Detect rogue taxa using phylogenetic information distance

Calculate the volatility of each tip: namely, the impact on the mean phylogenetic information distance (Smith 2020) between trees when that tip is removed. Effective when the number of trees is small.

Usage

TipVolatility(trees)

Arguments

trees: List of trees to analyse.

Value

TipVolatility() returns a named vector listing the volatility index calculated for each leaf.

References

Smith MR (2020). “Information theoretic Generalized Robinson--Foulds metrics for comparing phylogenetic trees.” Bioinformatics, 36(20), 5007--5013. doi:10.1093/bioinformatics/btaa614 .

Examples

library("TreeTools", quietly = TRUE)

# Generate some trees with two rogue taxa
trees <- AddTipEverywhere(BalancedTree(8), "Rogue")
trees[] <- lapply(trees, AddTip, "Rogue", "Rogue2")

# Calculate tip volatility
sb <- TipVolatility(trees)

# Use volatility to colour leaves in consensus tree
sbNorm <- 1 + (99 * (sb - min(sb)) / (max(sb - min(sb))))
col <- hcl.colors(128, "inferno")[sbNorm]
plot(consensus(trees), tip.color = col)

# Add a legend for the colour scale used
PlotTools::SpectrumLegend(
  "bottomleft", bty = "n", # Suppress box
  inset = -0.02,           # Avoid overlap
  title = "Volatility",
  legend = signif(seq(max(sb), min(sb), length.out = 4), 3),
  palette = hcl.colors(128, "inferno")
)


# Plot consensus after removing highly volatile taxa
plot(ConsensusWithout(trees, names(sb[sb == max(sb)])))