For the selected values of K, we assessed the average proportion of membership of the samples to the inferred clusters (PMIs) by combining the 10 replications using CLUMPP (Jakobsson and Rosenberg, 2007), applying the LargeKGreedy algorithm.
Within the final three-class model, the probabilities of class membership for the sample were 24%% for the first class, 41 % for the second class, and 35%% for the third class.
In each iteration, our online algorithm samples a node and estimates its cluster memberships only by its adjacency links, and model parameters are then estimated by the memberships of the sampled node and old model parameters obtained in the previous iteration.
If the population memberships of the samples are known, estimation of the variance-covariance parameters is straightforward.
To further validate the selected SNPs as signatures of population structure, we study the performance of predicting the continental memberships of the samples using the panel of most informative SNPs.
To compare the informativeness of selected SNPs with the PCA approach, we split the HGDP dataset equally into a training set and a testing set, and use the standard Support Vector Machine (SVM) [20] [22] to predict the continental memberships of the samples (see Results for details).
The weight represents the membership of the -th sample to the -th cluster.
where the element u i,k) of fuzzy partition matrix is the membership of the kth sample corresponding to the center v i of ith class, u i,k)∈[0,1] and (sum _{i=1}^{c}u i,k)=1), q is the weighted exponent on each fuzzy membership and q∈(1,∞).
Each element in the class vector simply indicates class membership of the corresponding sample.
Because the value of partition matrix represents the membership of a sample belonging to a class, we can directly use the membership value of partition matrix as the BPA or mass function of D-S theory.
