In KlausVigo/phangorn: Phylogenetic Reconstruction and Analysis

# set global chunk options: images will be bigger
knitr::opts_chunk$set(fig.width=8, fig.height=6)
options(digits = 4)

Maximum likelihood by hand

With the function pml_bb from phangorn [@Schliep2011] a lot of steps have become easier and shorter. If you want to have more control over all of the used parameters, it is also possible to use the older functions, e.g. optim_pml. The data is the same as in the vignette Estimating phylogenetic trees with phangorn:

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

As a starting tree, we calculate a neighbor joining tree:

dm <- dist.ml(primates)
treeNJ  <- NJ(dm)

fit <- pml(treeNJ, data=primates)
fit

The function pml returns an object of class pml. This object contains the data, the tree and many different parameters of the model like the likelihood. There are many generic functions for the class pml available, which allow the handling of these objects.

methods(class="pml")

The object fit just estimated the likelihood for the tree it got supplied, but the branch length are not optimized for the Jukes-Cantor [@Jukes1969] model yet, which can be done with the function optim.pml.

fitJC  <- optim.pml(fit, rearrangement="NNI")
logLik(fitJC)

With the default values pml will estimate a Jukes-Cantor model. That means equal base frequencies and all transition rates are equal. The generic function update allows to change parameters manually. This is not what we usually want to do. However we might want to supply a different tree or change the number of rate categories.

fitF81 <- update(fitJC, k=4, inv=0.2, bf=baseFreq(primates))
fitF81

In the line above we changed the model to a (discrete) rate across site model with 4 rate categories (using the default shape parameter of 1), to 0.2 invariant sites and supply empirical base frequencies.

fitGTR <- optim.pml(fitF81, model="GTR", optInv=TRUE, optGamma=TRUE,
    rearrangement = "NNI", control = pml.control(trace = 0))
fitGTR

We will change the model to the GTR + $\Gamma(4)$ + I model and then optimize all the parameters.

With the control parameters the thresholds for the fitting process can be changed. Here we want just to suppress output during the fitting process. For larger trees the NNI rearrangements often get stuck in a local maximum. We added two stochastic algorithms to improve topology search. The first (set rearrangement="stochastic") performs stochastic rearrangements similar as in [@Nguyen2015], which makes random NNI permutation to the tree, which than gets optimized to escape local optima. The second option (rearrangement="ratchet") perform the likelihood ratchet [@Vos2003].

While these algorithms may find better trees they will also take more time.

fitGTR <- optim.pml(fitGTR, model="GTR", optInv=TRUE, optGamma=TRUE,
    rearrangement = "stochastic", control = pml.control(trace = 0))
fitGTR

Model comparison

We can compare nested models for the JC and GTR + $\Gamma(4)$ + I model using likelihood ratio statistic

anova(fitJC, fitGTR)

with the Shimodaira-Hasegawa \cite{Shimodaira1999} test

SH.test(fitGTR, fitJC)

or with the AIC

AIC(fitJC)
AIC(fitGTR)
AICc(fitGTR)
BIC(fitGTR)

Bootstrap

At last we may want to apply standard bootstrap to test how well the edges of the tree are supported. This has already been shown in the vignette Estimating phylogenetic trees with phangorn.

bs <- bootstrap.pml(fitJC, bs=100, optNni=TRUE,
    control = pml.control(trace = 0))

Now we can plot the tree with the bootstrap support values on the edges and also look at consensusNet to identify potential conflict.

plotBS(midpoint(fitJC$tree), bs, p = 50, type="p")

cnet <- consensusNet(bs, p=0.2)
plot(cnet, show.edge.label=TRUE)

Generating trees

phangorn has several functions to generate tree topologies, which may are interesting for simulation studies. allTrees computes all possible bifurcating tree topologies either rooted or unrooted for up to 10 taxa. One has to keep in mind that the number of trees is growing exponentially, use howmanytrees from ape as a reminder.

trees <- allTrees(5)
par(mfrow=c(3,5), mar=rep(0,4))
for(i in 1:15)plot(trees[[i]], cex=1, type="u")

nni returns a list of all trees which are one nearest neighbor interchange away.

nni(trees[[1]])

rNNI and rSPR generate trees which are a defined number of NNI (nearest neighbor interchange) or SPR (subtree pruning and regrafting) away.

Session info

sessionInfo()

References

KlausVigo/phangorn documentation built on Jan. 16, 2025, 1:08 p.m.