Primate Phylogeny
Carl Boettiger 00:37, 30 April 2010 (EDT):
- Simon sent me the tree data from their paper:
- Chatterjee HJ, Ho SY, Barnes I, and Groves C. Estimating the phylogeny and divergence times of primates using a supermatrix approach. BMC Evol Biol. 2009 Oct 27;9:259. DOI:10.1186/1471-2148-9-259 | PubMed ID:19860891 | HubMed [primates]
- This is the first calibrated ultrametric tree I've been able to couple to the primate data Daniel provided. I've preformed a quick ancestral state reconstruction on log brain weight (warmer colors are more massive).
R code to create figure
<syntaxhighlight lang="rsplus">
library(ape)
library(geiger)
genera_set <- read.nexus("mt.genera.ucld.trees")
nuc_set <- read.nexus("nuc.ucld.trees")
genera <- genera_set1
nuc <- nuc_set1
nuc_names <- nuc$tip.label
genera_names <- genera$tip.label
traits <- read.csv("Primate_brain_comparisons.csv")
dups <- duplicated(traits'Genus')
trait_names <- as.character(traits$Genus[!dups])
x <- traits$log_brain.weight[!dups]
names(x) <- trait_names
compare <- treedata(nuc, x)
plot(compare$phy)
out <- ace(compare$data, compare$phy)
- a similar function but for the continuous ancestral state
plot_cts_ancestor <- function(phy, data, ancestral){
plot(phy) # just to get treelength
treelen <- max(axisPhylo() )
plot(compare$phy, cex=1, x.lim=1.3*treelen, edge.width=2)
mycol <- function(x){
tmp = (x - min(data)) /max(x-min(data))
rgb(blue = 1-tmp, green=0, red = tmp )
}
nodelabels(pch=19, col=mycol(ancestral$ace), cex=1.5 )
tiplabels(pch=19, col=mycol(data), cex=1.5, adj=0.5) # add tip color code
}
plot_cts_ancestor(compare$phy, compare$data, out)
</syntaxhighlight>
- This is essentially just at the proof of concept level. A couple obvious steps:
- This uses a single tree from the posterior distribution of trees, which we'll eventually want to average over in the parameter estimation. Meanwhile should use a consensus MLE tree instead.
- Trees are resolved to genus level, so traits should be genus averages (instead of subsamples).
- Ancestral state reconstruction under the Brownian Motion model is the most preliminary treatment, will certainly be interesting to consider the various multi-peak OU models and identify potential transitions between peaks, as well as power analysis for the model fits.
- Multidimensional trait inference should also be interesting
- Possible bug? The Homo/Pan and Gorilla/Pan nodes would be expected to be in the Gorilla/Pan colour rather than the Homo colour.
Code and data on Github
New github repository for sharing code and data: sandbox
- Grab this repository using git:
git clone git://github.com/cboettig/sandbox.git
- Revert to this edit's version of code and data using git:
git checkout 77af6613db294ca068cf
- Carl Boettiger 16:17, 5 May 2010 (EDT):
- Updated git version using R package is:
git checkout b1a90ad97b90d9cbeaa7
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Evolutionary MRI
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