My talk on doing phylogenetics in R

Recology has moved, go to http://recology.info/2011/11/my-talk-on-doing-phylogenetics-in-r

I gave a talk today on doing very basic phylogenetics in R, including getting sequence data, aligning sequence data, plotting trees, doing trait evolution stuff, etc.

Please comment if you have code for doing bayesian phylogenetic inference in R.  I know phyloch has function mrbayes, but can't get it to work...

Phylogenetics in R

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phylogenetic signal simulations

I did a little simulation to examine how K and lambda vary in response to tree size (and how they compare to each other on the same simulated trees). I use Liam Revell's functions fastBM to generate traits, and phylosig to measure phylogenetic signal.

Two observations: 


First, it seems that lambda is more sensitive than K to tree size, but then lambda levels out at about 40 species, whereas K continues to vary around a mean of 1.

Second, K is more variable than lambda at all levels of tree size (compare standard error bars).

Does this make sense to those smart folks out there?

Comparison of functions for comparative phylogenetics

With all the packages (and beta stage groups of functions) for comparative phylogenetics in R (tested here: picante, geiger, ape, motmot, Liam Revell's functions), I was simply interested in which functions to use in cases where multiple functions exist to do the same thing. I only show default settings, so perhaps these functions would differ under different parameter settings.  [I am using a Mac 2.4 GHz i5, 4GB RAM]

Get motmot here: https://r-forge.r-project.org/R/?group_id=782
Get Liam Revell's functions here: http://anolis.oeb.harvard.edu/~liam/R-phylogenetics/

> # Load 
require(motmot); require(geiger); require(picante)
source("http://anolis.oeb.harvard.edu/~liam/R-phylogenetics/phylosig/v0.3/phylosig.R")
source("http://anolis.oeb.harvard.edu/~liam/R-phylogenetics/fastBM/v0.4/fastBM.R")
 
# Make tree
tree <- rcoal(10)
 

# Transform branch lengths
> system.time( replicate(1000, transformPhylo(tree, model = "lambda", lambda = 0.5)) ) # motmot
   user  system elapsed 
  1.757   0.004   1.762 
> system.time( replicate(1000, lambdaTree(tree, 0.9)) ) # geiger
   user  system elapsed 
  3.708   0.008   3.716 
>   # motmot wins!!!

# Simulate trait evolution

system.time( replicate(1000, transformPhylo.sim(tree, model = "bm")) ) # motmot
   user  system elapsed 
  3.732   0.007   3.741 
> system.time( replicate(1000, rTraitCont(tree, model = "BM")) ) # ape
   user  system elapsed 
  0.312   0.009   0.321 
> system.time( replicate(1000, fastBM(tree)) ) # Revell
   user  system elapsed 
  1.315   0.005   1.320 
>   # ape wins!!!

# Phylogenetically independent contrasts
trait <- rnorm(10)
names(trait) <- tree$tip.label
 
> system.time( replicate(10000, pic.motmot(trait, tree)$contr[,1])  ) # motmot
   user  system elapsed 
  3.062   0.007   3.070 
> system.time( replicate(10000, pic(trait, tree)) ) # ape
   user  system elapsed 
  2.846   0.007   2.853 
>   # ape wins!!!

# Phylogenetic signal, Blomberg's K
> system.time( replicate(100, Kcalc(trait, tree))  ) # picante
   user  system elapsed 
  1.311   0.005   1.316 
> system.time( replicate(100, phylosig(tree, trait, method = "K")) ) # Revell
   user  system elapsed 
  0.201   0.000   0.202 
>   # Liam Revell wins!!!

# Ancestral character state estimation
> system.time( replicate(100, ace(trait, tree)$ace) ) # ape
   user  system elapsed 
  4.988   0.018   5.007 
> system.time( replicate(100, getAncStates(trait, tree)) ) # geiger
   user  system elapsed 
  2.253   0.005   2.258 
>   # geiger wins!!!

Created by Pretty R at inside-R.org


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It's hard to pick an overall winner because not all functions are available in all packages, but there are definitely some functions that are faster than others.