The goal of PCMFit is to provide a generic tool for inference and selection of phylogenetic comparative models (PCMs). Currently, the package implements Gaussian and mixed Gaussian phylogenetic models (MGPM) over all tree types (including non-ultrametric and polytomic trees). The package supports non-existing traits or missing measurements for some of the traits on some of the species. The package supports specifying measurement error associated with each tip of the tree or inferring a measurement error parameter for a group of tips. The Gaussian phylogenetic models include various parametrizations of Brownian motion (BM) and Ornstein-Uhlenbeck (OU) multivariate branching processes. The mixed Gaussian models represent models with shifts in the model parameters as well as the type of model at points of the tree. Each shift-point is described as a pair of a shift-node and associated type of model (e.g. OU or BM) driving the trait evolution from the beginning of the branch leading to the shift-node toward the shift-node and its descendants until reaching a tip or another shift-point. The function PCMFit is used to fit a given PCM or a MGPM for a given tree with specified shift-points. The function PCMFitMixed is used to fit an ensemble of possible MGPMs over a tree for which the shift-points are unknown. This function can perform model selection of the best MGPM for a given tree and data according to an information loss function such as the Akaike information criterion (AIC). The package has been thoroughly tested and applied to real data in the related research article (Mitov, Bartoszek, and Stadler 2019) <doi:10.1073/pnas.1813823116>. Currently, the package is available from https://github.com/venelin/PCMFit. The web-page https://venelin.github.io/PCMFit/ provides access to documentation and related resources.
Before installing PCMFit, it is necessary to ensure that several R-packages are installed or can be installed from CRAN. These are listed below:
install.packages("PCMBase")
[PCMBaseCpp]. This package contains Rcpp modules for likelihood calculation of the model types implemented in PCMBase. PCMBaseCpp can be used as a companion and not a substitute of PCMBase. The sole purpose of PCMBaseCpp is to accelerate the likelihood calculation by implementing the most computationally intensive algorithm in C++, which has shown a dramatic speed-up (in the order of 100 times) (Mitov et al. 2018). Hence, installing this package is optional but highly recommended, in particular, if the goal is to infer models with shifts and/or to infer models on trees bigger than 100 tips. Installing PCMBaseCpp requires a C++ compiler to be installed on the system. This installation has been tested on two systems:
.bashrc
):module load interconnect/ethernet
module load new gcc/6.3.0 open_mpi/1.6.5 r/3.4.0 intel/2017.5
xcode-select --install
Upon validating the availability of a C++ compiler, PCMBaseCpp can be installed using the commands:
# These two packages are available on CRAN
install.packages("Rcpp")
install.packages("RcppArmadillo")
# At the time of writing, PCMBaseCpp is available only from github.
devtools::install_github("venelin/PCMBaseCpp")
other third party dependencies include the packages
data.table
,
foreach
,
iterators
,
ape
and
digest
. These
packages should be installed automatically from CRAN when installing
PCMFit. If this does not happen, consult the packages’ web-pages
(links above).
An optional but highly recommended dependency. The function
PCMTreePlot
in the package PCMBase uses the R-package ggtree,
which is not on CRAN. It is highly recommended to install this
package in order to be able to visualize trees with colored parts
corresponding to defferent evolutionary regimes. If ggtree
is not
installed, the figures in the vignettes and coding examples cannot
be generated. At the time of writing this documentation, ggtree
can be installed from bioconductor through the following code (if
that does not work, check the ggtree home
page):
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ggtree", version = "3.8")
Currently the package can be installed from github using the command:
devtools::install_github("venelin/PCMFit")
Publishing PCMFit on CRAN is planned after release of the first stable and documented version.
Currently PCMFit implements parallel execution for the inference of
mixed Gaussian phylogenetic models with unknown shifts. This is optional
but highly recommended. To enable parallel execution, it is necessary to
run PCMFit on a computer equipped with a multiple core processor or on
multiple node computing cluster. In its current implementation, PCMFit
uses the function %dopar%
from the R-package
foreach
to parallelize
the execution of (nested) foreach
loops. This parallelization has been
tested using two parallel backends for the %dopar%
function:
doMPI
and
Rmpi
on a multiple node
cluster with open_mpi/1.6.5
installed. In particular, MGPM
inference has been run using up to 250 cores on the ETH scientific
computing cluster Euler.doParallel
on a
MacBook Pro (Retina, 15-inch, Late 2013), 2.3 GHz Intel Core i7
processor (4 physical cores, 8 logical cores), running macOS Sierra
10.12.6.To install the above packages, follow the most recent instructions in their documentation (links to the packages web-pages provided above). Once you have installed the parallel backend of choice, you can paste/edit the following code snippet in the beginning of the R-script for running PCMFit model inference:
library(PCMBase)
library(PCMBaseCpp)
library(PCMFit)
# other needed packages, e.g. ape, data.table etc...
# extract dataset identifier and possibly other parameters from the command line:
args <- commandArgs(trailingOnly = TRUE)
if(length(args) > 0) {
data_id <- as.integer(args[1])
} else {
data_id <- 1L
}
# A character string used in filenames for a model inference on a given data:
prefixFiles = paste0("MGPM_A_F_BC2_RR_DATAID_", data_id)
# creating the cluster for this PCMFit run:
if(!exists("cluster") || is.null(cluster)) {
if(require(doMPI)) {
# using MPI cluster as distributed node cluster (possibly running on a
# cluster of multiple nodes)
# Get the number of cores. Assume this is run in a batch job.
p = strtoi(Sys.getenv('LSB_DJOB_NUMPROC'))
cluster <- startMPIcluster(count = p-1, verbose = TRUE)
doMPI::registerDoMPI(cluster)
} else {
# possibly running on personal computer without mpi installation
cluster <- parallel::makeCluster(
parallel::detectCores(logical = TRUE),
outfile = paste0("log_", prefixFiles, ".txt"))
doParallel::registerDoParallel(cluster)
}
}
Finally, to tell PCMFit that it should run the inference in parallel,
specify the argument doParallel=TRUE
in calls to the function
PCMFitMixed
. A full example for this is provided in the user guide
Inferring an MGPM with Unknown
Shifts.
A brief historical background and theoretical overview of PCMs can be found in Chapter 1 of (Mitov 2018a). A more general introduction can be found in (Harmon 2018). The research article “Automatic Generation of Evolutionary Hypotheses using Mixed Gaussian Phylogenetic Models” provides a general introduction to MGPMs and reports a real data example and a simulation based comparison of MGPMs versus other implementations of phylogenetic comparative models with shifts. The article is currently undergoing peer review for a publication.
The user guides and technical reference for the library are available on the PCMFit web-page:
Note: The writing of the user gudes and help articles for this package is in progress. Please, contact the author for assistance, in case you need to use PCMFit right away and need help with the coding examples. Thanks for your understanding.
PCMFit()
to infer such
models on a given tree and trait data.PCMFitMixed()
to select the
best MGPM for a given tree and trait data, based on an information
loss function such as the Akaike information criterion (AIC) (in
preparation).The PCMFit source code is located in the PCMFit github repository.
Feature requests, bugs, etc can be reported in the PCMFit issues list.
PCMFit builds on top of a stack of three tools enabling fast likelihood calculation and simulation of MGPMs:
To acknowledge the PCMFit package in a publication or other presentation, please cite:
Mitov, V., Bartoszek, K., & Stadler, T. (2019). Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models. Proceedings of the National Academy of Sciences of the United States of America, http://doi.org/10.1073/pnas.1813823116.
@article{Mitov:2019agh,
title = {Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models},
author = {Venelin Mitov and Krzysztof Bartoszek and Tanja Stadler},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2019},
url = {https://www.pnas.org/lookup/doi/10.1073/pnas.1813823116},
}
Although, I have been consistent in my effort to update the following list with any new package I have used in developing and testing PCMFit, chances are that I have omitted some of these tools. I apologise to their authors.
The PCMFit R-package uses the following 3rd party R-packages: