All distances between a pair of trees

Source: R/utilities.R

AllDists() calculates the distances between two trees, using a suite of distance measures.

AllDists(tr1, tr2, verbose = FALSE)

Arguments

tr1, tr2

Phylogenetic trees of class phylo.
verbose

Logical specifying whether to print messages allowing the user to keep track of progress.

Methods tested

  • pid: Phylogenetic Information Distance (Smith 2020), normalized against the phylogenetic information content of the splits in the trees being compared.

  • msid: Matching Split Information Distance (Smith 2020), normalized against the phylogenetic information content of the splits in the trees being compared.

  • cid: Clustering Information Distance (Smith 2020), normalized against the entropy of the splits in the trees being compared.

  • qd: Quartet divergence (Smith 2019), normalized against its maximum possible value for n-leaf trees.

  • nye: Nye et al. tree distance (Nye et al. 2006), normalized against the total number of splits in the trees being compared.

  • jnc2, jnc4: Jaccard-Robinson-Foulds distances with k = 2, 4, conflicting pairings prohibited ('no-conflict'), normalized against the total number of splits in the trees being compared.

  • jco2, jco4: Jaccard-Robinson-Foulds distances with k = 2, 4, conflicting pairings permitted ('conflict-ok'), normalized against the total number of splits in the trees being compared.

  • ms: Matching Split Distance (Bogdanowicz & Giaro 2012), unnormalized.

  • mast: Size of Maximum Agreement Subtree (Valiente 2009), unnormalized.

  • masti: Information content of Maximum Agreement Subtree, unnormalized.

  • nni_l, nni_L, nni_t, nni_U, nni_u: Lower, best lower, tight upper, best upper, and upper bounds for nearest-neighbour interchange distance (Li et al. 1996), unnormalized. 'Best' lower bounds jump sharply when mismatched regions of a tree become large enough that a tight upper bound cannot be exactly calculated, so are discontinuous and cannot readily be compared between trees.

  • spr: Approximate subtree prune and regraft SPR distance, unnormalized.

  • tbr_l, tbr_u: Lower and upper bound for tree bisection and reconnection (TBR) distance, calculated using TBRDist; unnormalized.

  • rf: Robinson-Foulds distance (Robinson & Foulds 1981), unnormalized.

  • icrf: Robinson-Foulds distance, splits weighted by phylogenetic information content (Smith 2020), unnormalized.

  • path: Path distance (Steel & Penny 1993), unnormalized.

References

Bogdanowicz D, Giaro K (2012). “Matching split distance for unrooted binary phylogenetic trees.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(1), 150--160. doi: 10.1109/TCBB.2011.48 .

Li M, Tromp J, Zhang L (1996). “Some notes on the nearest neighbour interchange distance.” In Goos G, Hartmanis J, Leeuwen J, Cai J, Wong CK (eds.), Computing and Combinatorics, volume 1090, 343--351. Springer, Berlin, Heidelberg. ISBN 978-3-540-61332-9 978-3-540-68461-9, doi: 10.1007/3-540-61332-3_168 .

Kendall M, Colijn C (2016). “Mapping phylogenetic trees to reveal distinct patterns of evolution.” Molecular Biology and Evolution, 33(10), 2735--2743. doi: 10.1093/molbev/msw124 .

Nye TMW, Liò P, Gilks WR (2006). “A novel algorithm and web-based tool for comparing two alternative phylogenetic trees.” Bioinformatics, 22(1), 117--119. doi: 10.1093/bioinformatics/bti720 .

Robinson DF, Foulds LR (1981). “Comparison of phylogenetic trees.” Mathematical Biosciences, 53(1-2), 131--147. doi: 10.1016/0025-5564(81)90043-2 .

Smith MR (2019). “Bayesian and parsimony approaches reconstruct informative trees from simulated morphological datasets.” Biology Letters, 15, 20180632. doi: 10.1098/rsbl.2018.0632 .

Smith MR (2020). “Information theoretic Generalized Robinson-Foulds metrics for comparing phylogenetic trees.” Bioinformatics, online ahead of print. doi: 10.1093/bioinformatics/btaa614 .

Steel MA, Penny D (1993). “Distributions of tree comparison metrics---some new results.” Systematic Biology, 42(2), 126--141. doi: 10.1093/sysbio/42.2.126 .

Valiente G (2009). Combinatorial Pattern Matching Algorithms in Computational Biology using Perl and R, CRC Mathematical and Computing Biology Series. CRC Press, Boca Raton.

See also

Other pairwise tree distances: CompareAllTrees(), PairwiseDistances()

Examples

library('TreeTools', quietly = TRUE, warn.conflict = FALSE)
AllDists(BalancedTree(8), PectinateTree(8))
#>        pid       msid        cid         qd        nye       jnc2       jnc4 
#>  0.3436186  0.1842247  0.3091981  0.2285714  0.2400000  0.3360000  0.3897600 
#>       jco2       jco4         ms       mast      masti      nni_l      nni_L 
#>  0.3360000  0.3897600  6.0000000  6.0000000  6.7142455  2.0000000  2.0000000 
#>      nni_t      nni_U      nni_u        spr      tbr_l      tbr_u         rf 
#>  2.0000000  2.0000000  4.0000000  2.0000000  1.0000000  3.0000000  4.0000000 
#>       icrf       path 
#> 17.0076515  5.6568542 
AllDists(list(BalancedTree(6), PectinateTree(6)), BalancedTree(6))
#>               [,1]      [,2]
#> pid   0.000000e+00 0.3065122
#> msid  0.000000e+00 0.1864629
#> cid   0.000000e+00 0.3081816
#> qd    0.000000e+00 0.2000000
#> nye   1.480297e-16 0.2222222
#> jnc2  1.480297e-16 0.3333333
#> jnc4  1.480297e-16 0.3333333
#> jco2  1.480297e-16 0.2962963
#> jco4  1.480297e-16 0.3292181
#> ms    0.000000e+00 2.0000000
#> mast  6.000000e+00 5.0000000
#> masti 6.714246e+00 3.9068906
#> nni_l 0.000000e+00 1.0000000
#> nni_L 0.000000e+00 1.0000000
#> nni_t 0.000000e+00 1.0000000
#> nni_U 0.000000e+00 1.0000000
#> nni_u 0.000000e+00 2.0000000
#> spr   0.000000e+00 1.0000000
#> tbr_l 0.000000e+00 1.0000000
#> tbr_u 0.000000e+00 3.0000000
#> rf    0.000000e+00 2.0000000
#> icrf  0.000000e+00 5.6147098
#> path  0.000000e+00 2.8284271