Characterization Of A Bayesian Genetic Clustering Algorithm Based On A Dirichlet Process Prior And Comparison Among Bayesian Clustering Methods. - Info and Reading Options

The Internet Archive:

This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29" rel="ugc nofollow">BMC Bioinformatics</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29%20AND%20volume%3A%2812%29" rel="ugc nofollow">volume 12</a>.<h2>Abstract</h2>Background: A Bayesian approach based on a Dirichlet process (DP) prior is useful for inferring genetic population structures because it can infer the number of populations and the assignment of individuals simultaneously. However, the properties of the DP prior method are not well understood, and therefore, the use of this method is relatively uncommon. We characterized the DP prior method to increase its practical use. Results: First, we evaluated the usefulness of the sequentially-allocated merge-split (SAMS) sampler, which is a technique for improving the mixing of Markov chain Monte Carlo algorithms. Although this sampler has been implemented in a preceding program, HWLER, its effectiveness has not been investigated. We showed that this sampler was effective for population structure analysis. Implementation of this sampler was useful with regard to the accuracy of inference and computational time. Second, we examined the effect of a hyperparameter for the prior distribution of allele frequencies and showed that the specification of this parameter was important and could be resolved by considering the parameter as a variable. Third, we compared the DP prior method with other Bayesian clustering methods and showed that the DP prior method was suitable for data sets with unbalanced sample sizes among populations. In contrast, although current popular algorithms for population structure analysis, such as those implemented in STRUCTURE, were suitable for data sets with uniform sample sizes, inferences with these algorithms for unbalanced sample sizes tended to be less accurate than those with the DP prior method. Conclusions: The clustering method based on the DP prior was found to be useful because it can infer the number of populations and simultaneously assign individuals into populations, and it is suitable for data sets with unbalanced sample sizes among populations. Here we presented a novel program, DPART, that implements the SAMS sampler and can consider the hyperparameter for the prior distribution of allele frequencies to be a variable.

Search for “Characterization Of A Bayesian Genetic Clustering Algorithm Based On A Dirichlet Process Prior And Comparison Among Bayesian Clustering Methods.” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Characterization Of A Bayesian Genetic Clustering Algorithm Based On A Dirichlet Process Prior And Comparison Among Bayesian Clustering Methods.” in Libraries Near You:

Read or borrow “Characterization Of A Bayesian Genetic Clustering Algorithm Based On A Dirichlet Process Prior And Comparison Among Bayesian Clustering Methods.” from your local library.

• The WorldCat Libraries Catalog: Find a copy of “Characterization Of A Bayesian Genetic Clustering Algorithm Based On A Dirichlet Process Prior And Comparison Among Bayesian Clustering Methods.” at a library near you.