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. 2023 Sep 7;24:201. doi: 10.1186/s13059-023-03047-z

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© The Author(s) 2023

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. 6 — Summary of performances of BECAs and choice of number of samples for ratio-based scaling. a The summarized performance of seven BECAs in balanced and confounded scenarios. The BECAs were ordered by their total score. For the calculation of the total score, we first separately scaled the values of the four metrics, including signal-to-noise ratio (SNR), Matthews Correlation Coefficient (MCC) of identification of differentially expressed features (DEFs), MCC of prediction, and Adjusted Rand Index (ARI) of multiomics clustering, to an interval of (0,1) to equalize the weight of different metrics. The total score was expressed as mean of the scaled values of the four metrics. b Boxplot of SNR under different numbers of replicates of the reference sample used as dominators in conducting ratio-based scaling. Student's t-test was conducted. Symbolic number coding of p-value was used as: *** (p ≤ 0.001), ** (0.001 < p ≤ 0.01), * (0.01 < p ≤ 0.05), ns (p > 0.05, not significant)