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. 2018 Aug 21;8:12524. doi: 10.1038/s41598-018-30831-w

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© The Author(s) 2018

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Representative alignment quality results regarding the effect of the dimensionality reduction technique on alignment quality for geometric networks, 10% noise, and cosine similarity under (a) WAVE and (b) SANA. For MCE/ncMCE, we indicate the number of dimensions out of those tested (2, 3, 5, 10, 15, 20, 25, 30, 40, 50) that gives the best results, i.e., the highest alignment quality. NoPCA corresponds to using no dimensionality reduction. For additional results, see Supplementary Figs S17–22.