Datasets testing whether separating trees by increasingly many moves results in a corresponding increase in their distance.
sprDistances
A list of length 21. Each entry is named according to the corresponding tree distance method; see 'Methods tested' below.
Each member of the list is a 100 × 100 matrix listing the distance between each pair of trees in the SPR chain (see 'Details'), numbered from 1 to 100.
Scripts used to generate data objects are housed in the
data-raw
directory.
I generated a chain of 100 50-leaf trees, starting from a pectinate tree and deriving each tree in turn by performing an SPR operation on the previous tree. A consistent measure of tree similarity should correlate with the number of SPR operations separating a pair of trees in this chain. This said, because one SPR operation may counteract some of the difference introduced by a previous one, perfect correlation is unlikely.
For analysis of this data, see the accompanying vignette.
pid: Phylogenetic Information Distance (Smith 2020)
msid: Matching Split Information Distance (Smith 2020)
cid: Clustering Information Distance (Smith 2020)
qd: Quartet divergence (Smith 2019)
nye: Nye et al. tree distance (Nye et al. 2006)
jnc2, jnc4: Jaccard-Robinson-Foulds distances with k = 2, 4,
conflicting pairings prohibited ('no-conflict')
joc2, jco4: Jaccard-Robinson-Foulds distances with k = 2, 4,
conflicting pairings permitted ('conflict-ok')
ms: Matching Split Distance (Bogdanowicz & Giaro 2012)
mast: Size of Maximum Agreement Subtree (Valiente 2009)
masti: Information content of Maximum Agreement Subtree
nni_l, nni_t, nni_u: Lower
bound, tight upper bound, and upper bound
for nearest-neighbour interchange distance (Li et al. 1996)
spr: Approximate SPR distance
tbr_l, tbr_u: Lower and upper bound for tree bisection and reconnection
(TBR) distance, calculated using
TBRDist
rf: Robinson-Foulds distance (Robinson & Foulds 1981)
icrf: Information-corrected Robinson-Foulds distance (Smith 2020)
path: Path distance (Steel & Penny 1993)
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.