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. 2022 Aug 29;12:14683. doi: 10.1038/s41598-022-14395-4

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Applying PCA to four color populations. (A) An illustration of the PCA procedure (using the singular value decomposition (SVD) approach) applied to a color dataset consisting of four colors (n_All = 1). (B) A 3D plot of the original color dataset with the axes representing the primary colors, each color is represented by three numbers (“SNPs”). After PCA is applied to this dataset, the projections of color samples or populations (in their original color) are plotted along their first two eigenvectors (or principal components [PCs]) with (C) n_All = 1, (D) n_All = 100, and (E) n_All = 10,000. The latter two results are identical to those of (C). Grey lines and labels mark the Euclidean distances between the color populations calculated across all three PCs.