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. 2024 Jan 24;25:34. doi: 10.1186/s13059-024-03168-z

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© The Author(s) 2024

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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — Ratio-based metabolomics profiling improves quantitative data integration in long-term monitoring. a Qualitative concordance of metabolite identification. The numbers of metabolites detected in each batch of metabolomic datasets were shown. b, c Pearson correlation coefficients (PCCs) of pairs of technical replicates and of different Quartet samples were compared using quantitative profiles at the absolute abundance level (b) or ratio to D6 level (c). d, e Cross-batch data integration was visualized by hierarchical cluster analysis (HCA) at absolute abundance level (d) and ratio to D6 level (e). e, f Cross-batch data integration assessment using signal-to-noise ratio (SNR) by principal component analysis (PCA) at absolute abundance level (f) and ratio to D6 level (g)