phylodyn

The purpose of phylodyn is to facilitate phylodynamic inference and analysis in an approachable R package.

Installation

1. Install (if necessary) package dependencies and helpers ape, spam and devtools using install.packages.

2. Install INLA using install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE) or check r-inla.org for the most up-to-date installation instructions.

3. Load devtools using library(devtools).

4. Install phylodyn using

   a. install_github("mdkarcher/phylodyn"), or

   b. install_github("mdkarcher/phylodyn", build_vignettes = TRUE) if you want some illustrative vignettes (note: using build_vignettes = TRUE will make the install take longer).

Vignettes

• SimpleBNPR: A short example showing how to use BNPR and BNPR-PS on simulated data, illustraring methodology in [2] and [5].

• CovariatesBNPR: A short example showing how to use covariates within BNPR-PS on simulated data, illustraring methodology in [8].

• NewYorkInfluenza: A case study analyzing influenza data from New York, reproducing analysis in [5] on data from [1].

• RegionalInfluenza: A case study analyzing influenza data from nine geographic regions, reproducing analsyis in [5] on data from [3].

• RegionalSeasonality: A case study analyzing influenza seasonality from nine geographic regions, reproducing analsyis in [5] on data from [3].

• SimplePhyloinfer: A short example comparing BNPR with a split HMC MCMC sampler approach, illustrating methodology in [4].

• LongPhyloinfer: A longer example comparing BNPR with multiple MCMC samplers, including split HMC as in SimplePhyloinfer, illustrating methodology in [4].

• LocalGenealogies: A short example of MCMC-based inference of effective population size trajectories from a sequence of local genealogies. Genealogies are assumed to be a realization of the Sequentially Markov Coalescent (SMC') model. The methodology is developed in [6]

• LocalGenealogiesSimulation: A short example of simulation of genealogies under isochronous and heterochronous sampling schemes, illustraring methodology in [2] and [5].

Datasets

Datasets below can be found at: https://github.com/mdkarcher/PhyloData/

1. New York influenza BEAST XML for inferring genealogy using sequence data from [1].

   • NewYork.xml

2. Regional influenza BEAST XML for inferring genealogy using sequence data from [3].

   • Europe.xml
   • India.xml
   • JapanKorea.xml
   • NorthChina.xml
   • Oceania.xml
   • SouthAmerica.xml
   • SouthChina.xml
   • SoutheastAsia.xml
   • USACanada.xml

References

1. A. Rambaut, O. G. Pybus, M. I. Nelson, C. Viboud, J. K. Taubenberger, E. C. Holmes The genomic and epidemiological dynamics of human influenza A virus. Nature, 453(7195): 615–619, 2008.

2. J. A. Palacios and V. N. Minin. Integrated nested Laplace approximation for Bayesian nonparametric phylodynamics. In Proceedings of the Twenty-Eighth International Conference on Uncertainty in Artificial Intelligence, pages 726–735, 2012.

3. D. Zinder, T. Bedford, E. B. Baskerville, R. J. Woods, M. Roy, M. Pascual. Seasonality in the migration and establishment of H3N2 Influenza lineages with epidemic growth and decline. BMC Evolutionary Biology, 14(1): 272, 2014.

4. S. Lan, J. A. Palacios, M. Karcher, V. N. Minin, and B. Shahbaba. An Efficient Bayesian Inference Framework for Coalescent-Based Nonparametric Phylodynamics, Bioinformatics, 31(20): 3282-3289, 2015.

5. M. D. Karcher, J. A. Palacios, T. Bedford, M. A. Suchard, and V. N. Minin. Quantifying and mitigating the effect of preferential sampling on phylodynamic inference. PLOS Computational Biology, 12:e1004789, 2016.

6. J.A. Palacios, J. Wakeley, and S. Ramachandran. Bayesian nonparametric inference of population size changes from sequential genealogies. Genetics Vol. 201:281-304, 2015.

7. M. Karcher M, J.A. Palacios, S. Lan, V.N. Minin. phylodyn: an R package for phylodynamic simulation and inference, Molecular Ecology Resources, 17, 96-100, 2017.

8. Karcher MD, Suchard MA, Dudas G, Minin VN. Estimating effective population size changes from preferentially sampled genetic sequences, PLOS Computational Biology, 16: e1007774, 2020.