Detecting rogue taxa in Bayesian posterior tree sets"

Detecting "rogue" taxa and removing them from summary trees can produce consensus trees with a higher resolution, and can reveal strong support for groupings that would otherwise be masked by the uncertain position of rogues.

The raw output of Bayesian analysis requires a little processing before rogue taxa can be identified and explored using the "Rogue" R package.

The workflow presented here should be reasonably easy to adapt for the output of any Bayesian phylogenetic analysis, but if you hit snags or get stuck please let me know by filing a GitHub issue or by e-mail.

Set up

Let's start by loading the packages we'll need:

library("TreeTools") # Read and plot trees
library("Rogue") # Find rogue taxa
online <- tryCatch({
    read.csv("https://raw.githubusercontent.com/ms609/hyoliths/master/MrBayes/hyo.nex.pstat")
    TRUE
  },
  warning = function (w) invokeRestart(""),
  error = function (e) FALSE
)

We'll work with some example data generated from a morphological analysis of early brachiopods [@Sun2018] using MrBayes [@Hulsenbeck2001]. Our data files are stored on GitHub. Let's load the results of run 1:

```{R load-trees} if (online) { dataFolder <- "https://raw.githubusercontent.com/ms609/hyoliths/master/MrBayes/" run1.t <- paste0(dataFolder, "hyo.nex.run1.t") # Reading 10k trees takes a second or two... run1Trees <- ape::read.nexus(run1.t) if (packageVersion('ape') <= "5.6.1") { # Workaround for a bug in ape, hopefully fixed in v5.6.2 run1Trees <- structure(lapply(run1Trees, function (tr) { tr$tip.label <- attr(run1Trees, 'TipLabel') tr }), class = 'multiPhylo') } } else { # If no internet connection, we can generate some example trees instead run1Trees <- structure(unlist(lapply(0:21, function (backbone) { AddTipEverywhere(ape::as.phylo(0, nTip = 12), 'ROGUE') }), recursive = FALSE), class = 'multiPhylo') }

## Select trees to analyse

Our tree file contains all trees generated.  We typically want to discard 
a proportion of trees as burn-in:

```{R discard-burnin}
burninFrac <- 0.25
nTrees <- length(run1Trees)
trees <- run1Trees[seq(from = burninFrac * nTrees, to = nTrees)]

This is a large number of trees to analyse. We could save time for an initial analysis by thinning our sample somewhat.

```{R thin-trees} sampleSize <- 100 trees <- run1Trees[seq(from = burninFrac * nTrees, to = nTrees, length.out = sampleSize)]

For a full analysis, we ought to consider the output from the other runs of our
analysis, perhaps with

```r
nRuns <- 4
allTrees <- lapply(seq_len(nRuns), function (run) {
  runTrees <- ape::read.nexus(paste0(dataFolder, 'hyo.nex.run', run, .'t'))
  runTrees <- runTrees[seq(from = burninFrac * nTrees, to = nTrees,
                           length.out = sampleSize / nRuns)]
})
trees <- structure(unlist(allTrees, recursive = FALSE), class = 'multiPhylo')

Initial appraisal

Let's start by looking at the majority rule consensus tree. It can be instructive to colour leaves by their instability; here we use the ad hoc approach of @SmithCons.

First let's define a function to plot a gradient legend:

# Adding a gradient as a legend is a slightly fussy task:
SpectrumLegend <- function (spectrum, labels) {
  nCol <- length(spectrum)

  segX <- rep(2, nCol)
  segY <- seq_len(nCol) / nCol
  legendY0 <- 2
  legendY1 <- (NTip(trees[1]) - 2) * 0.2
  segY <- legendY0 + (segY * (legendY1 - legendY0))
  segments(segX, segY - segY[1] + legendY0, y1 = segY, col = spectrum,
           lwd = 4)
  text(range(segX), c(legendY0, legendY1), 
       labels, pos = 4)
}
plenary <- Consensus(trees, p = 0.5)

par(mar = rep(0, 4), cex = 0.85)
plot(plenary, tip.color = ColByStability(trees))
SpectrumLegend(hcl.colors(131, 'inferno')[1:101], c("Stable", "Unstable"))

Some taxa stand out as having a less stable position on the tree than others. Will removing those taxa reveal enough additional information about the remaining taxa to compensate for the loss of information about where those taxa plot?

Detect rogue taxa

We have a few options for how we evaluate the negative impact of retaining these rogue taxa in our consensus tree.

QuickRogue() uses the quick heuristic method of @SmithCons; RogueTaxa() supports Smith's slower heuristic, which might find a set of rogue taxa that yield slightly more improvement to a consensus tree; it can also be configured to employ the RogueNaRok approach [@Aberer2013].

```{R find-rogues} rogues <- QuickRogue(trees)

rogues <- RogueTaxa(trees) might do a better job, much more slowly

rogues

The first line reports the information content of the plenary tree

rogueTaxa <- rogues$taxon[-1]

## Visualize results

Let's see how these taxa influence the majority rule consensus of our results.
Removing rogues may reveal information by producing reduced consensus trees
with a higher resolution, or with higher split support values.

```{R trees, fig.asp = 1/2, fig.width = 8}
par(mar = rep(0, 4)) # Remove plot margin
par(mfrow = c(1, 2)) # Multi-panel plot
par(cex = 0.85) # Smaller labels

plenary <- Consensus(trees, p = 0.5)
reduced <- ConsensusWithout(trees, rogueTaxa, p = 0.5)

plot(plenary,
     tip.color = ifelse(plenary$tip.label %in% rogueTaxa, 2, 1))
LabelSplits(plenary, SplitFrequency(plenary, trees))
plot(reduced)
LabelSplits(reduced, SplitFrequency(reduced, trees))

We can also visualize the locations where our rogue taxa would plot on the reduced consensus tree: the rogue occurs more frequently at the brighter locations.

par(mar = rep(0, 4), cex = 0.8)
whichTaxon <- length(rogueTaxa) # Select an illuminating taxon
positions <- RoguePlot(trees, rogueTaxa[whichTaxon], p = 0.5)

# Plot a legend for the edge colours
SpectrumLegend(spectrum = colorRampPalette(c(par("fg"), "#009E73"),
                                           space = "Lab")(100),
               labels = paste(range(positions$onEdge, positions$atNode),
                              'trees'))

References



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Rogue documentation built on Jan. 13, 2022, 5:07 p.m.