The values of the parameters are expected to differ depending on the dataset, sampling method and the classification algorithm.
It looks for the most possible distribution pattern of SFS under the constraints of given demographic parameter set that fits the real dataset sampled from the population.
The third reason was the enlargement of the dataset sample to its current total of 42 individuals.
3. A) The smoothness of the distribution is ultimately dependent on the dataset sample size, classifier error, number of splits, and possibly other factors.
The dataset sampler accepts the following three parameters as input: (1) Approximate number of samples (denoted as N) (2) Number of cancer types (denoted as C) (3) Similarity range (denoted as [δmin,δmax]) between samples belonging to different cancer types.
This parameter can vary between 1 and n − 1 neighbors, where n is the dataset sample size, and is generally used to infer local and/or global manifold topology.
The analyses were run for one million generations for the bacterial dataset and 0.5 million generations for the eukaryotic dataset, sampled every 100 generations.
The second dataset, sampled seven months later (herein t7), was obtained from phage as previously described [20].
Finally we ran the analysis on an empty dataset, sampling from the prior distribution to evaluate the influence of the priors on the posterior distribution estimates [ 103, 104].
Partitioning the dataset by sampling sites also had a dramatic effect on biodiversity analyses (Table 2).
