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Proper orthogonal decomposition (POD) overcomes this difficulty by allowing us to consider separately the principal coordinates and the associated eigenvectors, characterizing the dominant spatio-temporal structures of the analyzed field.
This method of canonical correlation analysis was based on partitioning variation of the data into the principal coordinates, and then testing by permutation whether the variation was significant.
However, the absence of correlation corroborates the results obtained by the principal coordinates and grouping analysis, which indicated greater genetic similarity between spatially distant populations.
However, since both principal coordinates and factorial coordinates provided similar data, PCs were chosen as they are the most commonly used of the two in association mapping.
Population structure was taken into account by two different approaches: Principal Coordinates Analysis (PCO), which generates the P matrix (94 inbred lines x 5 Principal Coordinates), and the Bayesian method implemented in STRUCTURE software, which generates the Q matrix (94 inbred lines x membership coefficients to each of the 9 ideal populations detected) [ 39].
After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'.
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Principal coordinate and cluster analyses were performed to estimate the discriminatory ability of 20 EST-SSR markers to assess inter and intra species relationships.
We performed principal coordinate and admixture analyses to determine the ability of the array to detect and resolve population structure and investigated the extent of LD within a worldwide validation panel.
Principal coordinate and genetic distance analyses performed in the current study yielded greater separation between diploid and tetraploid taxa when using epigenetic information than when using only genetic information, suggesting that ploidy levels are better separated using epigenetic information than genetic information alone.
Bayesian analysis corroborated the principal coordinate and clustering analyses, in which the most likely number of groups (K = 5) shows that individuals are divided into populations according to the sampled sites, without the presence of putative hybrids or migrant individuals across populations.
EMPeror's command line interface accepts QIIME principal coordinates files and metadata mapping files, and produces an interactive 3D visualization that can be delivered in the context of a web page independent of the command line tool.
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