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. 2022 May 12;13:2622. doi: 10.1038/s41467-022-30094-0

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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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Fig. 1 — A data-independent acquisition (DIA) benchmark dataset was created by adding E. coli peptides in known ratios to peptide preparations of lymph nodes of 92 individuals. We analyzed the raw data with different spectral libraries and DIA software suites. From samples to which E. coli peptides were added in the two E. coli: human peptide ratios 1:25 and 1:12, bootstrap datasets with group sizes of 3 to 23 were generated. For each of those 21 different group sizes, 100 bootstrap datasets were generated. On each bootstrap dataset different data analysis workflows, composed of different sparsity reductions, normalization options, and different statistical tests for detecting differentially abundant proteins, were applied. The results were returned in a table containing p-values and log2 fold-changes (log2FCs) for each protein. As the ground truth about the changed proteins (E. coli) is known, the prediction performance of each workflow can be assessed. This can be done based on the p-values from the statistical tests by calculating the receiver operating characteristic (ROC) curve, based on which the area under curve (AUC) is calculated. To quantify the accuracy of quantification the root-mean-square error (RMSE) is calculated based on the detected log2FC.