,
Juan A. Balbuena [aut, cph] ,
José Ignacio Lucas-Lledó [aut, cph]
,
Oscar Alejandro Pérez-Escobar [aut, cph]
| Title: | Random Tanglegram Partitions |
|---|---|
| Description: | Applies a given global-fit method to random partial tanglegrams of a fixed size to identify the associations, terminals, and nodes that maximize phylogenetic (in)congruence. It also includes functions to compute more easily the confidence intervals of classification metrics and plot results, reducing computational time. See "Llaberia-Robledillo et al. (2023, <doi:10.1093/sysbio/syad016>)". |
| Authors: | Mar Llaberia-Robledillo [aut, cre, cph]
,
Juan A. Balbuena [aut, cph] ,
José Ignacio Lucas-Lledó [aut, cph]
,
Oscar Alejandro Pérez-Escobar [aut, cph]
|
| Maintainer: | Mar Llaberia-Robledillo <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2 |
| Built: | 2025-02-09 05:15:58 UTC |
| Source: | https://github.com/mllaberia/rtapas |
Data set of mitochondrial haplotypes of the trematode Coitocaecum parvum (Crowcroft, 1945) and those of its amphipod host, Paracalliope fluviatilis (Thomson, 1879) from several locations in South Island, New Zeland (Largue et al. 2016).
data(amph_trem)data(amph_trem)
This data set compresses five objects:
am_matrixAssociations between 17 haplotypes of Coitocaecum parvum and 59 haplotypes of Paracalliope fluviatilis. A binary matrix with 59 rows (amphipod) and 17 variables (trematode).
amphipodParacalliope fluviatilis consensus tree.
An object of class "phylo" containing a list with the details of
the consensus phylogenetic tree (i.e. edges, edges length, nodes, and
tips names).
trematodeCoitocaecum parvum consensus tree.
An object of class "phylo" containing a list with the details of
the phylogenetic tree (i.e. edges, edges length, nodes and tips names).
amphipod_1000tr1000 Bayesian posterior probability trees
of Paracalliope fluviatilis.
List of class "multiphylo" containing a 1000 phylogenetic trees
with their respective details (i.e. edges, edges length, nodes, and
tips names).
trematode_1000tr1000 Bayesian posterior probability trees
of Coitocaecum parvum.
List of class "multiphylo" containing a 1000 phylogenetic trees
with their respective details (i.e. edges, edges length, nodes, and
tips names).
Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo. doi:10.5281/zenodo.6327235
Lagrue C., Joannes A., Poulin R., Blasco-Costa I. (2016). Genetic structure and host–parasite co‐divergence: evidence for trait‐specific local adaptation. Biological Journal of the Linnean Society. 118:344–358.
Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo. doi:10.5281/zenodo.6327235
Creates a binary host-symbiont association matrix from a two-columns matrix or data frame of host-symbiont associations.
assoc_mat(hs)assoc_mat(hs)
hs |
A two-columns matrix or data frame representing associations between hosts (column 1) and symbionts (column 2) species. |
An association binary matrix, with hosts in rows and symbionts in columns, sorted alphabetically.
# data(nuc_cp) # NTaxa <- sort(NUCtr$tip.label) # CPTaxa <- sort(CPtr$tip.label) # NC <- assoc_mat(data.frame(NTaxa, CPTaxa))# data(nuc_cp) # NTaxa <- sort(NUCtr$tip.label) # CPTaxa <- sort(CPtr$tip.label) # NC <- assoc_mat(data.frame(NTaxa, CPTaxa))
For any trimmed matrix produced with
trimHS_maxC() it prunes the host-symbiont
phylogenies to conform with the trimmed matrix and computes geodesic
distance between the pruned trees.
NOTE: This function can only be used with strictly bifurcating trees.
geo_D(ths, treeH, treeS, strat = "sequential", cl = 1)geo_D(ths, treeH, treeS, strat = "sequential", cl = 1)
ths |
A trimmed matrix. |
treeH |
Host phylogeny. An object of class |
treeS |
Symbiont phylogeny. An object of class |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
Geodesic distance
The node.label object in both trees can not contain NAs or null
values (i.e. no numeric value). All nodes should have a value. Else
remove node labels within the "phylo" class tree
with tree$node.label <- NULL. For more details, see
distory::dist.multiPhylo().
This function can not be used with the trimmed matrices produced
with \code{\link[=trimHS_maxI]{trimHS_maxI()}} or with the algorithm
\code{\link[=max_incong]{max_incong()}} in datasets with
multiple host-symbiont associations.
Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo.
Schardl C.L., Craven K.D., Speakman S., Stromberg A., Lindstrom A., Yoshida R. (2008). A Novel Test for Host-Symbiont Codivergence Indicates Ancient Origin of Fungal Endophytes in Grasses. Systematic Biology. 57:483–498.
Balbuena J.A., Perez-Escobar Ó.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.
# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # GD <- geo_D(TAM, amphipod, trematode, strat = "sequential", cl = 1)# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # GD <- geo_D(TAM, amphipod, trematode, strat = "sequential", cl = 1)
Computes and displays in a boxplot the Gini coefficient and their confidence intervals of the frequency (or residual/corrected frequencies) distributions of the estimated (in)congruence metric (with any of the three global-fit methods) of the individual host-symbiont associations.
gini_ci(LF_1, M01, ylab = "Gini coefficient", plot = TRUE, ...)gini_ci(LF_1, M01, ylab = "Gini coefficient", plot = TRUE, ...)
LF_1 |
Vector of statistics produced with
|
M01 |
Matrix produced with
|
ylab |
Title of the y label. |
plot |
Default is |
... |
Any optional argument admissible in
|
The Gini values obtained and their representation in a boxplot, with their confidence intervals.
It produces a conventional Gini coefficient (G)
(Ultsch and Lötsch 2017) if all output values are positive, or
a normalized Gini coefficient (G*) (Raffinetti et al. 2015) if
negative values are produced due to corrected frequencies
(if res.fq = TRUE or
diff.fq = TRUE). For more details see
Raffinetti et al. (2015).
Ultsch A., Lötsch J. (2017). A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions. PLOS ONE. 12:e0181572. doi:10.1371/journal.pone.0181572
Raffinetti E., Siletti E., Vernizzi A. (2015). On the Gini coefficient normalization when attributes with negative values are considered. Stat Methods Appl. 24:507–521. doi:10.1007/s10260-014-0293-4
data(nuc_cp) N = 1 #for the example, we recommend 1e+4 value n = 15 # Maximizing congruence NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE) # Loaded directly from dataset # THSC <- trimHS_maxC(N, np_matrix, n) # pp_treesPACo_cong <- prob_statistic(ths = THSc, np_matrix, NUC_500tr[1:10], # CP_500tr[1:10], freqfun = "paco", NPc_PACo, # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, correction = "none") gini_ci(LF_1 = NPc_PACo, M01 = pp_treesPACo_cong, ylab = "Gini Coefficient (G)", plot = TRUE, ylim = c(0.3, 0.8)) abline(h = 1/3) # because res.fq = TRUEdata(nuc_cp) N = 1 #for the example, we recommend 1e+4 value n = 15 # Maximizing congruence NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE) # Loaded directly from dataset # THSC <- trimHS_maxC(N, np_matrix, n) # pp_treesPACo_cong <- prob_statistic(ths = THSc, np_matrix, NUC_500tr[1:10], # CP_500tr[1:10], freqfun = "paco", NPc_PACo, # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, correction = "none") gini_ci(LF_1 = NPc_PACo, M01 = pp_treesPACo_cong, ylab = "Gini Coefficient (G)", plot = TRUE, ylim = c(0.3, 0.8)) abline(h = 1/3) # because res.fq = TRUE
Computes the Gini coefficient adjusted for negative (even weighted) data.
gini_RSV(y)gini_RSV(y)
y |
a vector of attributes containing even negative elements |
It produces a conventional Gini coefficient (G)
(Ultsch and Lötsch 2017) if all output values are positive, or
a normalized Gini coefficient (G*) (Raffinetti et al. 2015) if
negative values are produced due to corrected frequencies
(if res.fq = TRUE or
diff.fq = TRUE). For more details see
Raffinetti et al. (2015).
Ultsch A., Lötsch J. (2017). A data science based standardized Gini index as a Lorenz dominance preserving measure of the inequality of distributions. PLOS ONE. 12:e0181572. doi:10.1371/journal.pone.0181572
Raffinetti E., Siletti E., Vernizzi A. (2015). On the Gini coefficient normalization when attributes with negative values are considered. Stat Methods Appl. 24:507–521. doi:10.1007/s10260-014-0293-4
# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # Maximizing congruence # NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, res.fq = FALSE) # gini_RSV(y = NPc_PACo)# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # Maximizing congruence # NPc_PACo <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, res.fq = FALSE) # gini_RSV(y = NPc_PACo)
Determines the frequency (or residual/corrected frequency) of each host-symbiont association in a given percentile of cases that maximize phylogenetic (in)congruence.
link_freq( x, fx, HS, percentile = 0.01, sep = "-", below.p = TRUE, res.fq = TRUE )link_freq( x, fx, HS, percentile = 0.01, sep = "-", below.p = TRUE, res.fq = TRUE )
x |
List of trimmed matrices produced by
|
fx |
Vector of statistics produced with |
HS |
Host-symbiont association matrix. |
percentile |
Percentile to evaluate (p). Default is
|
sep |
Character that separates host and symbiont labels. |
below.p |
Determines whether frequencies are to be computed below or
above the percentile set. Default is |
res.fq |
Determines whether a correction to avoid one-to-one
associations being overrepresented in the percentile evaluated.
If |
A dataframe with host-symbiont associations in rows. The first and
second columns display the names of the host and symbiont terminals,
respectively. The third column designates the host-symbiont
association by pasting the names of the terminals, and the fourth
column displays the frequency of occurrence of each host-symbiont
association. If res.fq = TRUE, column 5 displays the
corrected frequencies as a residual.
The res.fq = TRUE correction is recommended in tanglegrams with
large portion of multiple (as opposed to one-to-one) host-symbiont
associations. For future usage, frequencies of host-symbiont
associations above a given percentile values can also be computed
setting below.p = FALSE.
# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE, # ei.correct = "sqrt.D", strat = "parallel", cl = 8) # LFPACO <- link_freq(TAM, PACO, am_matrix, percentile = 0.01, # below.p = TRUE, res.fq = TRUE)# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE, # ei.correct = "sqrt.D", strat = "parallel", cl = 8) # LFPACO <- link_freq(TAM, PACO, am_matrix, percentile = 0.01, # below.p = TRUE, res.fq = TRUE)
From the matrix obtained in prob_statistic(),
compute the confidence intervals for the frequencies (or residual/corrected
frequencies) of the host-symbiont associations using a set of pairs of
posterior probability trees of host and symbiont.
linkf_CI( freqfun = "paco", x, fx, c.level = 95, barplot = TRUE, col.bar = "lightblue", col.ci = "darkblue", y.lim = NULL, ... )linkf_CI( freqfun = "paco", x, fx, c.level = 95, barplot = TRUE, col.bar = "lightblue", col.ci = "darkblue", y.lim = NULL, ... )
freqfun |
Global-fit method. Options are |
x |
Matrix produced with |
fx |
Vector of statistics produced with
|
c.level |
Confidence interval level. Default is |
barplot |
Default is |
col.bar |
A vector of colors for the bars or bar components.
By default, |
col.ci |
A vector of colors for the confidence intervals arrows.
By default, |
y.lim |
Limits for the y axis. |
... |
Any graphical option admissible in
|
A dataframe with associations information (columns 1 and 2), the observed value of the frequencies for these associations (column 3), the mean, the minimum and the maximum value of the frequencies (columns 4, 5 and 6) obtained with the sets of posterior probability trees.
# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # Maximizing incongruence # NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE, # strat = "parallel", cl = 8) # Loaded directly from dataset # THSi <- trimHS_maxI(N, np_matrix, n) # pp_treesPACo_incong <- prob_statistic(ths = THSi, np_matrix, # NUC_500tr[1:5], CP_500tr[1:5], freqfun = "paco", # NPi, symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE, res.fq = FALSE, # below.p = FALSE, strat = "parallel", cl = 8) # LFci <- linkf_CI (freqfun = "paco", x = pp_treesPACo_incong, fx = NPi, # c.level = 95, ylab = "Observed - Expected frequency")# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # Maximizing incongruence # NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE, # strat = "parallel", cl = 8) # Loaded directly from dataset # THSi <- trimHS_maxI(N, np_matrix, n) # pp_treesPACo_incong <- prob_statistic(ths = THSi, np_matrix, # NUC_500tr[1:5], CP_500tr[1:5], freqfun = "paco", # NPi, symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE, res.fq = FALSE, # below.p = FALSE, strat = "parallel", cl = 8) # LFci <- linkf_CI (freqfun = "paco", x = pp_treesPACo_incong, fx = NPi, # c.level = 95, ylab = "Observed - Expected frequency")
Prunes the host (H) and symbiont (S) phylogenies to conform with trimmed matrices and computes the given global fit method, Geodesic distances (GD), Procrustes Approach to Cophylogeny (PACo) or ParaFit (Legendre et al. 2002) between the pruned trees. Then, determines the frequency or corrected residual of each host-symbiont association occurring in a given percentile of cases that maximize phylogenetic congruence.
max_cong( HS, treeH, treeS, n, N, method = "paco", symmetric = FALSE, ei.correct = "none", percentile = 0.01, res.fq = TRUE, strat = "sequential", cl = 1 )max_cong( HS, treeH, treeS, n, N, method = "paco", symmetric = FALSE, ei.correct = "none", percentile = 0.01, res.fq = TRUE, strat = "sequential", cl = 1 )
HS |
Host-Symbiont association matrix. |
treeH |
Host phyolgeny. An object of class "phylo". |
treeS |
Symbiont phylogeny. An object of class "phylo". |
n |
Number of unique associations. |
N |
Number of runs. |
method |
Specifies the desired global-fit method (GD, PACo or ParaFit).
The default is |
symmetric |
Specifies the type of Procrustes superimposition. Default
is |
ei.correct |
Specifies how to correct potential negative eigenvalues
from the conversion of phylogenetic distances into Principal
Coordinates: |
percentile |
Percentile to evaluate (p). Default is
|
res.fq |
Determines whether a correction to avoid one-to-one
associations being overrepresented in the percentile evaluated.
If |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A dataframe with host-symbiont associations in rows. The first and
second columns display the names of the host and symbiont terminals,
respectively. The third column designates the host-symbiont
association by pasting the names of the terminals, and the fourth
column displays the frequency of occurrence of each host-symbiont
association in p. If res.fq = TRUE, column 5 displays
the corrected frequencies as a residual.
If the node.label object in both trees contains NAs or empty
values (i.e. no numeric value). All nodes should have a value. Else
remove node labels within the "phylo" class tree
with tree$node.label <- NULL. For more details, see
distory::dist.multiPhylo()
data(nuc_pc) N = 1 #for the example, we recommend 1e+4 value n = 15 NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE)data(nuc_pc) N = 1 #for the example, we recommend 1e+4 value n = 15 NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE)
Prunes the host (H) and symbiont (S) phylogenies to conform with the trimmed matrix and computes the given global-fit method (PACo or ParaFit) between the pruned trees. Then, determines the frequency of each host-symbiont association occurring in a given percentile of cases that maximize phylogenetic incongruence.
max_incong( HS, treeH, treeS, n, N, method = "paco", symmetric = FALSE, ei.correct = "none", percentile = 0.99, diff.fq = FALSE, strat = "sequential", cl = 1 )max_incong( HS, treeH, treeS, n, N, method = "paco", symmetric = FALSE, ei.correct = "none", percentile = 0.99, diff.fq = FALSE, strat = "sequential", cl = 1 )
HS |
Host-Symbiont association matrix. |
treeH |
Host phyolgeny. An object of class "phylo". |
treeS |
Symbiont phylogeny. An object of class "phylo". |
n |
Number of associations. |
N |
Number of runs. |
method |
Specifies the desired global-fit method (PACo or ParaFit).
The default is |
symmetric |
Specifies the type of Procrustes superimposition. Default
is |
ei.correct |
Specifies how to correct potential negative eigenvalues
from the conversion of phylogenetic distances into Principal
Coordinates: |
percentile |
Percentile to evaluate (p). Default is
|
diff.fq |
Determines whether a correction to detect those associations
that present a similar contribution to (in)congruence and occur with
some frequency at the 0.01 and 0.99 percentiles. These correction
avoid multiple associations being overrepresented.
If |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A dataframe with host-symbiont associations in rows. The first and
second columns display the names of the host and symbiont terminals,
respectively. The third column designates the host-symbiont
association by pasting the names of the terminals, and the fourth
column displays the frequency of occurrence of each host-symbiont
association in p. If diff.fq = TRUE, column 5 displays
the corrected frequencies.
The node.label object in both trees can not contain NAs or null
values (i.e. no numeric value). All nodes should have a value. Else
remove node labels within the "phylo" class tree
with tree$node.label <- NULL. For more details, see
distory::dist.multiPhylo().
\code{GD} method can not be used with the trimmed matrices produced
with \code{\link[=trimHS_maxI]{trimHS_maxI()}} or with the algorithm
\code{\link[=max_incong]{max_incong()}} for those datasets with
multiple associations.
data(nuc_pc) N = 1 #for the example, we recommend 1e+4 value n = 15 NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.99, diff.fq = TRUE)data(nuc_pc) N = 1 #for the example, we recommend 1e+4 value n = 15 NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = FALSE, ei.correct = "sqrt.D", percentile = 0.99, diff.fq = TRUE)
Data set of nuclear and chloroplast loci of 52 orchid taxa from Kew DNA and Tissue Collection, https://dnabank.science.kew.org/homepage.html (Perez-Escobar et al. 2021).
data(nuc_cp)data(nuc_cp)
This data set consists of seven objects:
np_matrixAssociations one-to-one between the 52 orchid taxa. A binary matrix with 52 rows (nuclear) and 52 columns (chloroplast).
NUCtrPhylogeny constructed by sequence data of nuclear
loci of orchids (Perez-Escobar et al. 2021).
An object of class "phylo" containing the details of
the phylogenetic tree (i.e. edge, edge length, nodes and tips names).
CPtrPhylogeny constructed by sequence data of chloroplast
loci of orchids (Perez-Escobar et al. 2021).
An object of class "phylo" containing the details of
the phylogenetic tree (i.e. edge, edge length, nodes and tips names).
NUC_500tr500 bootstrap replicates trees from
Perez-Escobar et al. (2021).
Object of class "multiphylo" containing a 500 phylogenetic trees
with their respective details (i.e. edges, edges length, nodes, and
tips names).
CP_500tr500 bootstrap replicates trees from
Perez-Escobar et al. (2021).
Object of class "multiphylo" containing a 500 phylogenetic trees
with their respective details (i.e. edges, edges length, nodes, and
tips names).
pp_treesPACo_congMatrix with the value of the PACo statistics generated for each pair (H and S) of posterior probability trees maximizing congruence between them.
pp_treesPACo_incongMatrix with the value of the PACo statistics generated for each pair (H and S) of posterior probability trees maximizing incongruence between them.
Perez-Escobar O.A., Dodsworth S., Bogarin D., Bellot S., Balbuena J.A., Schley R., Kikuchi I., Morris S.K., Epitawalage N., Cowan R., Maurin O., Zuntini A., Arias T., Serna A., Gravendeel B., Torres M.F., Nargar K., Chomicki G., Chase M.W., Leitch I.J., Forest F., Baker W.J. (2021). Hundreds of nuclear and plastid loci yield novel insights into orchid relationships. American Journal of Botany, 108(7), 1166-1180.
Perez-Escobar O.A., Dodsworth S., Bogarin D., Bellot S., Balbuena J.A., Schley R., Kikuchi I., Morris S.K., Epitawalage N., Cowan R., Maurin O., Zuntini A., Arias T., Serna A., Gravendeel B., Torres M.F., Nargar K., Chomicki G., Chase M.W., Leitch I.J., Forest F., Baker W.J. (2021). Hundreds of nuclear and plastid loci yield novel insights into orchid relationships. American Journal of Botany, 108(7), 1166-1180.
For a binary matrix of host-symbiont associations, it finds the maximum
number of host-symbiont pairs, n, for which one-to-one unique
associations can be chosen.
one2one_f( HS, reps = 10000, interval = NULL, strat = "sequential", cl = 1, plot = TRUE )one2one_f( HS, reps = 10000, interval = NULL, strat = "sequential", cl = 1, plot = TRUE )
HS |
Host-symbiont association matrix. |
reps |
Number of runs to evaluate. |
interval |
Vector with the minimum and maximum |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
plot |
Default is |
The maximum number of unique one-to-one associations
(n).
It can be used to decide the best n prior to application of
max_cong().
# N = 10 #for the example, we recommend 1e+4 value # data(amph_trem) # n <- one2one_f(am_matrix, reps = N, interval = c(2, 10), plot = TRUE)# N = 10 #for the example, we recommend 1e+4 value # data(amph_trem) # n <- one2one_f(am_matrix, reps = N, interval = c(2, 10), plot = TRUE)
For any trimmed matrix produced with
trimHS_maxC() or
trimHS_maxI(), it prunes the host (H) and
symbiont (S) phylogenies to conform with the trimmed matrix and runs
Procruste Approach to Cophylogeny (PACo) to produce the squared sum of
residuals of the Procrustes superimposition of the host and symbiont
configurations in Euclidean space.
paco_ss( ths, treeH, treeS, symmetric = FALSE, proc.warns = FALSE, ei.correct = "none", strat = "sequential", cl = 1 )paco_ss( ths, treeH, treeS, symmetric = FALSE, proc.warns = FALSE, ei.correct = "none", strat = "sequential", cl = 1 )
ths |
Trimmed matrix. |
treeH |
Host phylogeny. An object of class |
treeS |
Symbiont phylogeny. An object of class |
symmetric |
Specifies the type of Procrustes superimposition. Default
is |
proc.warns |
Switches on/off trivial warnings returned when treeH and
treeS differ in size (number of tips). Default is |
ei.correct |
Specifies how to correct potential negative eigenvalues
from the conversion of phylogenetic distances into Principal
Coordinates: |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A sum of squared residuals.
Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2022). User’s Guide Random Tanglegram Partitions V.1.0.0. Zenodo.
Balbuena J.A., Miguez-Lozano R., Blasco-Costa I. (2013). PACo: A Novel Procrustes Application to Cophylogenetic Analysis. PLOS ONE. 8:e61048.
Balbuena J.A., Perez-Escobar Ó.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.
# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE, # ei.correct = "sqrt.D", strat = "parallel", cl = 8)# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PACO <- paco_ss(TAM, amphipod, trematode, symmetric = TRUE, # ei.correct = "sqrt.D", strat = "parallel", cl = 8)
For any trimmed matrix produced with
trimHS_maxC() or
trimHS_maxI(), it prunes the host (H) and
symbiont (S) phylogenies to conform with the trimmed matrix and runs
ape::parafit() (Legendre et al. 2002) to
calculate the ParaFitGlobal Statistic.
paraF(ths, treeH, treeS, ei.correct = "none", strat = "sequential", cl = 1)paraF(ths, treeH, treeS, ei.correct = "none", strat = "sequential", cl = 1)
ths |
Trimmed matrix. |
treeH |
Host phylogeny. An object of class |
treeS |
Symbiont phylogeny. An object of class |
ei.correct |
Specifies how to correct potential negative eigenvalues
from the conversion of phylogenetic distances into Principal
Coordinates: |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A number object with the ParaFitGlobal Statistic of host-symbiont test for the N trimmed matrix.
Legendre P., Desdevises Y., Bazin E. (2002). A Statistical Test for Host–Parasite Coevolution. Systematic Biology. 51:217–234.
Balbuena J.A., Perez-Escobar O.A., Llopis-Belenguer C., Blasco-Costa I. (2020). Random Tanglegram Partitions (Random TaPas): An Alexandrian Approach to the Cophylogenetic Gordian Knot. Systematic Biology. 69:1212–1230.
# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PF <- paraF(TAM, amphipod, trematode, ei.correct = "sqrt.D", # strat = "parallel", cl = 8)# data(amph_trem) # N = 10 #for the example, we recommend 1e+4 value # n = 8 # TAM <- trimHS_maxC(N, am_matrix, n, check.unique = TRUE) # PF <- paraF(TAM, amphipod, trematode, ei.correct = "sqrt.D", # strat = "parallel", cl = 8)
Computes frequencies (or residual/corrected frequencies) of the
host-symbiont associations for pairs (H and S) of posterior probability
trees from the statistics generatedwith GD (Geodesic Distances),
PACo (PACo) or ParaFit(ParaFit).
prob_statistic( ths, HS, mTreeH, mTreeS, freqfun = "paco", fx, percentile = 0.01, correction = "none", symmetric = FALSE, ei.correct = "none", algm = "maxcong", proc.warns = FALSE, strat = "sequential", cl = 1 )prob_statistic( ths, HS, mTreeH, mTreeS, freqfun = "paco", fx, percentile = 0.01, correction = "none", symmetric = FALSE, ei.correct = "none", algm = "maxcong", proc.warns = FALSE, strat = "sequential", cl = 1 )
ths |
List of trimmed matrices produced by
|
HS |
Host-Symbiont association matrix. |
mTreeH |
Number of posterior-probability trees of host. |
mTreeS |
Number of posterior-probability trees of symbiont. |
freqfun |
The global-fit method to compute using the
posterior probability trees. Options are |
fx |
Vector of statistics produced with
|
percentile |
Percentile to evaluate (p). Default is
|
correction |
Correction to be assumed. The default value is
|
symmetric |
Specifies the type of Procrustes superimposition. Default
is |
ei.correct |
Specifies how to correct potential negative eigenvalues
from the conversion of phylogenetic distances into Principal
Coordinates: |
algm |
Only required if |
proc.warns |
Switches on/off trivial warnings returned when treeH and
treeS differ in size (number of tips). Default is |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A matrix with the value of the statistics for each of the probability trees.
# data(nuc_pc) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # Maximizing congruence (not run) # NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, correction = "none", # strat = "parallel", cl = 8) # THSc <- trimHS_maxC(N, np_matrix, n) # pp_treesPACOo_cong <- prob_statistic(THSc, np_matrix, NUC_500tr[1:10], # CP_500tr[1:10], freqfun = "paco", NPc, # percentile = 0.01, correction = "none", # algm = "maxcong", symmetric = FALSE, # ei.correct = "sqrt.D", # strat = "parallel", cl = 8) # Maximizing incongruence # NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE) # THSi <- trimHS_maxI(N, np_matrix, n) # pp_treesPACOo_incong <- prob_statistic(THSi, np_matrix, NUC_500tr[1:5], # CP_500tr[1:5], freqfun = "paco", NPi, # percentile = 0.99, correction = "diff.fq", # symmetric = FALSE, ei.correct = "sqrt.D", # strat = "parallel", cl = 8)# data(nuc_pc) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # Maximizing congruence (not run) # NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.01, correction = "none", # strat = "parallel", cl = 8) # THSc <- trimHS_maxC(N, np_matrix, n) # pp_treesPACOo_cong <- prob_statistic(THSc, np_matrix, NUC_500tr[1:10], # CP_500tr[1:10], freqfun = "paco", NPc, # percentile = 0.01, correction = "none", # algm = "maxcong", symmetric = FALSE, # ei.correct = "sqrt.D", # strat = "parallel", cl = 8) # Maximizing incongruence # NPi <- max_incong(np_matrix, NUCtr, CPtr, n, N, method = "paco", # symmetric = FALSE, ei.correct = "sqrt.D", # percentile = 0.99, diff.fq = TRUE) # THSi <- trimHS_maxI(N, np_matrix, n) # pp_treesPACOo_incong <- prob_statistic(THSi, np_matrix, NUC_500tr[1:5], # CP_500tr[1:5], freqfun = "paco", NPi, # percentile = 0.99, correction = "diff.fq", # symmetric = FALSE, ei.correct = "sqrt.D", # strat = "parallel", cl = 8)
Maps the estimated (in)congruence metrics of the individual host-symbiont associations as heatmap on a tanglegram. It also plots the average frequency (or residual/corrected frequency) of occurrence of each terminal and optionally, the fast maximum likelihood estimators of ancestral states of each node.
tangle_gram( treeH, treeS, HS, fqtab, colscale = "diverging", colgrad, nbreaks = 50, node.tag = TRUE, cexpt = 1, link.lwd = 1, link.lty = 1, fsize = 0.5, pts = FALSE, link.type = "straight", ftype = "i", ... )tangle_gram( treeH, treeS, HS, fqtab, colscale = "diverging", colgrad, nbreaks = 50, node.tag = TRUE, cexpt = 1, link.lwd = 1, link.lty = 1, fsize = 0.5, pts = FALSE, link.type = "straight", ftype = "i", ... )
treeH |
Host phylogeny. An object of class |
treeS |
Symbiont phylogeny. An object of class |
HS |
Host-symbiont association matrix. |
fqtab |
Dataframe produced with |
colscale |
Choose between |
colgrad |
Vector of R specified colors defining the color gradient of the heatmap. |
nbreaks |
Number of discrete values along |
node.tag |
Specifies whether maximum likelihood estimators of ancestral
states are to be computed. Default is |
cexpt |
Size of color points at terminals and nodes. |
link.lwd |
Line width for plotting, default to 1. |
link.lty |
Line type. Coded as |
fsize |
Relative font size for tip labels. |
pts |
Logical value indicating whether or not to plot filled circles at
each vertex of the tree, as well as at transition points between
mapped states. Default is |
link.type |
If curved linking lines are desired, set to |
ftype |
Font type. Options are |
... |
Any graphical option admissible in
|
A tanglegram with quantitative information displayed as heatmap.
In order to calculate the ancestral states in the phylogenies, all nodes of the trees (node.label) must have a value (NA or empty values are not allowed). In addition, the trees must be time-calibrated and preferably rooted. If one of these elements is missing, an error will be generated and nodes and points of terminals will be displayed as black.
data(nuc_cp) N = 10 #for the example, we recommend 1e+4 value n = 8 NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = TRUE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE, strat = "parallel", cl = 4) col = c("darkorchid4", "gold") tangle_gram(NUCtr, CPtr, np_matrix, NPc, colscale = "sequential", colgrad = col, nbreaks = 50, node.tag = TRUE)data(nuc_cp) N = 10 #for the example, we recommend 1e+4 value n = 8 NPc <- max_cong(np_matrix, NUCtr, CPtr, n, N, method = "paco", symmetric = TRUE, ei.correct = "sqrt.D", percentile = 0.01, res.fq = FALSE, strat = "parallel", cl = 4) col = c("darkorchid4", "gold") tangle_gram(NUCtr, CPtr, np_matrix, NPc, colscale = "sequential", colgrad = col, nbreaks = 50, node.tag = TRUE)
For N runs, it randomly chooses n unique one-to-one associations and
trims the H-S association matrix to include only the n associations.
trimHS_maxC(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)trimHS_maxC(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)
N |
Number of runs. |
HS |
Host-Symbiont association matrix. |
n |
Number of unique associations. |
check.unique |
if |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A list of the N trimmed matrices.
# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # TNC <- trimHS_maxC(N, np_matrix, n, check.unique = TRUE)# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # TNC <- trimHS_maxC(N, np_matrix, n, check.unique = TRUE)
For N runs, it randomly chooses n associations and trims the H-S
association matrix to include them, allowing both single and multiple
associations.
trimHS_maxI(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)trimHS_maxI(N, HS, n, check.unique = TRUE, strat = "sequential", cl = 1)
N |
Number of runs. |
HS |
Host-Symbiont association matrix. |
n |
Number of associations. |
check.unique |
if |
strat |
Flag indicating whether execution is to be |
cl |
Number of cluster to be used for parallel computing.
|
A list of the N trimmed matrices.
# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # TNC <- trimHS_maxI(N, np_matrix, n, check.unique = TRUE)# data(nuc_cp) # N = 10 #for the example, we recommend 1e+4 value # n = 15 # TNC <- trimHS_maxI(N, np_matrix, n, check.unique = TRUE)