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. 2022 Nov 7;12:18877. doi: 10.1038/s41598-022-23241-6

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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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Graphical representation of the canonical correlation analysis. Each oval box represents a significant correlation (r) between linear combinations of PCs from two data set, with lines indicating the PCs from each data set significantly contributing to the combination. Thick lines indicate prominent contributions, i.e. the PC with a coefficient at least 1.5 times greater in modulus than that of any other PC in the same combination. PC box size is proportional to the variance explained by that PC within the dataset. ****p < 0.0001; ***0.0001 ≤ p < 0.001; **0.001 ≤ p < 0.01; *0.01 ≤ p < 0.05. Dashed lines indicate not statistically significant (0.05 < p < 0.15) though very strong (r > 0.7) correlations.