment, rogue taxa, and the phylogeny of living turtles.”
Systematic Biology, 59(1), 42–58.
doi:10.1093/sysbio/syp075.
Thorley JL, Wilkinson M (1999).
“Testing the phylogenetic stability of early tetrapods.”
Journal of Theoretical Biology, 200(3), 343–344.
doi:10.1006/jtbi.1999.0999.
Other tip instability functions:
TipVolatility()
library("TreeTools", quietly = TRUE) # Generate some trees with a rogue taxon trees <- AddTipEverywhere(BalancedTree(8), "Rogue")[3:6] # Display the strict consensus plot(consensus(trees), tip.col = ColByStability(trees)) # Add a legend for the colour scale used PlotTools::SpectrumLegend( "bottomleft", bty = "n", # No box legend = c("Unstable", "", "Stable"), palette = hcl.colors(131, "inferno")[1:101] ) # Calculate leaf stability instab <- TipInstability(trees, log = FALSE, ave = "mean", dev = "mad") # Plot a consensus that omits the least stable leaves plot(ConsensusWithout(trees, names(instab[instab > 0.2])))
library("TreeTools", quietly = TRUE) # Generate some trees with a rogue taxon trees <- AddTipEverywhere(BalancedTree(8), "Rogue")[3:6] # Display the strict consensus plot(consensus(trees), tip.col = ColByStability(trees)) # Add a legend for the colour scale used PlotTools::SpectrumLegend( "bottomleft", bty = "n", # No box legend = c("Unstable", "", "Stable"), palette = hcl.colors(131, "inferno")[1:101] ) # Calculate leaf stability instab <- TipInstability(trees, log = FALSE, ave = "mean", dev = "mad") # Plot a consensus that omits the least stable leaves plot(ConsensusWithout(trees, names(instab[instab > 0.2])))
Calculate the volatility of each tip: namely, the impact on the mean phylogenetic information distance (Smith 2020) between trees when that tip is removed. Effective when the number of trees is small.
TipVolatility(trees)
TipVolatility(trees)
trees |
List of trees to analyse. |
TipVolatility()
returns a named vector listing the volatility
index calculated for each leaf.
Smith MR (2020). “Information theoretic Generalized Robinson–Foulds metrics for comparing phylogenetic trees.” Bioinformatics, 36(20), 5007–5013. doi:10.1093/bioinformatics/btaa614.
Other tip instability functions:
TipInstability()
library("TreeTools", quietly = TRUE) # Generate some trees with two rogue taxa trees <- AddTipEverywhere(BalancedTree(8), "Rogue") trees[] <- lapply(trees, AddTip, "Rogue", "Rogue2") # Calculate tip volatility sb <- TipVolatility(trees) # Use volatility to colour leaves in consensus tree sbNorm <- 1 + (99 * (sb - min(sb)) / (max(sb - min(sb)))) col <- hcl.colors(128, "inferno")[sbNorm] plot(consensus(trees), tip.color = col) # Add a legend for the colour scale used PlotTools::SpectrumLegend( "bottomleft", bty = "n", # Suppress box inset = -0.02, # Avoid overlap title = "Volatility", legend = signif(seq(max(sb), min(sb), length.out = 4), 3), palette = hcl.colors(128, "inferno") ) # Plot consensus after removing highly volatile taxa plot(ConsensusWithout(trees, names(sb[sb == max(sb)])))
library("TreeTools", quietly = TRUE) # Generate some trees with two rogue taxa trees <- AddTipEverywhere(BalancedTree(8), "Rogue") trees[] <- lapply(trees, AddTip, "Rogue", "Rogue2") # Calculate tip volatility sb <- TipVolatility(trees) # Use volatility to colour leaves in consensus tree sbNorm <- 1 + (99 * (sb - min(sb)) / (max(sb - min(sb)))) col <- hcl.colors(128, "inferno")[sbNorm] plot(consensus(trees), tip.color = col) # Add a legend for the colour scale used PlotTools::SpectrumLegend( "bottomleft", bty = "n", # Suppress box inset = -0.02, # Avoid overlap title = "Volatility", legend = signif(seq(max(sb), min(sb), length.out = 4), 3), palette = hcl.colors(128, "inferno") ) # Plot consensus after removing highly volatile taxa plot(ConsensusWithout(trees, names(sb[sb == max(sb)])))