This vignette explains how to extract evolutionary rate parameters estimated from relaxed clock Bayesian inference analyses produced by BEAST2. It also shows how to use evolutionary rate based inference of selection mode (strength) adapted to clock-based rates, as introduced by Simões and Pierce (2021). See the sister vignette “Evolutionary Rates & Selection Mode (BEAST2)” for an equivalent workflow using output data produced by Mr. Bayes.
Load the EvoPhylo package
In this section, we will extract evolutionary rate parameters from each node from a Bayesian clock (time-calibrated) summary tree. The functions below will store them in a data frame, produce summary statistics tables, and create different plots showing how rates are distributed across morphological partitions and clades. Note that step 0 needs to be performed before running the inference when using BEAST2.
When using a default configuration generated through BEAUti, only one of the clocks will be logged along the tree. For the other clocks, only summary statistics (such as the mean rate) will be logged. In order to obtain the clock rates for different partitions, we need to modify the setup before running the inference.
In order to do this, open the BEAST2 XML configuration file and find the section which logs the tree, which should look similar to this:
logger id="treelog.t:26" spec="Logger" fileName="multiclock.trees" logEvery="10" mode="tree"> <log id="TreeWithMetaDataLogger.t:26" spec="beast.evolution.tree.TreeWithMetaDataLogger" branchratemodel="@RelaxedClock.c:26" tree="@Tree.t:26"/> <logger></
This section needs to be duplicated for each additional
clock, modifying the name of the clock (
the file name (
fileName) and the name of the logger
id) like in this example:
logger id="treelog.t:26.2" spec="Logger" fileName="multiclock.clock2.trees" logEvery="10" mode="tree"> <log id="TreeWithMetaDataLogger.t:26" spec="beast.evolution.tree.TreeWithMetaDataLogger" branchratemodel="@ExponentialRelaxedClock.c:261" tree="@Tree.t:26"/> <logger></
The clock name needs to reference the name (
id) of one
of the clock models set up in the analysis, which can be found earlier
in the XML file.
Here we assume that a consensus or summary tree has been generated
for each of the tree log files generated by the inference configured in
step 0, for instance by using the software TreeAnnotator. First, import
these summary clock trees using
## Import all clock summary trees (.tre, .t, or .tree) produced by BEAST2 from your local directory <- treeio::read.beast("tree_clock1.tre") tree_clock1 <- treeio::read.beast("tree_clock2.tre") tree_clock2 #etc
Below, we use the example BEAST2 clock trees from 3 morphological
partitions and one molecular clock partition that accompany
<- system.file("extdata", "Penguins_MCC_morpho_part1.t", package = "EvoPhylo") tree_clock1 <- system.file("extdata", "Penguins_MCC_morpho_part2.t", package = "EvoPhylo") tree_clock2 <- system.file("extdata", "Penguins_MCC_morpho_part3.t", package = "EvoPhylo") tree_clock3 <- system.file("extdata", "Penguins_MCC_dna.t", package = "EvoPhylo") tree_clock4 <- treeio::read.beast(tree_clock1) tree_clock1 <- treeio::read.beast(tree_clock2) tree_clock2 <- treeio::read.beast(tree_clock3) tree_clock3 <- treeio::read.beast(tree_clock4)tree_clock4
can extract mean or median rate values for each node in the summary
tree. These mean or median rate values are calculated by TreeAnnotator
taking into account all trees from the posterior sample. Please note
that analyses must have reached the stationarity phase and independent
runs converging for the summary statistics in each node to be meaningful
summaries of the posterior sample.
The example shown here uses two different clocks, however the function supports any number of clocks which can all be passed as separate arguments.
## Get table of clock rates with summary stats for each node in ## the tree for each relaxed clock partition (4 partitions in this dataset) <- get_clockrate_table_BEAST2(tree_clock1, tree_clock2, tree_clock3, tree_clock4, summary = "median") RateTable_Medians_4p <- get_clockrate_table_BEAST2(tree_clock1, tree_clock2, tree_clock3, tree_clock4, summary = "mean")RateTable_Means_4p
Once a rate table has been obtained from BEAST2 files, it is necessary to export it. This is a necessary step to subsequently open the rate table spreadsheet locally (e.g., using Microsoft Office Excel) and customize the table with clade names associated with with each node in the tree for downstream analyses. Note that the root node may include “NA” for rate value, so it must be removed from the rate table.
## Export the rate tables write.csv(RateTable_Means_4p, file = "RateTable_Means_4p.csv") write.csv(RateTable_Medians_4p, file = "RateTable_Medians_4p.csv")
To visualize the node values in the tree, you can use
ggtree(). This can be done on any of the imported trees, as
all BEAST2 Summary trees should have the same topology and divergence
times, differing only on rate parameters.
## Plot tree node labels library(ggtree) <- ggtree(tree_clock1, branch.length = "none", size = 0.05) + tree_nodes geom_tiplab(size = 2, linesize = 0.01, color = "black", offset = 0.5) + geom_label(aes(label = node), size = 2, color="purple") tree_nodes
## Save your plot to your working directory as a PDF ::ggsave("Tree_nodes.pdf", width = 10, height = 10)ggplot2
A new “clade” column has been added to the rates table. Below, we use
the rate table with clade membership
RateTable_Means_4p_Clades that accompanies
EvoPhylo (clade names in this example are for demonstration
## Import rate table with clade membership (new "clade" column added) ## from your local directory <- system.file("extdata", "RateTable_Medians_Clades.csv", package = "EvoPhylo") RateTable_Medians_Clades <- read.csv(RateTable_Medians_Clades, header = TRUE) RateTable_Medians_Clades head(RateTable_Medians_Clades, 5) ## clade nodes rates1 rates2 rates3 rates4 ## 1 EarlyStemPenguins 1 0.01059049 0.01142373 0.04521063 0.03214791 ## 2 EarlyStemPenguins 2 0.01127067 0.01117188 0.04558499 0.03297338 ## 3 EarlyStemPenguins 58 0.01252320 0.01143236 0.04487146 0.03119615 ## 4 CrownPenguins 3 0.01493279 0.01009941 0.04626338 0.04521892 ## 5 CrownPenguins 4 0.01050220 0.01008585 0.04537996 0.03908424
Obtain summary statistics table and plots for each clade by clock
clockrate_summary(). Supplying a file path
file save the output to that file.
## Get summary statistics table for each clade by clock clockrate_summary(RateTable_Medians_Clades, file = "Sum_RateTable_Medians_4p.csv")
Plot distributions of rates by clock partition and clade with
## Overlapping plots clockrate_dens_plot(RateTable_Medians_Clades, stack = FALSE, nrow = 1, scales = "fixed")
Sometimes using stacked plots provides a better visualization as it avoids overlapping distributions.
## Stacked plots clockrate_dens_plot(RateTable_Medians_Clades, stack = TRUE, nrow = 1, scales = "fixed")
It is also possible to append extra layers using
function, such as for changing the color scale. Below, we change the
color scale to be the Viridis scale.
## Stacked plots with viridis color scale clockrate_dens_plot(RateTable_Medians_Clades, stack = TRUE, nrow = 1, scales = "fixed") + ::scale_color_viridis_d() + ggplot2::scale_fill_viridis_d() ggplot2
We can also plot linear model regressions between rates from two or
more clocks with
## Plot regressions of rates from multiple clocks #Morpho-morpho rates <- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 1, clock_y = 2) p1<- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 1, clock_y = 3) p2<- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 2, clock_y = 3) p3 #Morpho-Mol rates <- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 1, clock_y = 4) p4<- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 2, clock_y = 4) p5<- clockrate_reg_plot(RateTable_Medians_Clades, clock_x = 3, clock_y = 4) p6 library(patchwork) #for combining plots + p2 + p3 + p4 + p5 + p6 + plot_layout(nrow = 2)p1
## Save your plot to your working directory as a PDF ::ggsave("Plot_regs.pdf", width = 8, height = 8)ggplot2
In this section, we will use evolutionary rate based inference of selection mode, as first introduced by Baker et al. (2016) for continuous traits, and later adapted to clock-based rates by Simões and Pierce (2021).
Although the dataset used in the example here includes both morphological and molecular partitions, we will focus the selection mode inference using morphological partitions only. There are multiple available methods to infer the strength of selection using molecular data (e.g., dn/ds ratio) that are better suited to the intrinsic properties of molecular data, or which take into account additional variables available for extant taxa (generation times), and we refer users to those approaches.
The combined posterior log file from BEAST2
is outputted by LogCombiner from their software
package. Our own function to combined log files
is intended to work with Mr. Bayes posterior
When using more two or more clock partitions, posterior log files
from BEAST2 will have the mean evolutionary rates for each partition
labeled as “rate
Evophylo, users must ensure that all clock
partitions that wish to be analyzed should include the word “part” just
before the partition number in this label, as outputted by the
write_partitioned_alignments. So, this label should read
write_partitioned_alignments as a Nexus
file labeled “KairukuEA2015_Morpho_3p_part1”, and so the mean
evolutionary rates for the first partition is labeled
Below, we use the posterior dataset “Penguins_log.log” that
<- system.file("extdata", "Penguins_log.log", package = "EvoPhylo") posterior <- read.table(posterior, header = TRUE) posterior ## Show first 10 lines of combined log file head(posterior, 10)
###2. Check background rates distribution and if they need transformation
The output includes histograms showing the data distribution before and after data transformation for comparisons.
Using different thresholds, Identify the selection mode across
branches in the tree for each clock partition with
Users must indicate the type of output file (between Mr. Bayes and BEAST2) and whether they would like
to log transform the background rate to meet assumptions of normally
distributed data, based on the results obtained from
plot_back_rates. Users should also indicate in “clock” the
number of the clock partition they would like to plot rates from and the
desired significance threshold to interpret branch rates (we recommend
number of standard deviations around the mean of background
rates).Finally, a series of arguments enable users to customize the
geological timescale to add to the tree.
## Plot tree using various thresholds for clock partition 1 <-plot_treerates_sgn( A1type = "BEAST2", trans = "log10", #Indicates software name output and type of transformation #Calls tree for clock partition 1 and posterior log file tree_clock1, posterior, clock = 1, #Use background rates for clock partition 1 threshold = c("1 SD", "3 SD"), #sets threshold for selection mode branch_size = 1.5, tip_size = 3, #sets size for tree elements xlim = c(-70, 30), nbreaks = 8, geo_size = list(3, 3)) #sets limits and breaks for geoscale A1
Plot tree using various thresholds for the other clock partitions and combine them.
## Plot tree using various thresholds for other clock partition and combine them <-plot_treerates_sgn( A2type = "BEAST2", trans = "log10", #Indicates software name output and type of transformation #Calls tree for clock partition 2 and posterior log file tree_clock2, posterior, clock = 2, #Use background rates for clock partition 3 threshold = c("1 SD", "3 SD"), #sets threshold for selection mode branch_size = 1.5, tip_size = 3, #sets size for tree elements xlim = c(-70, 30), nbreaks = 8, geo_size = list(3, 3)) #sets limits and breaks for geoscale <-plot_treerates_sgn( A3type = "BEAST2", trans = "log10", #Indicates software name output and type of transformation #Calls tree for clock partition 3 and posterior log file tree_clock3, posterior, clock = 3, #Use background rates for clock partition 3 threshold = c("1 SD", "3 SD"), #sets threshold for selection mode branch_size = 1.5, tip_size = 3, #sets size for tree elements xlim = c(-70, 30), nbreaks = 8, geo_size = list(3, 3)) #sets limits and breaks for geoscale library(patchwork) + A2 + A3 + plot_layout(nrow = 1)A1
## Save your plot to your working directory as a PDF ggplot2::ggsave("Tree_Sel_Morpho_3p.pdf", width = 18, height = 8)
get_pwt_rates_BEAST2() complements the
plot_treerates_sgn by producing a table of
pairwise t-tests for differences between the mean background rate in the
posterior and the absolute rate for each summary tree branches Should be
used only for normally distributed data in which a CI=0.95 is considered
a good threshold. In many cases, however, using multiple standard
deviations as outputted using
plot_treerates_sgn provides a
more robust test of whether branch rates are significantly different
from background rates.
4.1. Import rate table with clade membership (new “clade” column added) from your local directory with “mean” values
Below, we use the rate table with clade membership
“RateTable_Means_Clades.csv” that accompanies
## Import rate table with clade membership (new "clade" column added) ## from your local directory with "mean" values <- system.file("extdata", "RateTable_Means_Clades.csv", package = "EvoPhylo") RateTable_Means_Clades <- read.csv(RateTable_Means_Clades, header = TRUE) RateTable_Means_Clades head(RateTable_Means_Clades, 5) ## clade nodes rates1 rates2 rates3 rates4 ## 1 EarlyStemPenguins 1 0.01680336 0.01217294 0.04894430 0.03657966 ## 2 EarlyStemPenguins 2 0.01782966 0.01181350 0.04985747 0.03746188 ## 3 EarlyStemPenguins 58 0.02404947 0.01263853 0.04995735 0.03741211 ## 4 CrownPenguins 3 0.01913400 0.01017825 0.05145375 0.04892464 ## 5 CrownPenguins 4 0.01319288 0.01016964 0.04996688 0.04325669
4.2. Get and export table of pairwise t-tests
## Get table of pairwise t-tests for difference between the posterior ## mean and the rate for each tree node <- get_pwt_rates_BEAST2(RateTable_Means_Clades, posterior)RateSign_Tests
## Export the table write.csv(RateSign_Tests, file = "RateSign_Tests.csv")