Citation

Identification of hidden population structure in time-scaled phylogenies

Erik Volz, Carsten Wiuf, Yonatan Grad, Simon Frost, Ann Dennis, Xavier Didelot, bioRxiv 704528; https://doi.org/10.1101/704528

Structured coalescent simulation

This example shows trestruct applied to a simulated structured coalescent tree that includes samples from a large constant size population and samples from three small ‘outbreaks’ which are growing exponentially. These simulations were generated with the phydynR package.

library(treestructure)

Load the tree:

( tree <- ape::read.tree( system.file('sim.nwk', package='treestructure') ) ) 
#> 
#> Phylogenetic tree with 275 tips and 274 internal nodes.
#> 
#> Tip labels:
#>  1, 1, 1, 1, 1, 1, ...
#> 
#> Rooted; includes branch lengths.

Note that the tip labels corresponds to the deme of each sample. ‘1’ is the constant size reservoir, and ‘0’ is the exponentially growing deme.

This will run the treestructure algorithm under default setting:

s <- trestruct( tree ) 
#> Tree has NA or duplicated tip labels. Adding a unique id.
#> Finding splits under nodes: 276 
#> Finding splits under nodes: 276 526 
#> Finding splits under nodes: 276 421 
#> Finding splits under nodes: 276 473

You can print the results:

print(s) 
#> Call: 
#> trestruct(tre = tree)
#> 
#> Significance level: 0.01 
#> Number of clusters: 4 
#> Number of partitions: 2 
#> Number of taxa in each cluster:
#> 
#>   1   2   3   4 
#>  25  25 200  25 
#> Number of taxa in each partition:
#> 
#>   1   2 
#>  75 200 
#> ...
#> For complete data, use `as.data.frame(...)`

The default plotting behavior uses the ggtree package if available.

plot(s)  + ggtree::geom_tiplab() 

If not, or if desired, ape plots are available

plot( s, use_ggtree = FALSE )

For subsequent analysis, you may want to turn the treestructure result into a dataframe:

structureData <- as.data.frame( s ) 
head( structureData )
#>   taxon cluster partition
#> 1   1_1       3         2
#> 2   2_1       3         2
#> 3   3_1       3         2
#> 4   4_1       3         2
#> 5   5_1       3         2
#> 6   6_1       3         2

Each cluster and partition assignment is stored as a factor. You could use split to get a data frame for each partition. Suppose we want a tree corresponding to partition 1:

with ( structureData, 
    ape::keep.tip(s$tree,  taxon[ partition==1 ] ) 
) -> partition1
partition1
#> 
#> Phylogenetic tree with 75 tips and 74 internal nodes.
#> 
#> Tip labels:
#>  143_0, 144_0, 145_0, 146_0, 147_0, 148_0, ...
#> 
#> Rooted; includes branch lengths.
plot(partition1)

Two parameters will have large influence on results:

1. level is the significance level for subdividing a clade into a new cluster. To detect more clusters, increase p, but note that this will also increase the false positive rate.
2. minCladeSize controls the smallest allowed cluster size in terms of the number of tips. With a smaller value, smaller clusters may be detected, but computation time will increase.

Example:

trestruct( tree, level = .05, minCladeSize = 5 )
#> Tree has NA or duplicated tip labels. Adding a unique id.
#> Finding splits under nodes: 276 
#> Finding splits under nodes: 276 526 
#> Finding splits under nodes: 276 421 
#> Finding splits under nodes: 276 473 
#> Finding splits under nodes: 276 359 
#> Finding splits under nodes: 276 277
#> Call: 
#> trestruct(tre = tree, minCladeSize = 5, level = 0.05)
#> 
#> Significance level: 0.05 
#> Number of clusters: 6 
#> Number of partitions: 2 
#> Number of taxa in each cluster:
#> 
#>   1   2   3   4   5   6 
#>  25  25  25   6  29 165 
#> Number of taxa in each partition:
#> 
#>   1   2 
#>  75 200 
#> ...
#> For complete data, use `as.data.frame(...)`