TAMING THE BEAST

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Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. SCOTTI Tutorial: NEW Reconstruct transmission trees using within-host data with an approximate structured coalescent. 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. 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. BEAUti will recognize the sequences from the *.nexus file as nucleotide data. It will do so for sequence files with the character set of A C G T N, where N indicates an unknown nucleotide. As soon as other non-gap characters are included (e.g. using R or Y to indicate purines and pyramidines) BEAUti will not recognize the data as nucleotides anymore (unless the type of data is specified in the *.nexus file) and open a dialogue box to confirm the data type.

Note that sometimes a factor 2 is used for multiplying BFs, so when comparing BFs from different publications, be aware which definition was used. Get to know the advantages and disadvantages of the Coalescent Bayesian Skyline Plot and the Birth-Death Skyline.The parallel implementation makes it possible to run many particles in parallel, giving a many-particle estimate in the same time as a single particle estimate (PS/SS can be parallelised by steps as well). The output is written on screen, which I forgot to save. Can I estimate them directly from the log files? In practice, we can get away much smaller sub-chain lengths, which you can verify by running multiple NS analysis with increasing sub-chain lengths. If the ML and SD estimates do not substantially differ, you know the shorter sub-chain length was sufficient. How many particles do I need? The workshop organisers and participants outside of the London School of Hygiene and Tropical Medicine. The Coalescent Bayesian Skyline model allows N e N_e N e ​ to change over time in a nonparametric fashion (i.e. we do not have to specify an equation governing changes in N e N_e N e ​ over time). Another way to think about the model is as maximally-parameterized, since it infers d d d change-point times (segment boundaries) and a value for N e N_e N e ​ in each segment. This makes the Bayesian Skyline flexible enough to model very complicated N e N_e N e ​ dynamics, provided that enough segments are specified. There are two ways to save the analysis, it can either be saved as a *.pdf for display purposes or as a tab delimited file.

Marginal likelihood: -12417.389793288146 sqrt(H/N)=(1.9543337689486355)=?=SD=(1.9614418034828585) Information: 122.2214553744953 The difference between the estimates is the way they are estimated from the nested sampling run. Since these are estimates that require random sampling, they differ from one estimate to another. When the standard deviation is small, the estimates will be very close, but when the standard deviations is quite large, the ML estimates can substantially differ. Regardless, any of the reported estimates are valid estimates, but make sure to report them with their standard deviation. How do I know the sub-chain length is large enough? Navigate to Analysis > Bayesian Skyline Reconstruction. From there open the *.trees file. To get the correct dates in the analysis we should specify the Age of the youngest tip. In our case it is 1993, the year where all the samples were taken. If the sequences were sampled at different times (heterochronous data), the age of the youngest tip is the time when the most recent sample was collected. However, the only informative events used by the Coalescent Bayesian Skyline plot are the coalescent events. Thus, using a maximally-flexible parameterization with only one informative event per segment often leads to erratic and noisy estimates of N e N_e N e ​ over time (especially if segments are very short, see Figure 6). Grouping segments together leads to smoother and more robust estimates. We will be using R to analyze the output of the Birth-Death Skyline plot. RStudio provides a user-friendly graphical user interface to R that makes it easier to edit and run scripts. (It is not necessary to use RStudio for this tutorial).

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If we compare the estimates of the population dynamics using different dimensions, we see that most of the dynamics are already captured with having only 2 dimensions, as shown in Figure 13. Adding more dimensions only changes the inferred effective population size before 1900. Note that adding more dimensions adds a slight dip before the increase in the effective population size (around 1900). When comparing to the HPD intervals ( Figure 12) we see that this dip is not significant and may not be indicative of a real decrease in the effective population size before the subsequent increase. Figure 13: Estimated mean effective population sizes using different dimensions.

We also identified some issues with a few of the tutorials during the workshop and I’ll also be updating them soon as well. 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.Note that since BEAST 2.7 the filenames used here are the default filenames and should not need to be changed!)