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Clumpak / Clustering Markov Packager Across K
Automates the post-processing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.
Provides a general method for visualizing estimated membership coefficients. Subpopulations are represented as colours, and individuals are depicted as bars partitioned into coloured segments that correspond to membership coefficients in the subgroups. Distruct can also be used to display subpopulation assignment probabilities when individuals are assumed to have ancestry in only one group. Various options enable the user to control left-to-right printing order of populations, bottom-to-top printing order of clusters, colors, and other graphical details.
A freely available software package for post-processing output from clustering inference using population genetic data. pong combines a network-graphical approach for analyzing and visualizing membership in latent clusters with an interactive D3.js-based visualization. pong outpaces current solutions by more than an order of magnitude in runtime while providing a user-friendly, interactive visualization of population structure that is more accurate than those produced by current tools. Thus, pong enables unprecedented levels of scale and accuracy in the analysis of population structure from multilocus genotype data.
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