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An Algorithm for Deciding the Number of Clusters and Validation using Simulated Data with Application to Exploring Crop Population Structure

By Mark A Newell, The Samuel, Roberts Noble Foundation, Dianne Cook, Heike Hofmann and Jean-luc Jannink


A first step in exploring population structure in crop plants and other organisms is to define the number of subpopulations that exist for a given data set. The genetic marker data sets being generated have become increasingly large over time and commonly are of the high-dimension, low sample size (HDLSS) situation. An algorithm for deciding the number of clusters is proposed, and is validated on simulated data sets varying in both the level of structure and the number of clusters covering the range of variation observed empirically. The algorithm was then tested on six empirical data sets across three small grain species. The algorithm uses bootstrapping, three methods of clustering, and defines the optimum number of clusters based on a common criterion, the Hubert’s gamma statistic. Validation on simulated sets coupled with testing on empirical sets suggests that the algorithm can be used for a wide variety of genetic data sets

Topics: Cluster analysis, High dimensional, low sample size, Simulation, Genetic marker data, Visualization, Bootstrap, Dimension reduction
Year: 2013
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