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. 2023 Jul 12;14:4154. doi: 10.1038/s41467-023-39869-5

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© The Author(s) 2023

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. 6 — Source data are provided as a Source Data file. a Bar plots of the number of quantified proteins in the ccRCC dataset. There are 187 independent biological samples. The bar height is the total number. The white dots are the numbers from individual runs. Proteins with more than 50% missing values are discarded. b The runtime of analyzing 20 runs of the ccRCC dataset on a Windows desktop. c The runtime of analyzing 20 runs on a Linux server. d PCA plot based on the FP-MSF hybrid results, showing tumor (blue) and normal (red) samples. e Histogram of Spearman’s correlation coefficients between the RNA-Seq and the DIA protein abundance data (FP-MSF hybrid pipeline). The adjusted p-value is from the two-sided test followed by the Benjamini-Hochberg procedure. f Histogram of the Spearman’s correlation coefficients between the RNA-Seq and the TMT DDA-based protein abundance data. The adjusted p-value is from the two-sided test followed by the Benjamini-Hochberg procedure.