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. 2022 Jan 10;12:450. doi: 10.1038/s41598-021-04182-y

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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 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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Cross-prediction matrix that measures the performance of the proposed model in terms of the area under the receiver operating characteristic curve (AUROC) for (A) taxonomic, (B) KEGG and (C) eggNog metagenomic profiles. The diagonal represents the intra-project performance by reporting the mean of the AUROC of 20-times tenfold cross validation, whereas the off-diagonal shows the cross-dataset performance, i.e. train with the model indicated in the rows and test over the project in the columns. Finally, the Leave one Project Out (LOPO) row reports the performance of predicting the dataset referred to in the columns while training with the other datasets, whereas the oLOPO row is the same experiment but using the functional signature learned during the LOPO procedure.