TAMING THE BEAST

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This depends on many things, but in general, depends on how accurate the estimates should be. For NS, we get an estimate of the SD, which is not available for PS/SS. If the hypotheses have very large differences in MLs, NS requires very few (maybe just 1) particle, and will be very fast. If differences are smaller, more particles may be required, and the run-time of NS is linear in the number of particles.

The Coalescent Bayesian Skyline divides the time between the present and the root of the tree (the tMRCA) into segments, and estimates a different effective population size ( N e N_e N e ​ ) for each segment. The endpoints of segments are tied to the branching times (also called coalescent events) in the tree ( Figure 6), and the size of segments is measured in the number of coalescent events included in each segment. The Coalescent Bayesian Skyline groups coalescent events into segments and jointly estimates the N e N_e N e ​ ( bPopSizes parameter in BEAST) and the size of each segment ( bGroupSizes parameter). To set the number of segments we have to change the dimension of bPopSizes and bGroupSizes (note that the dimension of both parameters always has to be the same). Note that the length of a segment is not fixed, but dependent on the timing of coalescent events in the tree ( Figure 6), as well as the number of events contained within a segment ( bGroupSizes). Figure 6: Example tree where the red dotted lines show the time-points of coalescent events. If the difference is smaller, you can guess how much the SD estimates must shrink to get a difference that is sufficiently large. Since the SD=sqrt(H/N), we have that N=H/(SD*SD) and H comes from the NS run with a few particles. Run the analysis again, with the increased number of particles, and see if the difference becomes large enough. Get to know the advantages and disadvantages of the Coalescent Bayesian Skyline Plot and the Birth-Death Skyline.Marginal likelihood: -12426.207750474812 sqrt(H/N)=(1.8913059067381148)=?=SD=(1.8374367294317693) Information: 114.46521705159945 An NS analysis produces two trace log files: one for the nested sampling run (say myFile.log) and one with the posterior sample ( myFile.posterior.log).

The third edition of Taming the BEAST took place last week at the London School of Hygiene and Tropical Medicine. I had a lot of fun at the workshop in London and also learned a lot. Thanks to a great bunch of participants who came out to learn about BEAST2 with us, I hope you enjoyed the workshop! If the difference is less than 2, the hypotheses may not be distinguishable – in terms of Bayes factors, are barely worth mentioning. Is NS faster than path sampling/stepping stone (PS/SS)?

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Estimates of N e N_e N e ​ therefore do not directly tell us something about the number of infected, nor the transmission rate. However, changes in N e N_e N e ​ can be informative about changes in the transmission rate or the number of infected (if they do not cancel out).

It may be tempting to specify the maximum dimension for the model (each group contains only one coalescent event, thus N e N_e N e ​ changes at each branching time in the tree), making it as flexible as possible. This is the parameterization used by the Classic Skyline plot (Pybus et al., 2000), which is the direct ancestor of the Coalescent Bayesian Skyline plot. Marginal likelihood: -12428.557546706481 sqrt(H/N)=(11.22272275528845)=?=SD=(11.252847709777592) Information: 125.94950604206919 Set the dimension of bPopSizes and bGroupSizes to 4 (the default value is 5) after expanding the boxes for the two parameters ( Figure 8). Figure 7: Show the initialization panel. With an estimated 15-25%, Egypt has the highest Hepatits C prevalence in the world. In the mid 20th century, the prevalence of Hepatitis C increased drastically (see Figure 1 for estimates). We will try to infer this increase from sequence data. Once the analyses have run, open the log file in Tracer and compare estimates and see whether the analyses substantially differ. You can also compare the trees in DensiTree.

Choosing the dimension for the Bayesian Skyline can be rather arbitrary. If the dimension is chosen too low, not all population size changes are captured, but if it is chosen too large, there may be too little information in a segment to support a robust estimate. When trying to decide if the dimension is appropriate it may be useful to consider the average number of informative (coalescent) events per segment. (A tree of n n n taxa has n − 1 n-1 n − 1 coalescences, thus N e N_e N e ​ in each segment is estimated from on average n − 1 d \frac{n-1}{d} d n − 1 ​ informative data points). Would this number of random samples drawn from a hypothetical distribution allow you to accurately estimate the distribution? If not, consider decreasing the dimension. It has already been more than two weeks since the second Taming the BEAST workshop took place on Waiheke island in New Zealand.