Title:	Phylogenetic Reconstruction and Analysis
Version:	2.12.1
Description:	Allows for estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017).
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://github.com/KlausVigo/phangorn, https://klausvigo.github.io/phangorn/
BugReports:	https://github.com/KlausVigo/phangorn/issues
Depends:	ape (≥ 5.8), R (≥ 4.1.0)
Imports:	digest, fastmatch, generics, graphics, grDevices, igraph (≥ 1.0), Matrix, methods, parallel, quadprog, Rcpp, stats, utils
Suggests:	apex, Biostrings, ggseqlogo, ggplot2, knitr, magick, rgl, rmarkdown, seqinr, testthat (≥ 3.0.0), tinytest, vdiffr, xtable
LinkingTo:	Rcpp
VignetteBuilder:	knitr, utils
biocViews:	Software, Technology, QualityControl
Encoding:	UTF-8
Repository:	CRAN
RoxygenNote:	7.3.2
Language:	en-US
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2024-09-17 15:09:22 UTC; klaus
Author:	Klaus Schliep [aut, cre], Emmanuel Paradis [aut], Leonardo de Oliveira Martins [aut], Alastair Potts [aut], Iris Bardel-Kahr [aut], Tim W. White [ctb], Cyrill Stachniss [ctb], Michelle Kendall [ctb], Keren Halabi [ctb], Richel Bilderbeek [ctb], Kristin Winchell [ctb], Liam Revell [ctb], Mike Gilchrist [ctb], Jeremy Beaulieu [ctb], Brian O'Meara [ctb], Long Qu [ctb], Joseph Brown [ctb], Santiago Claramunt [ctb]
Maintainer:	Klaus Schliep <klaus.schliep@gmail.com>
Date/Publication:	2024-09-17 17:00:02 UTC

phangorn: Phylogenetic Reconstruction and Analysis

Description

Allows for estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation (Schliep 2011). Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017).

Author(s)

Maintainer: Klaus Schliep klaus.schliep@gmail.com (ORCID)

Authors:

• Emmanuel Paradis (ORCID)

• Leonardo de Oliveira Martins (ORCID)

• Alastair Potts

• Iris Bardel-Kahr (ORCID)

Other contributors:

• Tim W. White [contributor]

• Cyrill Stachniss [contributor]

• Michelle Kendall m.kendall@imperial.ac.uk [contributor]

• Keren Halabi [contributor]

• Richel Bilderbeek [contributor]

• Kristin Winchell [contributor]

• Liam Revell [contributor]

• Mike Gilchrist [contributor]

• Jeremy Beaulieu [contributor]

• Brian O'Meara [contributor]

• Long Qu [contributor]

• Joseph Brown (ORCID) [contributor]

• Santiago Claramunt (ORCID) [contributor]

See Also

Useful links:

• https://github.com/KlausVigo/phangorn

• https://klausvigo.github.io/phangorn/

• Report bugs at https://github.com/KlausVigo/phangorn/issues

Parsimony tree.

Description

pratchet implements the parsimony ratchet (Nixon, 1999) and is the preferred way to search for the best parsimony tree. For small number of taxa the function bab can be used to compute all most parsimonious trees.

Usage

acctran(tree, data)

fitch(tree, data, site = "pscore")

random.addition(data, tree = NULL, method = "fitch")

parsimony(tree, data, method = "fitch", cost = NULL, site = "pscore")

optim.parsimony(tree, data, method = "fitch", cost = NULL, trace = 1,
  rearrangements = "SPR", ...)

pratchet(data, start = NULL, method = "fitch", maxit = 1000,
  minit = 100, k = 10, trace = 1, all = FALSE,
  rearrangements = "SPR", perturbation = "ratchet", ...)

sankoff(tree, data, cost = NULL, site = "pscore")

Arguments

Details

parsimony returns the parsimony score of a tree using either the sankoff or the fitch algorithm. optim.parsimony optimizes the topology using either Nearest Neighbor Interchange (NNI) rearrangements or sub tree pruning and regrafting (SPR) and is used inside pratchet. random.addition can be used to produce starting trees and is an option for the argument perturbation in pratchet.

The "SPR" rearrangements are so far only available for the "fitch" method, "sankoff" only uses "NNI". The "fitch" algorithm only works correct for binary trees.

Value

parsimony returns the maximum parsimony score (pscore). optim.parsimony returns a tree after NNI rearrangements. pratchet returns a tree or list of trees containing the best tree(s) found during the search. acctran returns a tree with edge length according to the ACCTRAN criterion.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates, Sunderland.

Nixon, K. (1999) The Parsimony Ratchet, a New Method for Rapid Parsimony Analysis. Cladistics 15, 407-414

See Also

bab, CI, RI, ancestral.pml, nni, NJ, pml, getClans, ancestral.pars, bootstrap.pml

Examples

set.seed(3)
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
parsimony(tree, Laurasiatherian)
treeRA <- random.addition(Laurasiatherian)
treeSPR <- optim.parsimony(tree, Laurasiatherian)

# lower number of iterations for the example (to run less than 5 seconds),
# keep default values (maxit, minit, k) or increase them for real life
# analyses.
treeRatchet <- pratchet(Laurasiatherian, start=tree, maxit=100,
                        minit=5, k=5, trace=0)
# assign edge length (number of substitutions)
treeRatchet <- acctran(treeRatchet, Laurasiatherian)
# remove edges of length 0
treeRatchet <- di2multi(treeRatchet)

plot(midpoint(treeRatchet))
add.scale.bar(0,0, length=100)

parsimony(c(tree,treeSPR, treeRatchet), Laurasiatherian)

Draw Confidences Intervals on Phylogenies

Description

These are low-level plotting commands to draw the confidence intervals on the node of a tree as rectangles with coloured backgrounds or add boxplots to ultrametric or tipdated trees.

Usage

add_ci(tree, trees, col95 = "#FF00004D", col50 = "#0000FF4D",
  height = 0.7, legend = TRUE, ...)

add_boxplot(tree, trees, ...)

Arguments

Details

All trees should to be rooted, either ultrametric or tip dated.

Value

Nothing. Function is called for adding to a plot.

Author(s)

Emmanuel Paradis, Santiago Claramunt, Joseph Brown, Klaus Schliep

See Also

plot.phylo, plotBS, add_edge_length, maxCladeCred

Examples

data("Laurasiatherian")
dm <- dist.hamming(Laurasiatherian)
tree <- upgma(dm)
set.seed(123)
trees <- bootstrap.phyDat(Laurasiatherian,
                          FUN=function(x)upgma(dist.hamming(x)), bs=100)
tree <- plotBS(tree, trees, "phylogram")
add_ci(tree, trees)
plot(tree, direction="downwards")
add_boxplot(tree, trees, boxwex=.7)

Assign and compute edge lengths from a sample of trees

Description

This command can infer some average edge lengths and assign them from a (bootstrap/MCMC) sample.

Usage

add_edge_length(tree, trees, fun = function(x) median(na.omit(x)),
  rooted = all(is.rooted(trees)))

Arguments

Value

The tree with newly assigned edge length.

Author(s)

Klaus Schliep

See Also

node.depth, consensus, maxCladeCred, add_boxplot

Examples

data("Laurasiatherian")
set.seed(123)
bs <- bootstrap.phyDat(Laurasiatherian,
                FUN=function(x)upgma(dist.ml(x)), bs=100)
tree_compat <- allCompat(bs, rooted=TRUE) |>
              add_edge_length(bs)
plot(tree_compat)
add_boxplot(tree_compat, bs, boxwex=.7)

Add tips to a tree

Description

This function binds tips to nodes of a phylogenetic trees.

Usage

add.tips(tree, tips, where, edge.length = NULL)

Arguments

Value

an object of class phylo

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

bind.tree

Examples

tree <- rcoal(10)
plot(tree)
nodelabels()
tiplabels()
tree1 <- add.tips(tree, c("A", "B", "C"), c(1,2,15))
plot(tree1)

Compare splits and add support values to an object

Description

Add support values to a splits, phylo or networx object.

Usage

addConfidences(x, y, ...)

## S3 method for class 'phylo'
addConfidences(x, y, rooted = FALSE, ...)

presenceAbsence(x, y)

createLabel(x, y, label_y, type = "edge", nomatch = NA)

Arguments

Value

The object x with added bootstrap / MCMC support values.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017), Intertwining phylogenetic trees and networks. Methods Ecol Evol.8, 1212–1220. doi:10.1111/2041-210X.12760

See Also

as.splits, as.networx, RF.dist, plot.phylo

Examples

data(woodmouse)
woodmouse <- phyDat(woodmouse)
tmpfile <- normalizePath(system.file(
             "extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn"))
boot_trees <- read.tree(tmpfile)

dm <- dist.ml(woodmouse)
tree <- upgma(dm)
nnet <- neighborNet(dm)

tree <- addConfidences(tree, boot_trees)
nnet <- addConfidences(nnet, boot_trees)

plot(tree, show.node.label=TRUE)
plot(nnet, show.edge.label=TRUE)

Splits representation of graphs and trees.

Description

as.splits produces a list of splits or bipartitions.

Usage

allSplits(k, labels = NULL)

allCircularSplits(k, labels = NULL)

as.splits(x, ...)

## S3 method for class 'splits'
as.matrix(x, zero.print = 0L, one.print = 1L, ...)

## S3 method for class 'splits'
as.Matrix(x, ...)

## S3 method for class 'splits'
print(x, maxp = getOption("max.print"), zero.print = ".",
  one.print = "|", ...)

## S3 method for class 'splits'
c(..., recursive = FALSE)

## S3 method for class 'splits'
unique(x, incomparables = FALSE, unrooted = TRUE, ...)

## S3 method for class 'phylo'
as.splits(x, ...)

## S3 method for class 'multiPhylo'
as.splits(x, ...)

## S3 method for class 'networx'
as.splits(x, ...)

## S3 method for class 'splits'
as.prop.part(x, ...)

## S3 method for class 'splits'
as.bitsplits(x)

## S3 method for class 'bitsplits'
as.splits(x, ...)

compatible(obj1, obj2 = NULL)

Arguments

Value

as.splits returns an object of class splits, which is mainly a list of splits and some attributes. Often a splits object will contain attributes confidences for bootstrap or Bayesian support values and weight storing edge weights. compatible return a lower triangular matrix where an 1 indicates that two splits are incompatible.

Note

The internal representation is likely to change.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

prop.part, lento, as.networx, distanceHadamard, read.nexus.splits

Examples

(sp <- as.splits(rtree(5)))
write.nexus.splits(sp)
spl <- allCircularSplits(5)
plot(as.networx(spl))

Compute all trees topologies.

Description

allTrees computes all bifurcating tree topologies for rooted or unrooted trees with up to 10 tips. The number of trees grows fast.

Usage

allTrees(n, rooted = FALSE, tip.label = NULL)

Arguments

Value

an object of class multiPhylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

rtree, nni, howmanytrees, dfactorial

Examples

trees <- allTrees(5)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(3,5))
for(i in 1:15)plot(trees[[i]])
par(old.par)

tree utility function

Description

Functions for describing relationships among phylogenetic nodes.

Usage

Ancestors(x, node, type = c("all", "parent"))

allDescendants(x)

Children(x, node)

Descendants(x, node, type = c("tips", "children", "all"))

Siblings(x, node, include.self = FALSE)

mrca.phylo(x, node = NULL, full = FALSE)

Arguments

Details

These functions are inspired by treewalk in phylobase package, but work on the S3 phylo objects. The nodes are the indices as given in edge matrix of an phylo object. From taxon labels these indices can be easily derived matching against the tip.label argument of an phylo object, see example below. All the functions allow node to be either a scalar or vector. mrca is a faster version of the mrca in ape, in phangorn only because of dependencies. If the argument node is missing the function is evaluated for all nodes.

Value

a vector or a list containing the indices of the nodes.

Functions

• allDescendants(): list all the descendant nodes of each node

See Also

treewalk, as.phylo, nodelabels

Examples

tree <- rtree(10)
plot(tree, show.tip.label = FALSE)
nodelabels()
tiplabels()
Ancestors(tree, 1:3, "all")
Children(tree, 11)
Descendants(tree, 11, "tips")
Siblings(tree, 3)
# Siblings of all nodes
Siblings(tree)
mrca.phylo(tree, 1:3)
mrca.phylo(tree, match(c("t1", "t2", "t3"), tree$tip))
mrca.phylo(tree)
# same as mrca(tree), but faster for large trees

Ancestral character reconstruction.

Description

Marginal reconstruction of the ancestral character states.

Usage

ancestral.pml(object, type = "marginal", return = "prob", ...)

anc_pml(object, type = "marginal", ...)

## S3 method for class 'ancestral'
as.phyDat(x, ...)

## S3 method for class 'ancestral'
as.data.frame(x, ...)

ancestral.pars(tree, data, type = c("MPR", "ACCTRAN", "POSTORDER"),
  cost = NULL, return = "prob", ...)

anc_pars(tree, data, type = c("MPR", "ACCTRAN", "POSTORDER"), cost = NULL,
  ...)

pace(tree, data, type = c("MPR", "ACCTRAN", "POSTORDER"), cost = NULL,
  return = "prob", ...)

Arguments

Details

The argument "type" defines the criterion to assign the internal nodes. For ancestral.pml so far "ml and marginal (empirical) "bayes" and for ancestral.pars "MPR" and "ACCTRAN" are possible.

The function return a list containing the tree with node labels, the original alignment as an phyDat object, a data.frame containing the probabilities belonging to a state for all (internal nodes) and the most likely state. For parsimony and nucleotide data the most likely state might be ambiguous. For ML this is very unlikely to be the case.

If the input tree does not contain unique node labels the function ape::MakeNodeLabel is used to create them.

With parsimony reconstruction one has to keep in mind that there will be often no unique solution.

The functions use the node labels of the provided tree (also if part of the pml object) if these are unique. Otherwise the function ape::MakeNodeLabel is used to create them.

For further details see vignette("Ancestral").

Value

An object of class ancestral. This is a list containing the tree with node labels, the original alignment as an phyDat object, a data.frame containing the probabilities belonging to a state for all (internal nodes) and the most likely state.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates, Sunderland.

Swofford, D.L., Maddison, W.P. (1987) Reconstructing ancestral character states under Wagner parsimony. Math. Biosci. 87: 199–229

Yang, Z. (2006). Computational Molecular evolution. Oxford University Press, Oxford.

See Also

pml, parsimony, ace, plotAnc, latag2n.phyDat, latag2n, gap_as_state, root, makeNodeLabel

Examples

example(NJ)
# generate node labels to ensure plotting will work
tree <- makeNodeLabel(tree)
fit <- pml(tree, Laurasiatherian)
anc.ml <- anc_pml(fit)
anc.p <- anc_pars(tree, Laurasiatherian)
# plot ancestral sequences at the root
plotSeqLogo( anc.ml, 48, 1, 20)
plotSeqLogo( anc.p, 48, 1, 20)
# plot the first character
plotAnc(anc.ml)
# plot the third character
plotAnc(anc.ml, 3)

Conversion among phylogenetic network objects

Description

as.networx convert splits objects into a networx object. And most important there exists a generic plot function to plot phylogenetic network or split graphs.

Usage

as.networx(x, ...)

## S3 method for class 'splits'
as.networx(x, planar = FALSE, coord = "none", ...)

## S3 method for class 'phylo'
as.networx(x, ...)

Arguments

Details

A networx object hold the information for a phylogenetic network and extends the phylo object. Therefore some generic function for phylo objects will also work for networx objects. The argument planar = TRUE will create a planar split graph based on a cyclic ordering. These objects can be nicely plotted in "2D".

Value

an object of class networx.

Note

The internal representation is likely to change.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017), Intertwining phylogenetic trees and networks. Methods Ecol Evol. 8, 1212–1220. doi:10.1111/2041-210X.12760

See Also

consensusNet, neighborNet, splitsNetwork, hadamard, distanceHadamard, plot.networx, evonet, as.phylo

Examples

set.seed(1)
tree1 <- rtree(20, rooted=FALSE)
sp <- as.splits(rNNI(tree1, n=10))
net <- as.networx(sp)
plot(net)
## Not run: 
# also see example in consensusNet
example(consensusNet)

## End(Not run)

Likelihood of a tree.

Description

pml computes the likelihood of a phylogenetic tree given a sequence alignment and a model. optim.pml optimizes the different model parameters. For a more user-friendly interface see pml_bb.

Usage

as.pml(x, ...)

pml(tree, data, bf = NULL, Q = NULL, inv = 0, k = 1, shape = 1,
  rate = 1, model = NULL, site.rate = "gamma", ASC = FALSE, ...)

optim.pml(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
  optInv = FALSE, optGamma = FALSE, optEdge = TRUE, optRate = FALSE,
  optRooted = FALSE, control = pml.control(), model = NULL,
  rearrangement = ifelse(optNni, "NNI", "none"), subs = NULL,
  ratchet.par = ratchet.control(), ...)

## S3 method for class 'pml'
logLik(object, ...)

## S3 method for class 'pml'
anova(object, ...)

## S3 method for class 'pml'
vcov(object, ...)

## S3 method for class 'pml'
print(x, ...)

Arguments

Details

Base frequencies in pml can be supplied in different ways. For amino acid they are usually defined through specifying a model, so the argument bf does not need to be specified. Otherwise if bf=NULL, each state is given equal probability. It can be a numeric vector given the frequencies. Last but not least bf can be string "equal", "empirical" and for codon models additionally "F3x4".

The topology search uses a nearest neighbor interchange (NNI) and the implementation is similar to phyML. The option model in pml is only used for amino acid models. The option model defines the nucleotide model which is getting optimized, all models which are included in modeltest can be chosen. Setting this option (e.g. "K81" or "GTR") overrules options optBf and optQ. Here is a overview how to estimate different phylogenetic models with pml:

Via model in optim.pml the following nucleotide models can be specified: JC, F81, K80, HKY, TrNe, TrN, TPM1, K81, TPM1u, TPM2, TPM2u, TPM3, TPM3u, TIM1e, TIM1, TIM2e, TIM2, TIM3e, TIM3, TVMe, TVM, SYM and GTR. These models are specified as in Posada (2008).

So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blosum62", "Dayhoff_DCMut" and "JTT_DCMut") and additionally rate matrices and amino acid frequencies can be supplied.

It is also possible to estimate codon models (e.g. YN98), for details see also the chapter in vignette("phangorn-specials").

If the option 'optRooted' is set to TRUE than the edge lengths of rooted tree are optimized. The tree has to be rooted and by now ultrametric! Optimising rooted trees is generally much slower.

If rearrangement is set to stochastic a stochastic search algorithm similar to Nguyen et al. (2015). and for ratchet the likelihood ratchet as in Vos (2003). This should helps often to find better tree topologies, especially for larger trees.

Value

pml or optim.pml return a list of class pml, some are useful for further computations like

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution, 17, 368–376.

Felsenstein, J. (2004). Inferring Phylogenies. Sinauer Associates, Sunderland.

Yang, Z. (2006). Computational Molecular evolution. Oxford University Press, Oxford.

Adachi, J., P. J. Waddell, W. Martin, and M. Hasegawa (2000) Plastid genome phylogeny and a model of amino acid substitution for proteins encoded by chloroplast DNA. Journal of Molecular Evolution, 50, 348–358

Rota-Stabelli, O., Z. Yang, and M. Telford. (2009) MtZoa: a general mitochondrial amino acid substitutions model for animal evolutionary studies. Mol. Phyl. Evol, 52(1), 268–72

Whelan, S. and Goldman, N. (2001) A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Molecular Biology and Evolution, 18, 691–699

Le, S.Q. and Gascuel, O. (2008) LG: An Improved, General Amino-Acid Replacement Matrix Molecular Biology and Evolution, 25(7), 1307–1320

Yang, Z., R. Nielsen, and M. Hasegawa (1998) Models of amino acid substitution and applications to Mitochondrial protein evolution. Molecular Biology and Evolution, 15, 1600–1611

Abascal, F., D. Posada, and R. Zardoya (2007) MtArt: A new Model of amino acid replacement for Arthropoda. Molecular Biology and Evolution, 24, 1–5

Kosiol, C, and Goldman, N (2005) Different versions of the Dayhoff rate matrix - Molecular Biology and Evolution, 22, 193–199

L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A fast and effective stochastic algorithm for estimating maximum likelihood phylogenies. Molecular Biology and Evolution, 32, 268–274.

Vos, R. A. (2003) Accelerated Likelihood Surface Exploration: The Likelihood Ratchet. Systematic Biology, 52(3), 368–373

Yang, Z., and R. Nielsen (1998) Synonymous and nonsynonymous rate variation in nuclear genes of mammals. Journal of Molecular Evolution, 46, 409-418.

Lewis, P.O. (2001) A likelihood approach to estimating phylogeny from discrete morphological character data. Systematic Biology 50, 913–925.

See Also

pml_bb, bootstrap.pml, modelTest, pmlPart, pmlMix, plot.phylo, SH.test, ancestral.pml

Examples

  example(NJ)
# Jukes-Cantor (starting tree from NJ)
  fitJC <- pml(tree, Laurasiatherian)
# optimize edge length parameter
  fitJC <- optim.pml(fitJC)
  fitJC

## Not run: 
# search for a better tree using NNI rearrangements
  fitJC <- optim.pml(fitJC, optNni=TRUE)
  fitJC
  plot(fitJC$tree)

# JC + Gamma + I - model
  fitJC_GI <- update(fitJC, k=4, inv=.2)
# optimize shape parameter + proportion of invariant sites
  fitJC_GI <- optim.pml(fitJC_GI, optGamma=TRUE, optInv=TRUE)
# GTR + Gamma + I - model
  fitGTR <- optim.pml(fitJC_GI, rearrangement = "stochastic",
      optGamma=TRUE, optInv=TRUE, model="GTR")

## End(Not run)


# 2-state data (RY-coded)
  dat <- acgt2ry(Laurasiatherian)
  fit2ST <- pml(tree, dat)
  fit2ST <- optim.pml(fit2ST,optNni=TRUE)
  fit2ST
# show some of the methods available for class pml
  methods(class="pml")

Branch and bound for finding all most parsimonious trees

Description

bab finds all most parsimonious trees.

Usage

bab(data, tree = NULL, trace = 0, ...)

Arguments

Details

This implementation is very slow and depending on the data may take very long time. In the worst case all (2n-5)!! = 1 \times 3 \times 5 \times \ldots \times (2n-5) possible trees have to be examined, where n is the number of species / tips. For ten species there are already 2027025 tip-labelled unrooted trees. It only uses some basic strategies to find a lower and upper bounds similar to penny from phylip. bab uses a very basic heuristic approach of MinMax Squeeze (Holland et al. 2005) to improve the lower bound. On the positive side bab is not like many other implementations restricted to binary or nucleotide data.

Value

bab returns all most parsimonious trees in an object of class multiPhylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com based on work on Liam Revell

References

Hendy, M.D. and Penny D. (1982) Branch and bound algorithms to determine minimal evolutionary trees. Math. Biosc. 59, 277-290

Holland, B.R., Huber, K.T. Penny, D. and Moulton, V. (2005) The MinMax Squeeze: Guaranteeing a Minimal Tree for Population Data, Molecular Biology and Evolution, 22, 235–242

White, W.T. and Holland, B.R. (2011) Faster exact maximum parsimony search with XMP. Bioinformatics, 27(10),1359–1367

See Also

pratchet, dfactorial

Examples

data(yeast)
dfactorial(11)
# choose only the first two genes
gene12 <- yeast[, 1:3158]
trees <- bab(gene12)

Summaries of alignments

Description

baseFreq computes the frequencies (absolute or relative) of the states from a sample of sequences. glance computes some useful information about the alignment. composition\_test computes a \chi^2-test testing if the state composition for a species differs.

Usage

baseFreq(obj, freq = FALSE, all = FALSE, drop.unused.levels = FALSE)

## S3 method for class 'phyDat'
glance(x, ...)

composition_test(obj)

Arguments

Value

baseFreq returns a named vector and glance a one row data.frame.

Author(s)

Klaus Schliep

See Also

phyDat, base.freq, glance

Examples

data(Laurasiatherian)
data(chloroplast)
# base frequencies
baseFreq(Laurasiatherian)
baseFreq(Laurasiatherian, all=TRUE)
baseFreq(Laurasiatherian, freq=TRUE)
baseFreq(chloroplast)
glance(Laurasiatherian)
glance(chloroplast)
composition_test(Laurasiatherian)[1:10,]

Bootstrap

Description

bootstrap.pml performs (non-parametric) bootstrap analysis and bootstrap.phyDat produces a list of bootstrapped data sets. plotBS plots a phylogenetic tree with the bootstrap values assigned to the (internal) edges.

Usage

bootstrap.pml(x, bs = 100, trees = TRUE, multicore = FALSE,
  mc.cores = NULL, tip.dates = NULL, ...)

bootstrap.phyDat(x, FUN, bs = 100, multicore = FALSE, mc.cores = NULL,
  jumble = TRUE, ...)

Arguments

Details

It is possible that the bootstrap is performed in parallel, with help of the multicore package. Unfortunately the multicore package does not work under windows or with GUI interfaces ("aqua" on a mac). However it will speed up nicely from the command line ("X11").

Value

bootstrap.pml returns an object of class multi.phylo or a list where each element is an object of class pml. plotBS returns silently a tree, i.e. an object of class phylo with the bootstrap values as node labels. The argument BStrees is optional and if not supplied the tree with labels supplied in the node.label slot.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein J. (1985) Confidence limits on phylogenies. An approach using the bootstrap. Evolution 39, 783–791

Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D., De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature, 556(7702), 452–456.

Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree construction. Cladistics 1, 266–278

Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary trees. Molecular Biology and Evolution 3, 403–417

See Also

optim.pml, pml, plot.phylo, maxCladeCred nodelabels,consensusNet and SOWH.test for parametric bootstrap

Examples

## Not run: 
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
# NJ
set.seed(123)
NJtrees <- bootstrap.phyDat(Laurasiatherian,
     FUN=function(x)NJ(dist.hamming(x)), bs=100)
treeNJ <- plotBS(tree, NJtrees, "phylogram")

# Maximum likelihood
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit, rearrangement="NNI")
set.seed(123)
bs <- bootstrap.pml(fit, bs=100, optNni=TRUE)
treeBS <- plotBS(fit$tree,bs)

# Maximum parsimony
treeMP <- pratchet(Laurasiatherian)
treeMP <- acctran(treeMP, Laurasiatherian)
set.seed(123)
BStrees <- bootstrap.phyDat(Laurasiatherian, pratchet, bs = 100)
treeMP <- plotBS(treeMP, BStrees, "phylogram")
add.scale.bar()

# export tree with bootstrap values as node labels
# write.tree(treeBS)

## End(Not run)

Chloroplast alignment

Description

Amino acid alignment of 15 genes of 19 different chloroplast.

Examples

data(chloroplast)
chloroplast

Consistency Index and Retention Index

Description

CI and RI compute the Consistency Index (CI) and Retention Index (RI).

Usage

CI(tree, data, cost = NULL, sitewise = FALSE)

RI(tree, data, cost = NULL, sitewise = FALSE)

Arguments

Details

The Consistency Index is defined as minimum number of changes divided by the number of changes required on the tree (parsimony score). The Consistency Index is equal to one if there is no homoplasy. And the Retention Index is defined as

RI = \frac{MaxChanges - ObsChanges}{MaxChanges - MinChanges}

Value

a scalar or vector with the CI/RI vector.

See Also

parsimony, pratchet, fitch, sankoff, bab, ancestral.pars

Examples

example(as.phylo.formula)
lab <- tr$tip.label
lab[79] <- "Herpestes fuscus"
tr$tip.label <- abbreviateGenus(lab)
X <- matrix(0, 112, 3, dimnames = list(tr$tip.label, c("Canis", "Panthera",
            "Canis_Panthera")))
desc_canis <- Descendants(tr, "Canis")[[1]]
desc_panthera <- Descendants(tr, "Panthera")[[1]]
X[desc_canis, c(1,3)] <- 1
X[desc_panthera, c(2,3)] <- 1
col <- rep("black", 112)
col[desc_panthera] <- "red"
col[desc_canis] <- "blue"
X <- phyDat(X, "USER", levels=c(0,1))
plot(tr, "f", tip.color=col)
# The first two sites are homoplase free!
CI(tr, X, sitewise=TRUE)
RI(tr, X, sitewise=TRUE)

Utility function to plot.phylo

Description

cladePar can help you coloring (choosing edge width/type) of clades.

Usage

cladePar(tree, node, edge.color = "red", tip.color = edge.color,
  edge.width = 1, edge.lty = "solid", x = NULL, plot = FALSE, ...)

Arguments

Value

A list containing the information about the edges and tips.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

plot.phylo

Examples

tree <- rtree(10)
plot(tree)
nodelabels()
x <- cladePar(tree, 12)
cladePar(tree, 18, "blue", "blue", x=x, plot=TRUE)

Species Tree

Description

coalSpeciesTree estimates species trees and can handle multiple individuals per species.

Usage

coalSpeciesTree(tree, X = NULL, sTree = NULL)

Arguments

Details

coalSpeciesTree estimates a single linkage tree as suggested by Liu et al. (2010) from the element wise minima of the cophenetic matrices of the gene trees. It extends speciesTree in ape as it allows that have several individuals per gene tree.

Value

The function returns an object of class phylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com Emmanuel Paradies

References

Liu, L., Yu, L. and Pearl, D. K. (2010) Maximum tree: a consistent estimator of the species tree. Journal of Mathematical Biology, 60, 95–106.

See Also

speciesTree

Examples

## example in Liu et al. (2010)
tr1 <- read.tree(text = "(((B:0.05,C:0.05):0.01,D:0.06):0.04,A:0.1);")
tr2 <- read.tree(text = "(((A:0.07,C:0.07):0.02,D:0.09):0.03,B:0.12);")
TR <- c(tr1, tr2)
sp_tree <- coalSpeciesTree(TR)

codonTest

Description

Models for detecting positive selection

Usage

codonTest(tree, object, model = c("M0", "M1a", "M2a"),
  frequencies = "F3x4", opt_freq = FALSE, codonstart = 1,
  control = pml.control(maxit = 20), ...)

Arguments

Details

codonTest allows to test for positive selection similar to programs like PAML (Yang ) or HyPhy (Kosakovsky Pond et al. 2005).

There are several options for deriving the codon frequencies. Frequencies can be "equal" (1/61), derived from nucleotide frequencies "F1x4" and "F3x4" or "empirical" codon frequencies. The frequencies taken using the empirical frequencies or estimated via maximum likelihood.

So far the M0 model (Goldman and Yang 2002), M1a and M2a are implemented. The M0 model is always computed the other are optional. The convergence may be very slow and sometimes fails.

Value

A list with an element called summary containing a data.frame with the log-likelihood, number of estimated parameters, etc. of all tested models. An object called posterior which contains the posterior probability for the rate class for each sites and the estimates of the defined models.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Ziheng Yang (2014). Molecular Evolution: A Statistical Approach. Oxford University Press, Oxford

Sergei L. Kosakovsky Pond, Simon D. W. Frost, Spencer V. Muse (2005) HyPhy: hypothesis testing using phylogenies, Bioinformatics, 21(5): 676–679, doi:10.1093/bioinformatics/bti079

Nielsen, R., and Z. Yang. (1998) Likelihood models for detecting positively selected amino acid sites and applications to the HIV-1 envelope gene. Genetics, 148: 929–936

See Also

pml, pmlMix, modelTest, AIC

Examples

## Not run: 
# load woodmouse data from ape
data(woodmouse)
dat_codon <- dna2codon(as.phyDat(woodmouse))
tree <- NJ(dist.ml(dat_codon))
# optimize the model the old way
fit <- pml(tree, dat_codon, bf="F3x4")
M0 <- optim.pml(fit, model="codon1")
# Now using the codonTest function
fit_codon <- codonTest(tree, dat_codon)
fit_codon
plot(fit_codon, "M1a")

## End(Not run)

Computes a consensusNetwork from a list of trees Computes a networx object from a collection of splits.

Description

Computes a consensusNetwork, i.e. an object of class networx from a list of trees, i.e. an class of class multiPhylo. Computes a networx object from a collection of splits.

Usage

consensusNet(obj, prob = 0.3, ...)

Arguments

Value

consensusNet returns an object of class networx. This is just an intermediate to plot phylogenetic networks with igraph.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Holland B.R., Huber K.T., Moulton V., Lockhart P.J. (2004) Using consensus networks to visualize contradictory evidence for species phylogeny. Molecular Biology and Evolution, 21, 1459–61

See Also

splitsNetwork, neighborNet, lento, distanceHadamard, plot.networx, maxCladeCred

Examples

data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)nj(dist.hamming(x)),
    bs=50)
cnet <- consensusNet(bs, .3)
plot(cnet, angle=-60, direction="axial")
## Not run: 
library(rgl)
open3d()
plot(cnet, type = "3D", show.tip.label=FALSE, show.nodes=TRUE)
plot(cnet, type = "equal angle", show.edge.label=TRUE)

tmpfile <- normalizePath(system.file(
              "extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn"))
trees <- read.tree(tmpfile)
cnet_woodmouse <- consensusNet(trees, .3)
plot(cnet_woodmouse, type = "equal angle", show.edge.label=TRUE)

## End(Not run)

Pairwise Distances from a Phylogenetic Network

Description

cophenetic.networx computes the pairwise distances between the pairs of tips from a phylogenetic network using its branch lengths.

Usage

## S3 method for class 'networx'
cophenetic(x)

Arguments

Value

an object of class dist, names are set according to the tip labels (as given by the element tip.label of the argument x).

Author(s)

Klaus Schliep

See Also

cophenetic for the generic function, neighborNet to construct a network from a distance matrix

Examples

example(neighborNet)
cophenetic(nnet)

Computes the \delta score

Description

Computes the treelikeness

Usage

delta.score(x, arg = "mean", ...)

Arguments

Value

A vector containing the \delta scores.

Author(s)

Alastair Potts and Klaus Schliep

References

BR Holland, KT Huber, A Dress, V Moulton (2002) \delta Plots: a tool for analyzing phylogenetic distance data Russell D. Gray, David Bryant, Simon J. Greenhill (2010) On the shape and fabric of human history Molecular Biology and Evolution, 19(12) 2051–2059

Russell D. Gray, David Bryant, Simon J. Greenhill (2010) On the shape and fabric of human history Phil. Trans. R. Soc. B, 365 3923–3933; DOI: 10.1098/rstb.2010.0162

See Also

dist.hamming

Examples

data(yeast)
hist(delta.score(yeast, "all"))

Plots a densiTree.

Description

An R function to plot trees similar to those produced by DensiTree.

Usage

densiTree(x, type = "phylogram", ..., alpha = 1/length(x),
  consensus = NULL, direction = "rightwards", optim = FALSE,
  scaleX = FALSE, col = 1, width = 1, lty = 1, cex = 0.8, font = 3,
  tip.color = 1, adj = 0, srt = 0, underscore = FALSE,
  label.offset = 0, scale.bar = TRUE, jitter = list(amount = 0, random =
  TRUE), tip.dates = NULL, xlim = NULL, ylim = NULL)

Arguments

Details

If no consensus tree is provided densiTree computes a consensus tree, and if the input trees have different labels a mrp.supertree as a backbone. This should avoid too many unnecessary crossings of edges. Trees should be rooted, other wise the output may not be visually pleasing. jitter shifts trees a bit so that they are not exactly on top of each other. If amount == 0, it is ignored. If random=TRUE the result of the permutation is runif(n, -amount, amount), otherwise seq(-amount, amount, length=n), where n <- length(x).

Value

densiTree returns silently x.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

densiTree is inspired from the great DensiTree program of Remco Bouckaert.

Remco R. Bouckaert (2010) DensiTree: making sense of sets of phylogenetic trees Bioinformatics, 26 (10), 1372-1373.

See Also

plot.phylo, plot.networx, jitter, rtt

Examples

data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian, FUN =
   function(x) upgma(dist.hamming(x)), bs=25)
# cladogram nice to show topological differences
densiTree(bs, type="cladogram", col="blue")
densiTree(bs, type="phylogram", col="green", direction="downwards", width=2)
# plot five trees slightly shifted, no transparent color
densiTree(bs[1:5], type="phylogram", col=1:5, width=2, jitter=
    list(amount=.3, random=FALSE), alpha=1)
## Not run: 
# phylograms are nice to show different age estimates
require(PhyloOrchard)
data(BinindaEmondsEtAl2007)
BinindaEmondsEtAl2007 <- .compressTipLabel(BinindaEmondsEtAl2007)
densiTree(BinindaEmondsEtAl2007, type="phylogram", col="red")

## End(Not run)

Compute a design matrix or non-negative LS

Description

nnls.tree estimates the branch length using non-negative least squares given a tree and a distance matrix. designTree and designSplits compute design matrices for the estimation of edge length of (phylogenetic) trees using linear models. For larger trees a sparse design matrix can save a lot of memory. computes a contrast matrix if the method is "rooted".

Usage

designTree(tree, method = "unrooted", sparse = FALSE, tip.dates = NULL,
  calibration = NULL, ...)

nnls.tree(dm, tree, method = c("unrooted", "ultrametric", "tipdated"),
  rooted = NULL, trace = 1, weight = NULL, balanced = FALSE,
  tip.dates = NULL)

nnls.phylo(x, dm, method = "unrooted", trace = 0, ...)

nnls.splits(x, dm, trace = 0, eps = 1e-08)

nnls.networx(x, dm, eps = 1e-08)

designSplits(x, splits = "all", ...)

Arguments

Value

nnls.tree return a tree, i.e. an object of class phylo. designTree and designSplits a matrix, possibly sparse.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

fastme, rtt, distanceHadamard, splitsNetwork, upgma

Examples

example(NJ)
dm <-  as.matrix(dm)
y <- dm[lower.tri(dm)]
X <- designTree(tree)
lm(y~X-1)
# avoids negative edge weights
tree2 <- nnls.tree(dm, tree)

Discrete Gamma and Beta distribution

Description

discrete.gamma internally used for the likelihood computations in pml or optim.pml. It is useful to understand how it works for simulation studies or in cases where .

Usage

discrete.gamma(alpha, k)

discrete.beta(shape1, shape2, k)

plot_gamma_plus_inv(shape = 1, inv = 0, k = 4, discrete = TRUE,
  cdf = TRUE, append = FALSE, xlab = "x", ylab = ifelse(cdf, "F(x)",
  "f(x)"), xlim = NULL, verticals = FALSE, edge.length = NULL,
  site.rate = "gamma", ...)

plotRates(obj, cdf.color = "blue", main = "cdf", ...)

Arguments

Details

These functions are exported to be used in different packages so far only in the package coalescentMCMC, but are not intended for end user. Most of the functions call C code and are far less forgiving if the import is not what they expect than pml.

Value

discrete.gamma returns a matrix.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

pml.fit, stepfun, pgamma, pbeta,

Examples

discrete.gamma(1, 4)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(2,1))
plot_gamma_plus_inv(shape=2, discrete = FALSE, cdf=FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE, cdf=FALSE)

plot_gamma_plus_inv(shape=2, discrete = FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE)
par(old.par)

Pairwise Distances from Sequences

Description

dist.hamming, dist.ml and dist.logDet compute pairwise distances for an object of class phyDat. dist.ml uses DNA / AA sequences to compute distances under different substitution models.

Usage

dist.hamming(x, ratio = TRUE, exclude = "none")

dist.ml(x, model = "JC69", exclude = "none", bf = NULL, Q = NULL,
  k = 1L, shape = 1, ...)

dist.logDet(x)

Arguments

Details

So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff", "cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb", "FLU", "Blosum62", "Dayhoff_DCMut" and "JTT_DCMut") and additional rate matrices and frequencies can be supplied.

The "F81" model uses empirical base frequencies, the "JC69" equal base frequencies. This is even the case if the data are not nucleotides.

Value

an object of class dist

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994) Recovering evolutionary trees under a more realistic model of sequence evolution. Molecular Biology and Evolution, 11, 605–602.

Jukes TH and Cantor CR (1969). Evolution of Protein Molecules. New York: Academic Press. 21–132.

McGuire, G., Prentice, M. J. and Wright, F. (1999). Improved error bounds for genetic distances from DNA sequences. Biometrics, 55, 1064–1070.

See Also

For more distance methods for nucleotide data see dist.dna and dist.p for pairwise polymorphism p-distances. writeDist for export and import distances.

Examples

data(Laurasiatherian)
dm1 <- dist.hamming(Laurasiatherian)
tree1 <- NJ(dm1)
dm2 <- dist.logDet(Laurasiatherian)
tree2 <- NJ(dm2)
treedist(tree1,tree2)
# JC model
dm3 <- dist.ml(Laurasiatherian)
tree3 <- NJ(dm3)
treedist(tree1,tree3)
# F81 + Gamma
dm4 <- dist.ml(Laurasiatherian, model="F81", k=4, shape=.4)
tree4 <- NJ(dm4)
treedist(tree1,tree4)
treedist(tree3,tree4)

Pairwise Polymorphism P-Distances from DNA Sequences

Description

This function computes a matrix of pairwise uncorrected polymorphism p-distances. Polymorphism p-distances include intra-individual site polymorphisms (2ISPs; e.g. "R") when calculating genetic distances.

Usage

dist.p(x, cost = "polymorphism", ignore.indels = TRUE)

Arguments

Details

The polymorphism p-distances (Potts et al. 2014) have been developed to analyse intra-individual variant polymorphism. For example, the widely used ribosomal internal transcribed spacer (ITS) region (e.g. Alvarez and Wendel, 2003) consists of 100's to 1000's of units within array across potentially multiple nucleolus organizing regions (Bailey et al., 2003; Goeker and Grimm, 2008). This can give rise to intra-individual site polymorphisms (2ISPs) that can be detected from direct-PCR sequencing or cloning . Clone consensus sequences (see Goeker and Grimm, 2008) can be analysed with this function.

Value

an object of class dist.

Author(s)

Klaus Schliep and Alastair Potts

References

Alvarez, I., and J. F. Wendel. (2003) Ribosomal ITS sequences and plant phylogenetic inference. Molecular Phylogenetics and Evolution, 29, 417–434.

Bailey, C. D., T. G. Carr, S. A. Harris, and C. E. Hughes. (2003) Characterization of angiosperm nrDNA polymorphism, paralogy, and pseudogenes. Molecular Phylogenetics and Evolution 29, 435–455.

Goeker, M., and G. Grimm. (2008) General functions to transform associate data to host data, and their use in phylogenetic inference from sequences with intra-individual variability. BMC Evolutionary Biology, 8:86.

Potts, A.J., T.A. Hedderson, and G.W. Grimm. (2014) Constructing phylogenies in the presence of intra-individual site polymorphisms (2ISPs) with a focus on the nuclear ribosomal cistron. Systematic Biology, 63, 1–16

See Also

dist.dna, dist.hamming

Examples

data(Laurasiatherian)
laura <- as.DNAbin(Laurasiatherian)

dm <- dist.p(Laurasiatherian, "polymorphism")

########################################################
# Dealing with indel 2ISPs
# These can be coded using an "x" in the alignment. Note
# that as.character usage in the read.dna() function.
#########################################################
cat("3 5",
    "No305     ATRA-",
    "No304     ATAYX",
    "No306     ATAGA",
    file = "exdna.txt", sep = "\n")
(ex.dna <- read.dna("exdna.txt", format = "sequential", as.character=TRUE))
dat <- phyDat(ex.dna, "USER", levels=unique(as.vector(ex.dna)))
dist.p(dat)

unlink("exdna.txt")

Distance Hadamard

Description

Distance Hadamard produces spectra of splits from a distance matrix.

Usage

distanceHadamard(dm, eps = 0.001)

Arguments

Value

distanceHadamard returns a matrix. The first column contains the distance spectra, the second one the edge-spectra. If eps is positive an object of with all splits greater eps is returned.

Author(s)

Klaus Schliep klaus.schliep@gmail.com, Tim White

References

Hendy, M. D. and Penny, D. (1993). Spectral Analysis of Phylogenetic Data. Journal of Classification, 10, 5-24.

See Also

hadamard, lento, plot.networx, neighborNet

Examples

data(yeast)
dm <- dist.hamming(yeast)
dm <- as.matrix(dm)
fit <- distanceHadamard(dm)
lento(fit)
plot(as.networx(fit))

Translate nucleic acid sequences into codons

Description

The function transforms dna2codon DNA sequences to codon sequences, codon2dna transform the other way. dna2codon translates nucleotide to amino acids using the function trans.

Usage

dna2codon(x, codonstart = 1, code = 1, ambiguity = "---", ...)

codon2dna(x)

dna2aa(x, codonstart = 1, code = 1)

Arguments

Details

The following genetic codes are described here. The number preceding each corresponds to the code argument.

Alignment gaps and ambiguities are currently ignored and sites containing these are deleted.

Value

The functions return an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=cgencodes

See Also

trans, phyDat and the chapter 4 in the vignette("phangorn-specials", package="phangorn")

Examples

data(Laurasiatherian)
class(Laurasiatherian)
Laurasiatherian
dna2codon(Laurasiatherian)

Treat gaps as a state

Description

The function gap_as_state changes the contrast of an phyDat object to treat as its own state. Internally phyDat are stored similar to a factor objects and only the contrast matrix and some attributes change.

Usage

gap_as_state(obj, gap = "-", ambiguous = "?")

gap_as_ambiguous(obj, gap = "-")

has_gap_state(obj)

Arguments

Value

The functions return an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

phyDat, latag2n.phyDat, latag2n, ancestral.pml, gap_as_state

Examples

data(Laurasiatherian)
tmp <- gap_as_state(Laurasiatherian)
contr <- attr(tmp, "contrast")
rownames(contr) <- attr(tmp, "allLevels")
contr

Clans, slices and clips

Description

Functions for clanistics to compute clans, slices, clips for unrooted trees and functions to quantify the fragmentation of trees.

Usage

getClans(tree)

getSlices(tree)

getClips(tree, all = TRUE)

getDiversity(tree, x, norm = TRUE, var.names = NULL, labels = "new")

## S3 method for class 'clanistics'
summary(object, ...)

diversity(tree, X)

Arguments

Details

Every split in an unrooted tree defines two complementary clans. Thus for an unrooted binary tree with n leaves there are 2n - 3 edges, and therefore 4n - 6 clans (including n trivial clans containing only one leave).

Slices are defined by a pair of splits or tripartitions, which are not clans. The number of distinguishable slices for a binary tree with n tips is 2n^2 - 10n + 12.

cophenetic distance and not by the topology. Namely clips are groups of leaves for which the maximum pairwise distance is smaller than threshold. distance within a clip is lower than the distance between any member of the clip and any other tip.

A clip is a different type of partition, defining groups of leaves that are related in terms of evolutionary distances and not only topology. Namely, clips are groups of leaves for which all pairwise path-length distances are smaller than a given threshold value (Lapointe et al. 2010). There exists different numbers of clips for different thresholds, the largest (and trivial) one being the whole tree. There is always a clip containing only the two leaves with the smallest pairwise distance.

Clans, slices and clips can be used to characterize how well a vector of categorial characters (natives/intruders) fit on a tree. We will follow the definitions of Lapointe et al.(2010). A complete clan is a clan that contains all leaves of a given state/color, but can also contain leaves of another state/color. A clan is homogeneous if it only contains leaves of one state/color.

getDiversity computes either the
Shannon Diversity: H = -\sum_{i=1}^{k}(N_i/N) log(N_i/N), N=\sum_{i=1}^{k} N_i
or the
Equitability Index: E = H / log(N)
where N_i are the sizes of the k largest homogeneous clans of intruders. If the categories of the data can be separated by an edge of the tree then the E-value will be zero, and maximum equitability (E=1) is reached if all intruders are in separate clans. getDiversity computes these Intruder indices for the whole tree, complete clans and complete slices. Additionally the parsimony scores (p-scores) are reported. The p-score indicates if the leaves contain only one color (p-score=0), if the the leaves can be separated by a single split (perfect clan, p-score=1) or by a pair of splits (perfect slice, p-score=2).

So far only 2 states are supported (native, intruder), however it is also possible to recode several states into the native or intruder state using contrasts, for details see section 2 in vignette("phangorn-specials"). Furthermore unknown character states are coded as ambiguous character, which can act either as native or intruder minimizing the number of clans or changes (in parsimony analysis) needed to describe a tree for given data.

Set attribute labels to "old" for analysis as in Schliep et al. (2010) or to "new" for names which are more intuitive.

diversity returns a data.frame with the parsimony score for each tree and each levels of the variables in X. X has to be a data.frame where each column is a factor and the rownames of X correspond to the tips of the trees.

Value

getClans, getSlices and getClips return a matrix of partitions, a matrix of ones and zeros where rows correspond to a clan, slice or clip and columns to tips. A one indicates that a tip belongs to a certain partition.
getDiversity returns a list with tree object, the first is a data.frame of the equitability index or Shannon divergence and parsimony scores (p-score) for all trees and variables. The data.frame has two attributes, the first is a splits object to identify the taxa of each tree and the second is a splits object containing all partitions that perfectly fit.

Author(s)

Klaus Schliep klaus.schliep@snv.jussieu.fr

Francois-Joseph Lapointe francois-joseph.lapointe@umontreal.ca

References

Lapointe, F.-J., Lopez, P., Boucher, Y., Koenig, J., Bapteste, E. (2010) Clanistics: a multi-level perspective for harvesting unrooted gene trees. Trends in Microbiology 18: 341-347

Wilkinson, M., McInerney, J.O., Hirt, R.P., Foster, P.G., Embley, T.M. (2007) Of clades and clans: terms for phylogenetic relationships in unrooted trees. Trends in Ecology and Evolution 22: 114-115

Schliep, K., Lopez, P., Lapointe F.-J., Bapteste E. (2011) Harvesting Evolutionary Signals in a Forest of Prokaryotic Gene Trees, Molecular Biology and Evolution 28(4): 1393-1405

See Also

parsimony, Consistency index CI, Retention index RI, phyDat

Examples

set.seed(111)
tree <- rtree(10)
getClans(tree)
getClips(tree, all=TRUE)
getSlices(tree)

set.seed(123)
trees <- rmtree(10, 20)
X <- matrix(sample(c("red", "blue", "violet"), 100, TRUE, c(.5,.4, .1)),
   ncol=5, dimnames=list(paste('t',1:20, sep=""), paste('Var',1:5, sep="_")))
x <- phyDat(X, type = "USER", levels = c("red", "blue"), ambiguity="violet")
plot(trees[[1]], "u", tip.color = X[trees[[1]]$tip,1])  # intruders are blue

(divTab <- getDiversity(trees, x, var.names=colnames(X)))
summary(divTab)

Tree manipulation

Description

midpoint performs midpoint rooting of a tree. pruneTree produces a consensus tree. pruneTree prunes back a tree and produces a consensus tree, for trees already containing nodelabels. It assumes that nodelabels are numerical or character that allows conversion to numerical, it uses as.numeric(as.character(tree$node.labels)) to convert them. midpoint by default assumes that node labels contain support values. This works if support values are computed from splits, but should be recomputed for clades. keep_as_tip takes a list of tips and/or node labels and returns a tree pruned to those. If node label, then it prunes all descendants of that node until that internal node becomes a tip.

Usage

getRoot(tree)

midpoint(tree, node.labels = "support", ...)

## S3 method for class 'phylo'
midpoint(tree, node.labels = "support", ...)

## S3 method for class 'multiPhylo'
midpoint(tree, node.labels = "support", ...)

pruneTree(tree, ..., FUN = ">=")

keep_as_tip(tree, labels)

Arguments

Value

pruneTree and midpoint a tree. getRoot returns the root node.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

consensus, root, multi2di

Examples

tree <- rtree(10, rooted = FALSE)
tree$node.label <- c("", round(runif(tree$Nnode-1), digits=3))

tree2 <- midpoint(tree)
tree3 <- pruneTree(tree, .5)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(3,1))
plot(tree, show.node.label=TRUE)
plot(tree2, show.node.label=TRUE)
plot(tree3, show.node.label=TRUE)
par(old.par)

Hadamard Matrices and Fast Hadamard Multiplication

Description

A collection of functions to perform Hadamard conjugation. Hadamard matrix H with a vector v using fast Hadamard multiplication.

Usage

hadamard(x)

fhm(v)

h4st(obj, levels = c("a", "c", "g", "t"))

h2st(obj, eps = 0.001)

Arguments

Details

h2st and h4st perform Hadamard conjugation for 2-state (binary, RY-coded) or 4-state (DNA/RNA) data. write.nexus.splits writes splits returned from h2st or distanceHadamard to a nexus file, which can be processed by Spectronet or SplitsTree.

Value

hadamard returns a Hadamard matrix. fhm returns the fast Hadamard multiplication.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Hendy, M.D. (1989). The relationship between simple evolutionary tree models and observable sequence data. Systematic Zoology, 38 310–321.

Hendy, M. D. and Penny, D. (1993). Spectral Analysis of Phylogenetic Data. Journal of Classification, 10, 5–24.

Hendy, M. D. (2005). Hadamard conjugation: an analytical tool for phylogenetics. In O. Gascuel, editor, Mathematics of evolution and phylogeny, Oxford University Press, Oxford

Waddell P. J. (1995). Statistical methods of phylogenetic analysis: Including hadamard conjugation, LogDet transforms, and maximum likelihood. PhD thesis.

See Also

distanceHadamard, lento, plot.networx

Examples

H <- hadamard(3)
v <- 1:8
H %*% v
fhm(v)

data(yeast)

# RY-coding
dat_ry <- acgt2ry(yeast)
fit2 <- h2st(dat_ry)
lento(fit2)

# write.nexus.splits(fit2, file = "test.nxs")
# read this file into Spectronet or SplitsTree to show the network

fit4 <- h4st(yeast)
old.par <- par(no.readonly = TRUE)
par(mfrow=c(3,1))
lento(fit4[[1]], main="Transversion")
lento(fit4[[2]], main="Transition 1")
lento(fit4[[3]], main="Transition 2")
par(old.par)

Identify splits in a network

Description

identify.networx reads the position of the graphics pointer when the mouse button is pressed. It then returns the split belonging to the edge closest to the pointer. The network must be plotted beforehand.

Usage

## S3 method for class 'networx'
identify(x, quiet = FALSE, ...)

Arguments

Value

identify.networx returns a splits object.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

plot.networx, identify

Examples

## Not run: 
data(yeast)
dm <- dist.ml(yeast)
nnet <- neighborNet(dm)
plot(nnet)
identify(nnet) # click close to an edge

## End(Not run)

Plot of a Sequence Alignment

Description

This function plots an image of an alignment of sequences.

Usage

## S3 method for class 'phyDat'
image(x, ...)

Arguments

Details

A wrapper for using image.DNAbin and image.AAbin. Codons triplets are handled as nucleotide sequences.

Value

Nothing. The function is called for plotting.

See Also

image.DNAbin, image.AAbin

Examples

data("chloroplast")
image(chloroplast[, 1:50], scheme="Clustal", show.aa = TRUE)

Replace leading and trailing alignment gaps with an ambiguous state

Description

Substitutes leading and trailing alignment gaps in aligned sequences into N (i.e., A, C, G, or T) or ?. The gaps in the middle of the sequences are left unchanged.

Usage

latag2n.phyDat(x, amb = ifelse(attr(x, "type") == "DNA", "N", "?"),
  gap = "-", ...)

Arguments

Value

returns an object of class phyDat.

See Also

latag2n, ancestral.pml, gap_as_state

Examples

x <- phyDat(matrix(c("-", "A", "G", "-", "T", "C"), 2, 3))
y <- latag2n.phyDat(x)
image(x)
image(y)

Laurasiatherian data (AWCMEE)

Description

Laurasiatherian RNA sequence data

Source

Data have been taken from the former repository of the Allan Wilson Centre and were converted to R format by klaus.schliep@gmail.com.

Examples

data(Laurasiatherian)
str(Laurasiatherian)

Arithmetic Operators

Description

double factorial function

Usage

ldfactorial(x)

dfactorial(x)

Arguments

Value

dfactorial(x) returns the double factorial, that is x\!\! = 1 * 3 * 5 * \ldots * x and ldfactorial(x) is the natural logarithm of it.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

factorial, howmanytrees

Examples

dfactorial(1:10)

Lento plot

Description

The lento plot represents support and conflict of splits/bipartitions.

Usage

lento(obj, xlim = NULL, ylim = NULL, main = "Lento plot", sub = NULL,
  xlab = NULL, ylab = NULL, bipart = TRUE, trivial = FALSE,
  col = rgb(0, 0, 0, 0.5), ...)

Arguments

Value

lento returns a plot.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Lento, G.M., Hickson, R.E., Chambers G.K., and Penny, D. (1995) Use of spectral analysis to test hypotheses on the origin of pinninpeds. Molecular Biology and Evolution, 12, 28-52.

See Also

as.splits, hadamard

Examples

data(yeast)
yeast.ry <- acgt2ry(yeast)
splits.h <- h2st(yeast.ry)
lento(splits.h, trivial=TRUE)

Internal maximum likelihood functions.

Description

These functions are internally used for the likelihood computations in pml or optim.pml.

Usage

lli(data, tree = NULL, ...)

edQt(Q = c(1, 1, 1, 1, 1, 1), bf = c(0.25, 0.25, 0.25, 0.25))

pml.free()

pml.init(data, k = 1L)

pml.fit(tree, data, bf = rep(1/length(levels), length(levels)), shape = 1,
  k = 1, Q = rep(1, length(levels) * (length(levels) - 1)/2),
  levels = attr(data, "levels"), inv = 0, rate = 1, g = NULL,
  w = NULL, eig = NULL, INV = NULL, ll.0 = NULL, llMix = NULL,
  wMix = 0, ..., site = FALSE, ASC = FALSE, site.rate = "gamma")

Arguments

Details

These functions are exported to be used in different packages so far only in the package coalescentMCMC, but are not intended for end user. Most of the functions call C code and are far less forgiving if the import is not what they expect than pml.

Value

pml.fit returns the log-likelihood.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evolution, 17, 368–376.

See Also

pml, pml_bb, pmlPart, pmlMix

Examples

data(Laurasiatherian)
tree <- NJ(dist.ml(Laurasiatherian))
bf <- rep(0.25, 4)
eig <- edQt()
pml.init(Laurasiatherian)
pml.fit(tree, Laurasiatherian, bf=bf, eig=eig)
pml.free()
pml(tree, Laurasiatherian) |> logLik()

Maximum agreement subtree

Description

mast computes the maximum agreement subtree (MAST).

Usage

mast(x, y, tree = TRUE, rooted = TRUE)

Arguments

Details

The code is derived from the code example in Valiente (2009). The version for the unrooted trees is much slower.

Value

mast returns a vector of the tip labels in the MAST.

Author(s)

Klaus Schliep klaus.schliep@gmail.com based on code of Gabriel Valiente

References

G. Valiente (2009). Combinatorial Pattern Matching Algorithms in Computational Biology using Perl and R. Taylor & Francis/CRC Press

See Also

SPR.dist

Examples

tree1 <- rtree(100)
tree2 <- rSPR(tree1, 5)
tips <- mast(tree1, tree2)

Maximum clade credibility tree

Description

maxCladeCred computes the maximum clade credibility tree from a sample of trees. So far just the best tree is returned. No annotations or transformations of edge length are performed and the edge length are taken from the tree.

Usage

maxCladeCred(x, tree = TRUE, part = NULL, rooted = TRUE)

mcc(x, tree = TRUE, part = NULL, rooted = TRUE)

allCompat(x, rooted = FALSE)

Arguments

Details

If a list of partition is provided then the clade credibility is computed for the trees in x.

allCompat returns a 50% majority rule consensus tree with added compatible splits similar to the option allcompat in MrBayes. This tree has no edge length.

add_edge_length can be used to add edge lengths computed from the sample of trees.

Value

a tree (an object of class phylo) with the highest clade credibility or a numeric vector of clade credibilities for each tree.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

consensus, consensusNet, prop.part, bootstrap.pml, plotBS, transferBootstrap, add_edge_length, add_boxplot

Examples

data(Laurasiatherian)
set.seed(42)
bs <- bootstrap.phyDat(Laurasiatherian,
  FUN = function(x)upgma(dist.hamming(x)), bs=100)

strict_consensus <- consensus(bs)
majority_consensus <- consensus(bs, p=.5)
all_compat <- allCompat(bs)
max_clade_cred <- maxCladeCred(bs)

old.par <- par(no.readonly = TRUE)
par(mfrow = c(2,2), mar = c(1,4,1,1))
plot(strict_consensus, main="Strict consensus tree")
plot(majority_consensus, main="Majority consensus tree")
plot(all_compat, main="Majority consensus tree with compatible splits")
plot(max_clade_cred, main="Maximum clade credibility tree")

par(mfrow = c(2,1))
plot(max_clade_cred, main="Edge length from tree")
add_boxplot(max_clade_cred, bs)
max_clade_cred_2 <- add_edge_length(max_clade_cred, bs)
plot(max_clade_cred_2, main="Edge length computed from sample")
add_boxplot(max_clade_cred_2, bs)

par(old.par)

# compute clade credibility for trees given a prop.part object
pp <- prop.part(bs)
tree <- rNNI(bs[[1]], 20)
maxCladeCred(c(tree, bs[[1]]), tree=FALSE, part = pp)
# first value likely be -Inf

Morphological characters of Mites (Schäffer et al. 2010)

Description

Matrix for morphological characters and character states for 12 species of mites. See vignette '02_Phylogenetic trees from morphological data' for examples to import morphological data.

References

Schäffer, S., Pfingstl, T., Koblmüller, S., Winkler, K. A., Sturmbauer, C., & Krisper, G. (2010). Phylogenetic analysis of European Scutovertex mites (Acari, Oribatida, Scutoverticidae) reveals paraphyly and cryptic diversity: a molecular genetic and morphological approach. Molecular Phylogenetics and Evolution, 55(2), 677–688.

Examples

data(mites)
mites
# infer all maximum parsimony trees
trees <- bab(mites)
# For larger data sets you might use pratchet instead bab
# trees <- pratchet(mites, minit=200, trace=0, all=TRUE)
# build consensus tree
ctree <- root(consensus(trees, p=.5), outgroup = "C._cymba",
              resolve.root=TRUE, edgelabel=TRUE)
plotBS(ctree, trees)
cnet <- consensusNet(trees)
plot(cnet)

ModelTest

Description

Comparison of different nucleotide or amino acid substitution models

Usage

modelTest(object, tree = NULL, model = NULL, G = TRUE, I = TRUE,
  FREQ = FALSE, k = 4, control = pml.control(epsilon = 1e-08, maxit = 10,
  trace = 1), multicore = FALSE, mc.cores = NULL)

Arguments

Details

modelTest estimates all the specified models for a given tree and data. When the mclapply is available, the computations are done in parallel. modelTest runs each model in one thread. This is may not work within a GUI interface and will not work under Windows.

Value

A data.frame containing the log-likelihood, number of estimated parameters, AIC, AICc and BIC all tested models. The data.frame has an attributes "env" which is an environment which contains all the trees, the data and the calls to allow get the estimated models, e.g. as a starting point for further analysis (see example).

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Burnham, K. P. and Anderson, D. R (2002) Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. Springer, New York

Posada, D. and Crandall, K.A. (1998) MODELTEST: testing the model of DNA substitution. Bioinformatics 14(9): 817-818

Posada, D. (2008) jModelTest: Phylogenetic Model Averaging. Molecular Biology and Evolution 25: 1253-1256

Darriba D., Taboada G.L., Doallo R and Posada D. (2011) ProtTest 3: fast selection of best-fit models of protein evolution. . Bioinformatics 27: 1164-1165

See Also

pml, anova, AIC, codonTest

Examples

## Not run: 
example(NJ)
(mT <- modelTest(Laurasiatherian, tree, model = c("JC", "F81", "K80", "HKY",
                 "SYM", "GTR")))

# extract best model
(best_model <- as.pml(mT))


data(chloroplast)
(mTAA <- modelTest(chloroplast, model=c("JTT", "WAG", "LG")))

# test all available amino acid models
(mTAA_all <- modelTest(chloroplast, model="all", multicore=TRUE, mc.cores=2))

## End(Not run)

Partition model.

Description

Model to estimate phylogenies for partitioned data.

Usage

multiphyDat2pmlPart(x, method = "unrooted", tip.dates = NULL, ...)

pmlPart2multiPhylo(x)

pmlPart(formula, object, control = pml.control(epsilon = 1e-08, maxit = 10,
  trace = 1), model = NULL, method = "unrooted", ...)

Arguments

Details

The formula object allows to specify which parameter get optimized. The formula is generally of the form edge + bf + Q ~ rate + shape + ...{}, on the left side are the parameters which get optimized over all partitions, on the right the parameter which are optimized specific to each partition. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameters can be used only once in the formula. "rate" is only available for the right side of the formula.

For partitions with different edge weights, but same topology, pmlPen can try to find more parsimonious models (see example).

pmlPart2multiPhylo is a convenience function to extract the trees out of a pmlPart object.

Value

kcluster returns a list with elements

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

pml,pmlCluster,pmlMix, SH.test

Examples

data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fits <- optim.pml(fit)

weight=xtabs(~ index+genes,attr(yeast, "index"))[,1:10]

sp <- pmlPart(edge ~ rate + inv, fits, weight=weight)
sp

## Not run: 
sp2 <- pmlPart(~ edge + inv, fits, weight=weight)
sp2
AIC(sp2)

sp3 <- pmlPen(sp2, lambda = 2)
AIC(sp3)

## End(Not run)

Computes a neighborNet from a distance matrix

Description

Computes a neighborNet, i.e. an object of class networx from a distance matrix.

Usage

neighborNet(x, ord = NULL)

Arguments

Details

neighborNet is still experimental. The cyclic ordering sometimes differ from the SplitsTree implementation, the ord argument can be used to enforce a certain circular ordering.

Value

neighborNet returns an object of class networx.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Bryant, D. & Moulton, V. (2004) Neighbor-Net: An Agglomerative Method for the Construction of Phylogenetic Networks. Molecular Biology and Evolution, 2004, 21, 255-265

See Also

splitsNetwork, consensusNet, plot.networx, lento, cophenetic.networx, distanceHadamard

Examples

data(yeast)
dm <- dist.ml(yeast)
nnet <- neighborNet(dm)
plot(nnet)

Neighbor-Joining

Description

This function performs the neighbor-joining tree estimation of Saitou and Nei (1987). UNJ is the unweighted version from Gascuel (1997).

Usage

NJ(x)

UNJ(x)

Arguments

Value

an object of class "phylo".

Author(s)

Klaus P. Schliep klaus.schliep@gmail.com

References

Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution, 4, 406–425.

Studier, J. A and Keppler, K. J. (1988) A Note on the Neighbor-Joining Algorithm of Saitou and Nei. Molecular Biology and Evolution, 6, 729–731.

Gascuel, O. (1997) Concerning the NJ algorithm and its unweighted version, UNJ. in Birkin et. al. Mathematical Hierarchies and Biology, 149–170.

See Also

nj, dist.dna, dist.hamming, upgma, fastme

Examples

data(Laurasiatherian)
dm <- dist.ml(Laurasiatherian)
tree <- NJ(dm)
plot(tree)

Tree rearrangements.

Description

nni returns a list of all trees which are one nearest neighbor interchange away. rNNI and rSPR are two methods which simulate random trees which are a specified number of rearrangement apart from the input tree. Both methods assume that the input tree is bifurcating. These methods may be useful in simulation studies.

Usage

nni(tree)

rNNI(tree, moves = 1, n = length(moves))

rSPR(tree, moves = 1, n = length(moves), k = NULL)

Arguments

Value

an object of class multiPhylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

allTrees, SPR.dist

Examples

tree <- rtree(20, rooted = FALSE)
trees1 <- nni(tree)
trees2 <- rSPR(tree, 2, 10)

Conversion among Sequence Formats

Description

These functions transform several DNA formats into the phyDat format. allSitePattern generates an alignment of all possible site patterns.

Usage

phyDat(data, type = "DNA", levels = NULL, return.index = TRUE, ...)

as.phyDat(x, ...)

## S3 method for class 'factor'
as.phyDat(x, ...)

## S3 method for class 'DNAbin'
as.phyDat(x, ...)

## S3 method for class 'AAbin'
as.phyDat(x, ...)

## S3 method for class 'alignment'
as.phyDat(x, type = "DNA", ...)

phyDat2alignment(x)

## S3 method for class 'MultipleAlignment'
as.phyDat(x, ...)

## S3 method for class 'AAStringSet'
as.phyDat(x, ...)

## S3 method for class 'DNAStringSet'
as.phyDat(x, ...)

as.StringSet(x, ...)

## S3 method for class 'phyDat'
as.StringSet(x, ...)

## S3 method for class 'phyDat'
as.MultipleAlignment(x, ...)

## S3 method for class 'phyDat'
as.character(x, allLevels = TRUE, ...)

## S3 method for class 'phyDat'
as.data.frame(x, ...)

## S3 method for class 'phyDat'
as.DNAbin(x, ...)

## S3 method for class 'phyDat'
as.AAbin(x, ...)

genlight2phyDat(x, ambiguity = NA)

acgt2ry(obj)

Arguments

Details

If type "USER" a vector has to be give to levels. For example c("a", "c", "g", "t", "-") would create a data object that can be used in phylogenetic analysis with gaps as fifth state. There is a more detailed example for specifying "USER" defined data formats in the vignette "phangorn-specials".

acgt2ry converts a phyDat object of nucleotides into an binary ry-coded dataset.

Value

The functions return an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

DNAbin, as.DNAbin, baseFreq, glance.phyDat, read.dna, read.nexus.data and the chapter 1 in the vignette("phangorn-specials", package="phangorn") and the example of pmlMix for the use of allSitePattern

Examples

data(Laurasiatherian)
class(Laurasiatherian)
Laurasiatherian
# transform as characters
LauraChar <- as.character(Laurasiatherian)
# and back
Laura <- phyDat(LauraChar)
all.equal(Laurasiatherian, Laura)
LauraDNAbin <- as.DNAbin(Laurasiatherian)
all.equal(Laurasiatherian, as.phyDat(LauraDNAbin))

plot phylogenetic networks

Description

So far not all parameters behave the same on the the rgl "3D" and basic graphic "2D" device.

Usage

## S3 method for class 'networx'
plot(x, type = "equal angle", use.edge.length = TRUE,
  show.tip.label = TRUE, show.edge.label = FALSE, edge.label = NULL,
  show.node.label = FALSE, node.label = NULL, show.nodes = FALSE,
  tip.color = "black", edge.color = "black", edge.width = 3,
  edge.lty = 1, split.color = NULL, split.width = NULL,
  split.lty = NULL, font = 3, cex = par("cex"), cex.node.label = cex,
  cex.edge.label = cex, col.node.label = tip.color,
  col.edge.label = tip.color, font.node.label = font,
  font.edge.label = font, underscore = FALSE, angle = 0, digits = 3,
  ...)

Arguments

Details

Often it is easier and safer to supply vectors of graphical parameters for splits (e.g. splits.color) than for edges. These overwrite values edge.color.

Value

plot.networx returns invisibly a list with paramters of the plot.

Note

The internal representation is likely to change.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Dress, A.W.M. and Huson, D.H. (2004) Constructing Splits Graphs IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 1(3), 109–115

Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017), Intertwining phylogenetic trees and networks. Methods Ecol Evol. 8, 1212–1220. doi:10.1111/2041-210X.12760

See Also

consensusNet, neighborNet, splitsNetwork, hadamard, distanceHadamard, as.networx, evonet, as.phylo, densiTree, nodelabels

Examples

set.seed(1)
tree1 <- rtree(20, rooted=FALSE)
sp <- as.splits(rNNI(tree1, n=10))
net <- as.networx(sp)
plot(net)
plot(net, direction="axial")
## Not run: 
# also see example in consensusNet
example(consensusNet)

## End(Not run)

Plot phylogeny of a pml object

Description

plot.pml is a wrapper around plot.phylo with different default values for unrooted, ultrametric and tip dated phylogenies.

Usage

## S3 method for class 'pml'
plot(x, type = "phylogram", direction = "rightwards", ...)

Arguments

Value

plot.pml returns invisibly a list with arguments dexcribing the plot. For further details see the plot.phylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

plot.phylo, axisPhylo, add.scale.bar

Examples

fdir <- system.file("extdata/trees", package = "phangorn")
tmp <- read.csv(file.path(fdir,"H3N2_NA_20.csv"))
H3N2 <- read.phyDat(file.path(fdir,"H3N2_NA_20.fasta"), format="fasta")
dates <- setNames(tmp$numdate_given, tmp$name)

fit_td <- pml_bb(H3N2, model="JC", method="tipdated", tip.dates=dates,
                 rearrangement="none", control = pml.control(trace = 0))
plot(fit_td, show.tip.label = FALSE)
# Same as:
# root_time <- max(dates) - max(node.depth.edgelength(fit_td$tree))
# plot(fit_td$tree, show.tip.label = FALSE)
# axisPhylo(root.time = root_time, backward = FALSE)
plot(fit_td, show.tip.label = FALSE, direction="up")

fit_unrooted <- pml_bb(H3N2, model="JC", rearrangement="none",
                       control = pml.control(trace = 0))
plot(fit_unrooted, cex=.5)

Plot ancestral character on a tree

Description

plotAnc plots a phylogeny and adds character to the nodes. Either takes output from ancestral.pars or ancestral.pml or from an alignment where there are node labels in the tree match the constructed sequences in the alignment.

Usage

plotAnc(x, i = 1, type = "phylogram", ..., col = NULL, cex.pie = 0.5,
  pos = "bottomright", scheme = NULL)

plotSeqLogo(x, node = getRoot(x$tree), start = 1, end = 10,
  scheme = "Ape_NT", ...)

add_mutations(x, frame = "none", ...)

Arguments

Details

For further details see vignette("Ancestral").

Value

plotAnc returns silently x.

plotSeqLogo returns a ggplot object.

add_mutations adds the position and and changes of possible mutations to a phylogeny.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

ancestral.pml, plot.phylo, image.DNAbin, image.AAbin ggseqlogo, edgelabels

Examples

example(NJ)
# generate node labels to ensure plotting will work
tree <- makeNodeLabel(tree)
anc.p <- anc_pars(tree, Laurasiatherian)
# plot the third character
plotAnc(anc.p, 3, pos="topright")
plotSeqLogo(anc.p, node="Node10", 1, 25)

data(chloroplast)
tree <- pratchet(chloroplast,  maxit=10, trace=0)
tree <- makeNodeLabel(tree)
anc.ch <- anc_pars(tree, chloroplast)
image(as.phyDat(anc.ch)[, 1:25])
plotAnc(anc.ch, 21, scheme="Ape_AA", pos="topleft")
plotAnc(anc.ch, 21, scheme="Clustal", pos="topleft")
plotSeqLogo(anc.ch, node="Node1", 1, 25, scheme="Clustal")


data(woodmouse)
tree <- pml_bb(woodmouse, "JC", rearrangement="NNI")$tree |> midpoint()
woodmouse_aa <- trans(woodmouse, 2) |> as.phyDat()
anc_aa <- anc_pars(tree, woodmouse_aa)
plot(tree, direction="downwards")
add_mutations(anc_aa)

Plotting trees with bootstrap values

Description

plotBS plots a phylogenetic tree with the bootstrap values assigned to the (internal) edges. It can also used to assign bootstrap values to a phylogenetic tree. add_support adds support values to a plot.

Usage

plotBS(tree, trees, type = "phylogram", method = "FBP", bs.col = "black",
  bs.adj = NULL, digits = 3, p = 0, frame = "none", tol = 1e-06,
  sep = "/", ...)

add_support(tree, trees, method = "FBP", tol = 1e-08, scale = TRUE,
  frame = "none", digits = 3, sep = "/", ...)

Arguments

Details

The functions can either assign the classical Felsenstein’s bootstrap proportions (FBP) (Felsenstein (1985), Hendy & Penny (1985)) or the transfer bootstrap expectation (TBE) of Lemoine et al. (2018). Using the option type=="n" just assigns the bootstrap values and return the tree without plotting it.

Value

plotBS returns silently a tree, i.e. an object of class phylo with the bootstrap values as node labels. The argument trees is optional and if not supplied the labels supplied in the node.label slot will be used.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Felsenstein J. (1985) Confidence limits on phylogenies. An approach using the bootstrap. Evolution 39, 783–791

Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D., De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature, 556(7702), 452–456.

Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree construction. Cladistics 1, 266–278

Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary trees. Molecular Biology and Evolution 3, 403–417

See Also

plot.phylo, add_ci, nodelabels, prop.clades, maxCladeCred, transferBootstrap, consensus, consensusNet

Examples

fdir <- system.file("extdata/trees", package = "phangorn")
# RAxML best-known tree with bipartition support (from previous analysis)
raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse"))
# RAxML bootstrap trees (from previous analysis)
raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse"))
par(mfrow=c(1,2))
plotBS(raxml.tree,  raxml.bootstrap, "p")
plotBS(raxml.tree,  raxml.bootstrap, "p", "TBE")

Likelihood of a tree.

Description

pml_bb for pml black box infers a phylogenetic tree infers a tree using maximum likelihood (ML).

Usage

pml_bb(x, model = NULL, rearrangement = "stochastic",
  method = "unrooted", start = NULL, tip.dates = NULL, ...)

Arguments

Details

pml_bb is a convenience function combining pml and optim.pml. If no tree is supplied, the function will generate a starting tree. If a modelTest object is supplied the model will be chosen according to BIC.

tip.dates should be a named vector of sampling times, in any time unit, with time increasing toward the present. For example, this may be in units of “days since study start” or “years since 10,000 BCE”, but not “millions of years ago”.

model takes a string and tries to extract the model. When an modelTest object the best BIC model is chosen by default. The string should contain a substitution model (e.g. JC, GTR, WAG) and can additional have a term "+I" for invariant sites, "+G(4)" for a discrete gamma model, "+R(4)" for a free rate model. In case of amino acid models a term "+F" for estimating the amino acid frequencies. Whether nucleotide frequencies are estimated is defined by pml.control.

Currently very experimental and likely to change.

Value

pml_bb returns an object of class pml.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

optim.pml, modelTest, rtt, pml.control

Examples

data(woodmouse)
tmp <- pml_bb(woodmouse, model="HKY+I", rearrangement="NNI")

## Not run: 
data(Laurasiatherian)
mt <- modelTest(Laurasiatherian)
fit <- pml_bb(mt)

# estimate free rate model with 2 rate categories
fit_HKY_R2 <- pml_bb(woodmouse, model="HKY+R(2)")

## End(Not run)

Auxiliary for Controlling Fitting

Description

Auxiliary functions for providing optim.pml, pml_bb fitting. Use it to construct a control or ratchet.par argument.

Usage

pml.control(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-08,
  statefreq = "empirical")

ratchet.control(iter = 20L, maxit = 200L, minit = 100L, prop = 1/2,
  rell = TRUE, bs = 1000L)

Arguments

Details

pml.control controls the fitting process. epsilon and maxit are only defined for the most outer loop, this affects pmlCluster, pmlPart and pmlMix.

epsilon is not an absolute difference between, but instead is defined as (logLik(k)-logLik(k+1))/logLik(k+1). This seems to be a good compromise and to work reasonably well for small and large trees or alignments.

If trace is set to zero than no out put is shown, if functions are called internally than the trace is decreased by one, so a higher of trace produces more feedback. It can be useful to figure out how long an run will take and for debugging.

statefreq controls if base/state frequencies are optimized or empirical estimates are taken, when this applies. For some nucleotide models (e.g. JC, SYM) equal base frequencies and for amino acid models precomputed state frequencies are used, if not '+F' is specified.

tau might be exactly zero if duplicated sequences in the alignment are observed. In this case the analysis is performed only on unique sequences and duplicated taxa are added to the tree with zero edge length. This may lead to multifurcations if there are three or more identical sequences. After optimization it is good practice to prune away edges of length tau using di2multi. See also Janzen et al. (2021).

Value

A list with components named as the arguments for controlling the fitting process.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Minh, B. Q., Nguyen, M. A. T., & von Haeseler, A. (2013). Ultrafast approximation for phylogenetic bootstrap. Molecular biology and evolution, 30(5), 1188-1195.

Janzen, T., Bokma, F.,Etienne, R. S. (2021) Nucleotide Substitutions during Speciation may Explain Substitution Rate Variation, Systematic Biology, 71(5), 1244–1254.

See Also

pml_bb, optim.pml

Examples

pml.control()
pml.control(maxit=25)

Stochastic Partitioning

Description

Stochastic Partitioning of genes into p cluster.

Usage

pmlCluster(formula, fit, weight, p = 1:5, part = NULL, nrep = 10,
  control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...)

Arguments

Details

The formula object allows to specify which parameter get optimized. The formula is generally of the form edge + bf + Q ~ rate + shape + ...{}, on the left side are the parameters which get optimized over all cluster, on the right the parameter which are optimized specific to each cluster. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameter can be used only once in the formula. There are also some restriction on the combinations how parameters can get used. "rate" is only available for the right side. When "rate" is specified on the left hand side "edge" has to be specified (on either side), if "rate" is specified on the right hand side it follows directly that edge is too.

Value

pmlCluster returns a list with elements

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis)

Lanfear, R., Calcott, B., Ho, S.Y.W. and Guindon, S. (2012) PartitionFinder: Combined Selection of Partitioning Schemes and Substitution Models for Phylogenetic Analyses. Molecular Biology and Evolution, 29(6), 1695-1701

See Also

pml,pmlPart,pmlMix, SH.test

Examples

## Not run: 
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fit <- optim.pml(fit)

weight <- xtabs(~ index+genes,attr(yeast, "index"))
set.seed(1)

sp <- pmlCluster(edge~rate, fit, weight, p=1:4)
sp
SH.test(sp)

## End(Not run)

Phylogenetic mixture model

Description

Phylogenetic mixture model.

Usage

pmlMix(formula, fit, m = 2, omega = rep(1/m, m),
  control = pml.control(epsilon = 1e-08, maxit = 20, trace = 1), ...)

Arguments

Details

The formula object allows to specify which parameter get optimized. The formula is generally of the form edge + bf + Q ~ rate + shape + ...{}, on the left side are the parameters which get optimized over all mixtures, on the right the parameter which are optimized specific to each mixture. The parameters available are "nni", "bf", "Q", "inv", "shape", "edge", "rate". Each parameters can be used only once in the formula. "rate" and "nni" are only available for the right side of the formula. On the other hand parameters for invariable sites are only allowed on the left-hand side. The convergence of the algorithm is very slow and is likely that the algorithm can get stuck in local optima.

Value

pmlMix returns a list with elements

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

pml,pmlPart,pmlCluster

Examples

## Not run: 
X <- allSitePattern(5)
tree <- read.tree(text = "((t1:0.3,t2:0.3):0.1,(t3:0.3,t4:0.3):0.1,t5:0.5);")
fit <- pml(tree,X, k=4)
weights <- 1000*exp(fit$siteLik)
attr(X, "weight") <- weights
fit1 <- update(fit, data=X, k=1)
fit2 <- update(fit, data=X)

(fitMixture <- pmlMix(edge~rate, fit1 , m=4))
(fit2 <- optim.pml(fit2, optGamma=TRUE))


data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree <- NJ(dm)
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit)

fit2 <- update(fit, k=4)
fit2 <- optim.pml(fit2, optGamma=TRUE)

fitMix <- pmlMix(edge ~ rate, fit, m=4)
fitMix


#
# simulation of mixture models
#
X <- allSitePattern(5)
tree1 <- read.tree(text = "((t1:0.1,t2:0.5):0.1,(t3:0.1,t4:0.5):0.1,t5:0.5);")
tree2 <- read.tree(text = "((t1:0.5,t2:0.1):0.1,(t3:0.5,t4:0.1):0.1,t5:0.5);")
tree1 <- unroot(tree1)
tree2 <- unroot(tree2)
fit1 <- pml(tree1,X)
fit2 <- pml(tree2,X)

weights <- 2000*exp(fit1$siteLik) + 1000*exp(fit2$siteLik)
attr(X, "weight") <- weights

fit1 <- pml(tree1, X)
fit2 <- optim.pml(fit1)
logLik(fit2)
AIC(fit2, k=log(3000))

fitMixEdge <- pmlMix( ~ edge, fit1, m=2)
logLik(fitMixEdge)
AIC(fitMixEdge, k=log(3000))

fit.p <- pmlPen(fitMixEdge, .25)
logLik(fit.p)
AIC(fit.p, k=log(3000))

## End(Not run)

Generic functions for class phyDat

Description

These functions help to manipulate alignments of class phyDat.

Usage

## S3 method for class 'phyDat'
print(x, ...)

## S3 method for class 'phyDat'
subset(x, subset, select, site.pattern = TRUE, ...)

## S3 method for class 'phyDat'
x[i, j, ..., drop = FALSE]

## S3 method for class 'phyDat'
unique(x, incomparables = FALSE, identical = TRUE, ...)

removeUndeterminedSites(x, ...)

removeAmbiguousSites(x)

allSitePattern(n, levels = NULL, names = NULL, type = "DNA", code = 1)

Arguments

Details

allSitePattern generates all possible site patterns and can be useful in simulation studies. For further details see the vignette AdvancedFeatures.

The generic function c can be used to to combine sequences and unique to get all unique sequences or unique haplotypes.

phyDat stores identical columns of an alignment only once and keeps an index of the original positions. This saves memory and especially computations as these are usually need to be done only once for each site pattern. In the example below the matrix x in the example has 8 columns, but column 1 and 2 and also 3 and 5 are identical. The phyDat object y has only 6 site pattern. If argument site.pattern=FALSE the indexing behaves like on the original matrix x. site.pattern=TRUE can be useful inside functions.

Value

The functions return an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

DNAbin, as.DNAbin, baseFreq, glance.phyDat, dna2codon, read.dna, read.nexus.data and the chapter 1 in the vignette("AdvancedFeatures", package="phangorn") and the example of pmlMix for the use of allSitePattern.

Examples

data(Laurasiatherian)
class(Laurasiatherian)
Laurasiatherian
# base frequencies
baseFreq(Laurasiatherian)
# subsetting phyDat objects
# the first 5 sequences
subset(Laurasiatherian, subset=1:5)
# the first 5 characters
subset(Laurasiatherian, select=1:5, site.pattern = FALSE)
# subsetting with []
Laurasiatherian[1:5, 1:20]
# short for
subset(Laurasiatherian, subset=1:5, select=1:20, site.pattern = FALSE)
# the first 5 site patterns (often more than 5 characters)
subset(Laurasiatherian, select=1:5, site.pattern = TRUE)

x <- matrix(c("a", "a", "c", "g", "c", "t", "a", "g",
              "a", "a", "c", "g", "c", "t", "a", "g",
              "a", "a", "c", "c", "c", "t", "t", "g"), nrow=3, byrow = TRUE,
            dimnames = list(c("t1", "t2", "t3"), 1:8))
(y <- phyDat(x))

subset(y, 1:2)
subset(y, 1:2, compress=TRUE)

subset(y, select=1:3, site.pattern = FALSE) |> as.character()
subset(y, select=1:3, site.pattern = TRUE) |> as.character()
y[,1:3] # same as subset(y, select=1:3, site.pattern = FALSE)

# Compute all possible site patterns
# for nucleotides there $4 ^ (number of tips)$ patterns
allSitePattern(5)

Function to import partitioned data from nexus files

Description

read.nexus.partitions reads in sequences in NEXUS format and splits the data according to the charsets given in the SETS block.

Usage

read.nexus.partitions(file, return = "list", ...)

Arguments

Value

a list where each element is a 'phyDat' object or an object of class 'multiphyDat'.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

read.nexus.data, read.phyDat

Examples

tree <- rtree(10)
dat <- simSeq(tree, l=24)
fcat <- function(..., file = zz) cat(..., file=file, sep="", append=TRUE)
zz <- tempfile(pattern="file", tmpdir=tempdir(), fileext=".nex")
write.phyDat(dat, file=zz, format="nexus")
fcat("BEGIN SETS;\n")
fcat("  Charset codon1 = 1-12/3;\n")
fcat("  Charset codon2 = 2-12/3;\n")
fcat("  Charset codon3 = 3-12/3;\n")
fcat("  Charset range = 16-18;\n")
fcat("  Charset range2 = 13-15 19-21;\n")
fcat("  Charset singles = 22 23 24;\n")
fcat("END;\n")

tmp <- read.nexus.partitions(zz)
tmp
unlink(zz)

Function to import and export splits and networks

Description

read.nexus.splits, write.nexus.splits, read.nexus.networx, write.nexus.networx can be used to import and export splits and networks with nexus format and allow to exchange these object with other software like SplitsTree. write.splits returns a human readable output.

Usage

read.nexus.splits(file)

write.nexus.splits(obj, file = "", weights = NULL, taxa = TRUE,
  append = FALSE)

write.nexus.networx(obj, file = "", taxa = TRUE, splits = TRUE,
  append = FALSE)

read.nexus.networx(file, splits = TRUE)

write.splits(x, file = "", zero.print = ".", one.print = "|",
  print.labels = TRUE, ...)

Arguments

Value

write.nexus.splits and write.nexus.networx write out the splits and networx object to read with other software like SplitsTree. read.nexus.splits and read.nexus.networx return an splits and networx object.

Note

read.nexus.splits reads in the splits block of a nexus file. It assumes that different co-variables are tab delimited and the bipartition are separated with white-space. Comments in square brackets are ignored.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

prop.part, lento, as.splits, as.networx

Examples

(sp <- as.splits(rtree(5)))
write.nexus.splits(sp)
spl <- allCircularSplits(5)
plot(as.networx(spl))
write.splits(spl, print.labels = FALSE)

Import and export sequence alignments

Description

These functions read and write sequence alignments.

Usage

read.phyDat(file, format = "phylip", type = "DNA", ...)

write.phyDat(x, file, format = "phylip", colsep = "", nbcol = -1, ...)

Arguments

Details

write.phyDat calls the function write.dna or write.nexus.data and read.phyDat calls the function read.dna or read.nexus.data, so see for more details over there.

You may import data directly with read.dna or read.nexus.data and convert the data to class phyDat.

Value

read.phyDat returns an object of class phyDat, write.phyDat write an alignment to a file.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

https://www.ncbi.nlm.nih.gov/blast/fasta.shtml Felsenstein, J. (1993) Phylip (Phylogeny Inference Package) version 3.5c. Department of Genetics, University of Washington. https://phylipweb.github.io/phylip/

See Also

read.dna, read.GenBank, phyDat, read.alignment

Examples

fdir <- system.file("extdata/trees", package = "phangorn")
primates <- read.phyDat(file.path(fdir, "primates.dna"),
                        format = "interleaved")

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

glance, tidy

Shimodaira-Hasegawa Test

Description

This function computes the Shimodaira–Hasegawa test for a set of trees.

Usage

SH.test(..., B = 10000, data = NULL, weight = NULL)

Arguments

Value

a numeric vector with the P-value associated with each tree given in ....

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Shimodaira, H. and Hasegawa, M. (1999) Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Molecular Biology and Evolution, 16, 1114–1116.

See Also

pml, pmlPart, pmlCluster, SOWH.test

Examples

data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree1 <- NJ(dm)
tree2 <- unroot(upgma(dm))
fit1 <- pml(tree1, Laurasiatherian)
fit2 <- pml(tree2, Laurasiatherian)
fit1 <- optim.pml(fit1) # optimize edge weights
fit2 <- optim.pml(fit2)
# with pml objects as input
SH.test(fit1, fit2, B=1000)
# in real analysis use larger B, e.g. 10000

# with matrix as input
X <- matrix(c(fit1$siteLik, fit2$siteLik), ncol=2)
SH.test(X, weight=attr(Laurasiatherian, "weight"), B=1000)
## Not run: 
example(pmlPart)
SH.test(sp, B=1000)

## End(Not run)

Simulate sequences.

Description

Simulate sequences from a given evolutionary tree.

Usage

simSeq(x, ...)

## S3 method for class 'phylo'
simSeq(x, l = 1000, Q = NULL, bf = NULL,
  rootseq = NULL, type = "DNA", model = NULL, levels = NULL,
  rate = 1, ancestral = FALSE, code = 1, ...)

## S3 method for class 'pml'
simSeq(x, ancestral = FALSE, ...)

Arguments

Details

simSeq is a generic function to simulate sequence alignments along a phylogeny. It is quite flexible and can generate DNA, RNA, amino acids, codon, morphological or binary sequences. simSeq can take as input a phylogenetic tree of class phylo, or a pml object; it will return an object of class phyDat. There is also a more low level version, which lacks rate variation, but one can combine different alignments with their own rates (see example). The rate parameter acts like a scaler for the edge lengths.

For codon models type="CODON", two additional arguments dnds for the dN/dS ratio and tstv for the transition transversion ratio can be supplied.

Defaults:

If x is a tree of class phylo, then sequences will be generated with the default Jukes-Cantor DNA model ("JC").

If bf is not specified, then all states will be treated as equally probable.

If Q is not specified, then a uniform rate matrix will be employed.

Value

simSeq returns an object of class phyDat.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

See Also

phyDat, pml, SOWH.test

Examples

## Not run: 
data(Laurasiatherian)
tree <- nj(dist.ml(Laurasiatherian))
fit <- pml(tree, Laurasiatherian, k=4)
fit <- optim.pml(fit, optNni=TRUE, model="GTR", optGamma=TRUE)
data <- simSeq(fit)

## End(Not run)


tree <- rtree(5)
plot(tree)
nodelabels()

# Example for simple DNA alignment
data <- simSeq(tree, l = 10, type="DNA", bf=c(.1,.2,.3,.4), Q=1:6,
               ancestral=TRUE)
as.character(data)


# Example to simulate discrete Gamma rate variation
rates <- discrete.gamma(1,4)
data1 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[1])
data2 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[2])
data3 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[3])
data4 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[4])
data <- c(data1,data2, data3, data4)

write.phyDat(data, file="temp.dat", format="sequential", nbcol = -1,
  colsep = "")
unlink("temp.dat")

Swofford-Olsen-Waddell-Hillis Test

Description

This function computes the Swofford–Olsen–Waddell–Hillis (SOWH) test, a parametric bootstrap test. The function is computational very demanding and likely to be very slow.

Usage

SOWH.test(x, n = 100, restricted = list(optNni = FALSE), optNni = TRUE,
  trace = 1, ...)

Arguments

Details

SOWH.test performs a parametric bootstrap test to compare two trees. It makes extensive use simSeq and optim.pml and can take quite long.

Value

an object of class SOWH. That is a list with three elements, one is a matrix containing for each bootstrap replicate the (log-) likelihood of the restricted and unrestricted estimate and two pml objects of the restricted and unrestricted model.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Goldman, N., Anderson, J. P., and Rodrigo, A. G. (2000) Likelihood -based tests of topologies in phylogenetics. Systematic Biology 49 652-670.

Swofford, D.L., Olsen, G.J., Waddell, P.J. and Hillis, D.M. (1996) Phylogenetic Inference in Hillis, D.M., Moritz, C. and Mable, B.K. (Eds.) Molecular Systematics (2nd ed.) 407-514, Sunderland, MA: Sinauer

See Also

pml, pmlPart, pmlCluster, simSeq, SH.test

Examples

# in real analysis use larger n, e.g. 500 preferably more
## Not run: 
data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree <- NJ(dm)
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit, TRUE)
set.seed(6)
tree <- rNNI(fit$tree, 1)
fit <- update(fit, tree = tree)
(res <- SOWH.test(fit, n=100))
summary(res)

## End(Not run)

Phylogenetic Network

Description

splitsNetwork estimates weights for a splits graph from a distance matrix.

Usage

splitsNetwork(dm, splits = NULL, gamma = 0.1, lambda = 1e-06,
  weight = NULL)

Arguments

Details

splitsNetwork fits non-negative least-squares phylogenetic networks using L1 (LASSO), L2(ridge regression) constraints. The function minimizes the penalized least squares

\beta = min \sum(dm - X\beta)^2 + \lambda \|\beta \|^2_2

with respect to

\|\beta \|_1 <= \gamma, \beta >= 0

where X is a design matrix constructed with designSplits. External edges are fitted without L1 or L2 constraints.

Value

splitsNetwork returns a splits object with a matrix added. The first column contains the indices of the splits, the second column an unconstrained fit without penalty terms and the third column the constrained fit.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Efron, Hastie, Johnstone and Tibshirani (2004) Least Angle Regression (with discussion) Annals of Statistics 32(2), 407–499

K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD Thesis)

See Also

distanceHadamard, designTree consensusNet, plot.networx

Examples

data(yeast)
dm <- dist.ml(yeast)
fit <- splitsNetwork(dm)
net <- as.networx(fit)
plot(net)
write.nexus.splits(fit)

Super Tree methods

Description

These function superTree allows the estimation of a supertree from a set of trees using either Matrix representation parsimony, Robinson-Foulds or SPR as criterion.

Usage

superTree(tree, method = "MRP", rooted = FALSE, trace = 0,
  start = NULL, multicore = FALSE, mc.cores = NULL, ...)

Arguments

Details

The function superTree extends the function mrp.supertree from Liam Revells, with artificial adding an outgroup on the root of the trees. This allows to root the supertree afterwards. The functions is internally used in DensiTree. The implementation for the RF- and SPR-supertree are very basic so far and assume that all trees share the same set of taxa.

Value

The function returns an object of class phylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com Liam Revell

References

Baum, B. R., (1992) Combining trees as a way of combining data sets for phylogenetic inference, and the desirability of combining gene trees. Taxon, 41, 3-10.

Ragan, M. A. (1992) Phylogenetic inference based on matrix representation of trees. Molecular Phylogenetics and Evolution, 1, 53-58.

See Also

mrp.supertree, densiTree, RF.dist, SPR.dist

Examples

data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian,
                       FUN = function(x) upgma(dist.hamming(x)), bs=50)

mrp_st <- superTree(bs, minit = 25, maxit=50)
plot(mrp_st)

rf_st <- superTree(bs, method = "RF")
spr_st <- superTree(bs, method = "SPR")

Internal phangorn Functions

Description

Internal phangorn functions.

Usage

threshStateC(x, thresholds)

candidate_tree(x, method = c("unrooted", "ultrametric", "tipdated"),
  eps = 1e-08, tip.dates = NULL, ...)

hash(x, ...)

map_duplicates(x, dist = length(x) < 500, ...)

coords(obj, dim = "3D")

pmlPen(object, lambda, ...)

Transfer Bootstrap

Description

transferBootstrap assigns transfer bootstrap (Lemoine et al. 2018) values to the (internal) edges.

Usage

transferBootstrap(tree, trees, phylo = TRUE, scale = TRUE)

Arguments

Value

a phylogentic tree (a phylo object) with bootstrap values assigned to the node labels.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D., De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature, 556(7702), 452–456.

See Also

plotBS, maxCladeCred, drawSupportOnEdges

Examples

fdir <- system.file("extdata/trees", package = "phangorn")
# RAxML best-known tree with bipartition support (from previous analysis)
raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse"))
# RAxML bootstrap trees (from previous analysis)
raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse"))

tree_tbe <- transferBootstrap(raxml.tree,  raxml.bootstrap)
par(mfrow=c(1,2))
plotBS(tree_tbe)
# same as
plotBS(raxml.tree,  raxml.bootstrap, "p", "TBE")

Distances between trees

Description

treedist computes different tree distance methods and RF.dist the Robinson-Foulds or symmetric distance. The Robinson-Foulds distance only depends on the topology of the trees. If edge weights should be considered wRF.dist calculates the weighted RF distance (Robinson & Foulds 1981). and KF.dist calculates the branch score distance (Kuhner & Felsenstein 1994). path.dist computes the path difference metric as described in Steel and Penny 1993). sprdist computes the approximate SPR distance (Oliveira Martins et al. 2008, de Oliveira Martins 2016).

Usage

treedist(tree1, tree2, check.labels = TRUE)

sprdist(tree1, tree2)

SPR.dist(tree1, tree2 = NULL)

RF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
  rooted = FALSE)

wRF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
  rooted = FALSE)

KF.dist(tree1, tree2 = NULL, check.labels = TRUE, rooted = FALSE)

path.dist(tree1, tree2 = NULL, check.labels = TRUE, use.weight = FALSE)

Arguments

Details

The Robinson-Foulds distance between two trees T_1 and T_2 with n tips is defined as (following the notation Steel and Penny 1993):

d(T_1, T_2) = i(T_1) + i(T_2) - 2v_s(T_1, T_2)

where i(T_1) denotes the number of internal edges and v_s(T_1, T_2) denotes the number of internal splits shared by the two trees. The normalized Robinson-Foulds distance is derived by dividing d(T_1, T_2) by the maximal possible distance i(T_1) + i(T_2). If both trees are unrooted and binary this value is 2n-6.

Functions like RF.dist returns the Robinson-Foulds distance (Robinson and Foulds 1981) between either 2 trees or computes a matrix of all pairwise distances if a multiPhylo object is given.

For large number of trees the distance functions can use a lot of memory!

Value

treedist returns a vector containing the following tree distance methods

Author(s)

Klaus P. Schliep klaus.schliep@gmail.com, Leonardo de Oliveira Martins

References

de Oliveira Martins L., Leal E., Kishino H. (2008) Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees. PLoS ONE 3(7). e2651. doi: 10.1371/journal.pone.0002651

de Oliveira Martins L., Mallo D., Posada D. (2016) A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction. Syst. Biol. 65(3): 397-416, doi:10.1093/sysbio/syu082

Steel M. A. and Penny P. (1993) Distributions of tree comparison metrics - some new results, Syst. Biol., 42(2), 126–141

Kuhner, M. K. and Felsenstein, J. (1994) A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates, Molecular Biology and Evolution, 11(3), 459–468

D.F. Robinson and L.R. Foulds (1981) Comparison of phylogenetic trees, Mathematical Biosciences, 53(1), 131–147

D.F. Robinson and L.R. Foulds (1979) Comparison of weighted labelled trees. In Horadam, A. F. and Wallis, W. D. (Eds.), Combinatorial Mathematics VI: Proceedings of the Sixth Australian Conference on Combinatorial Mathematics, Armidale, Australia, 119–126

See Also

dist.topo, nni, superTree, mast

Examples

tree1 <- rtree(100, rooted=FALSE)
tree2 <- rSPR(tree1, 3)
RF.dist(tree1, tree2)
treedist(tree1, tree2)
sprdist(tree1, tree2)
trees <- rSPR(tree1, 1:5)
SPR.dist(tree1, trees)

UPGMA, WPGMA and sUPGMA

Description

UPGMA and WPGMA clustering. UPGMA and WPGMA are a wrapper function around hclust returning a phylo object. supgma perform serial sampled UPGMA similar to Drummond and Rodrigo (2000).

Usage

upgma(D, method = "average", ...)

wpgma(D, method = "mcquitty", ...)

supgma(D, tip.dates, trace = 0)

Arguments

Value

A phylogenetic tree of class phylo.

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Sneath, P. H., & Sokal, R. R. (1973). Numerical taxonomy. The principles and practice of numerical classification.

Sokal, R. R., & Michener, C. D. (1958). A statistical method for evaluating systematic relationships. University of Kansas Scientific Bulletin, v. 38.

Drummond, A., & Rodrigo, A. G. (2000). Reconstructing genealogies of serial samples under the assumption of a molecular clock using serial-sample UPGMA. Molecular Biology and Evolution, 17(12), 1807-1815.

See Also

hclust, dist.hamming, NJ, as.phylo, fastme, nnls.tree, rtt

Examples

data(Laurasiatherian)
dm <- dist.ml(Laurasiatherian)
tree <- upgma(dm)
plot(tree)

Export and convenience functions for ancestral reconstructions

Description

write.ancestral allows to export ancestral reconstructions. It writes out the tree, a tab delimited text file with the probabilities and the alignment. ancestral generates an object of class ancestral.

Usage

write.ancestral(x, file = "ancestral")

as.ancestral(tree, align, prob)

## S3 method for class 'ancestral'
print(x, ...)

Arguments

Details

This allows also to read in reconstruction made by iqtree to use the plotting capabilities of R.

Value

write.ancestral returns the input x invisibly.

See Also

ancestral.pml, plotAnc

Examples

data(Laurasiatherian)
fit <- pml_bb(Laurasiatherian[,1:100], "JC", rearrangement = "none")
anc_ml <- anc_pml(fit)
write.ancestral(anc_ml)
# Can be also results from iqtree
align <- read.phyDat("ancestral_align.fasta", format="fasta")
tree <- read.tree("ancestral_tree.nwk")
df <- read.table("ancestral.state", header=TRUE)
anc_ml_disc <- as.ancestral(tree, align, df)
plotAnc(anc_ml_disc, 20)
unlink(c("ancestral_align.fasta", "ancestral_tree.nwk", "ancestral.state"))

Export pml objects

Description

write.pml writes out the ML tree and the model parameters.

Usage

write.pml(x, file = tempfile(), ...)

Arguments

Value

write.pml returns the input x invisibly.

See Also

ancestral.pml, plotAnc

Examples

data(woodmouse)
fit <- pml_bb(woodmouse, "JC", rearrangement = "none")
write.pml(fit, "woodmouse")
unlink(c("woodmouse_pml.txt", "woodmouse_tree.nwk"))

Writing and reading distances in phylip and nexus format

Description

readDist, writeDist and write.nexus.dist are useful to exchange distance matrices with other phylogenetic programs.

Usage

writeDist(x, file = "", format = "phylip", ...)

write.nexus.dist(x, file = "", append = FALSE, upper = FALSE,
  diag = TRUE, digits = getOption("digits"), taxa = !append)

readDist(file, format = "phylip")

read.nexus.dist(file)

## S3 method for class 'dist'
unique(x, incomparables, ...)

Arguments

Value

an object of class dist

Author(s)

Klaus Schliep klaus.schliep@gmail.com

References

Maddison, D. R., Swofford, D. L. and Maddison, W. P. (1997) NEXUS: an extensible file format for systematic information. Systematic Biology, 46, 590–621.

See Also

To compute distance matrices see dist.ml dist.dna and dist.p for pairwise polymorphism p-distances

Examples

data(yeast)
dm <- dist.ml(yeast)
writeDist(dm)
write.nexus.dist(dm)

Yeast alignment (Rokas et al.)

Description

Alignment of 106 genes of 8 different species of yeast.

References

Rokas, A., Williams, B. L., King, N., and Carroll, S. B. (2003) Genome-scale approaches to resolving incongruence in molecular phylogenies. Nature, 425(6960): 798–804

Examples

data(yeast)
str(yeast)