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. 2021 Jan 21;22:39. doi: 10.1186/s13059-021-02262-w

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© The Author(s) 2021

Open AccessThis 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. 2 — NPLS-DA analysis of TEDDY data. a NPLS-DA model mode 1 projection with combined gene expression, metabolites, and DBs showing separation between cases and controls. b R², Q², and element values for the NPLS-DA model. Each element is a triad containing the combination of mode components that captures the indicated explained variance. c NPLS-DA model mode 3 projection showing the relevance of early time points in the model as they have high absolute values. d VIP variable selection-validation by permutation analysis. For both gene expression and metabolomics datasets, the selected VIP-based features have significantly higher R² (circles) and Q² (triangles) values compared to a randomly selected set of features, (*) p < 0.001