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. 2022 Feb 28;28(3):535–544. doi: 10.1038/s41591-022-01695-5

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© The Author(s) 2022

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 2 — (a-e) Machine learning association analysis of the microbiome and response using either metadata alone or in combination with taxonomic (species abundance) and functional profiles (KEGG orthologs’ abundances) in PRIMM-NL. AUC-ROC curves are computed using LASSO models trained using 100-repeated fivefold-stratified cross-validations. Metadata used in the models included: age, gender, performance status, PPI use, antibiotic use, steroid use, ICI and previous therapy. Shaded areas represent AUC-ROCs from each individual machine learning model. (f) Multivariate analysis showing the amount of inferred variance explained (R2, blue vertical bars) by each identified covariate and their respective p value (orange vertical bars) as determined by PERMANOVA on KEGG clr-transformed relative abundances.