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. 2024 Jul 30;25:289. doi: 10.1186/s12931-024-02919-7

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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. 4 — A sparse multi-omic model of sarcoidosis and progression. A) Projection of samples based on selected feature weights. Points represent samples, colored by disease group, and are plotted for each data type on the top latent variables from the DIABLO multi-omic model. B) Feature importance of selected features for latent variable (1) Bars are colored by strongest sample group loading. C) Feature importance of selected features for latent variable (2) Bars are colored by strongest sample group loading. D) Network constructed from feature correlations on latent variable (1) Features are colored by dataset. Edges are colored by feature correlation, with green representing positive correlation. Features in the network share at least 90% correlation. E) Network constructed from feature correlations on latent variable (2) Features are colored by dataset. Edges are colored by feature correlation, with green representing positive correlation. Features in the network share at least 50% correlation