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. 2023 Nov 27;13:20869. doi: 10.1038/s41598-023-47971-3

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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/.

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Scheme for the identification of optimal normalisers. (A) Representation of the combinatorial approach to evaluating normaliser combinations. For n normalisers, there are 2ⁿ − 1 combinations. (B) Plasma samples analyzed by RT-qPCR and ExpressionSuite. (C) C_q values used to calculate − ΔΔC_q for each combination of normalisers (abbreviated here). (D) Normalised values of each combination of n normalisers for Alzheimer’s disease (AD) and control (HC) groups compared along three metrics—Kolmogorov–Smirnov (KS) test, mean of absolute deviation of group means from zero, and mean of group standard deviations. Data are then ranked by minimising each metric, with the top-ranked normaliser combination given n_combinations points (15 in the example above), and the bottom given 0. Where desired, weights can be applied to each scoring component by multiplying the rank score by a specified value. (E) Individual rank scores for the metrics are summed, and then all normaliser combinations are ranked according to that sum.