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These preliminary data sets were subjected to the multidimensional clustering approach employed in the principal coordinates analysis with modal clustering (PCO-MC) workflow (Reeves and Richards, 2009).
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The similarity matrix was also used for principal coordinate analysis with the DCenter, Eigen, and MXPlot subprograms in NTSYS-pc.
Principal coordinate analysis with Bray-Curtis distances demonstrated that subjects form distinct clusters, irrespective of health or disease status.
Principal coordinate analysis with all 29 variables (continuous and categorical) depicted effective segregation of a majority of aromatic and non-aromatic landraces [ see Supporting Information Fig. S2 ].
File 3. Figure S2: Principal coordinate analysis with all characters of 414 landraces showing separation among landraces and within aromatic landraces.
Nei's unbiased genetic distances estimated between pairs of populations were used for visualizing cluster analysis and for principal coordinates analysis (PCoA) with the help of Biodiversity R developed for the R statistical software (http://www.Rproject.org) [ 38].
Geographic groupings of samples were visualized with principal coordinates analysis (PCoA) computed with NTSYS (E xeter Software; Exeter, New York, NY, USA).
Principal coordinates analysis was carried out with R [ 17].
Calculation of Nei's genetic distance (DA) and principal coordinates analysis (PCoA) were performed with NTSYSpc 2.10 [ 22].
Both weighted and unweighted distance matrices were calculated and visualized with Principal coordinates analysis (PcoA) plots (http://metadp.cn:7001/metadp/F9/weighted_PCoA.html and http://metadp.cn:7001/metadp/F10/unweighted_PCoA.html).
Variation in composition among treatments was assessed with Principal coordinates analysis (PCO), using the cmdscale function in the R base package and wascores function in vegan.
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