Bayesian probability

Sociology

Dirichlet distribution

Phylogenetic tree

Population

Hierarchy

Mathematics

Latent Dirichlet allocation

Biology

Gene

Data mining

Demography

Mathematical analysis

Boundary value problem

Artificial intelligence

Economics

Topic model

Set (abstract data type)

Programming language

Computer science

Clade

Computational biology

Cluster analysis

Hierarchical clustering

Market economy

Genetics

Nucleic Acids Research

We present fastbaps, a fast solution to the genetic clustering problem. Fastbaps rapidly identifies an approximate fit to a Dirichlet process mixture model (DPM) for clustering multilocus genotype data. Our efficient model-based clustering approach is able to cluster datasets 10-100 times larger than the existing model-based methods, which we demonstrate by analyzing an alignment of over 110 000 sequences of HIV-1 pol genes. We also provide a method for rapidly partitioning an existing hierarchy in order to maximize the DPM model marginal likelihood, allowing us to split phylogenetic trees into clades and subclades using a population genomic model. Extensive tests on simulated data as well as a diverse set of real bacterial and viral datasets show that fastbaps provides comparable or improved solutions to previous model-based methods, while being significantly faster. The method is made freely available under an open source MIT licence as an easy to use R package at https://github.com/gtonkinhill/fastbaps.

Fast hierarchical Bayesian analysis of population structure