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. 2020 Nov 2;6:31. doi: 10.1038/s41526-020-00121-9

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© The Author(s) 2020

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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Fig. 1 — a Principal component analysis (PCA) highlights microRNA patterns observed in subjects at baseline (BL), 1 and 24 h after parabolic flight. Data from 213 circulating microRNAs detected in all 24 samples by small RNAseq was used (regularized log tags per million data). The first and second principal component explain 20 and 14% of the variance, respectively. b Expression heatmap with unit variance scaling applied to rows (microRNAs). Both rows and columns are clustered using correlation distance and average linkage. Pearson correlation was used as correlation distance. c Expression heatmap with unit variance scaling applied to rows (microRNAs). Only rows are clustered using Pearson correlation as measure of distance and average linkage.