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. 2023 Jan 23;14:377. doi: 10.1038/s41467-023-35888-4

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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. 2 — The correlation of tissue-level GReX predictions using MiXcan or PrediXcan with measured gene expression levels in adjacent normal mammary tissue samples from 103 European ancestry women with breast cancer in TCGA were computed for (a) all 6461 genes with MiXcan and PrediXcan models trained using mammary tissue samples from 125 European ancestry women in GTEx, (b) 5473 genes with cell-type-specific MiXcan models, and (c) 988 genes with nonspecific MiXcan models. Differences between the correlations for MiXcan and PrediXcan were compared using the two-sided Wilcoxon signed-rank test. Boxplot bounds show the lower, median, and upper quartiles; whisker lengths are 1.5 times the interquartile range; and points beyond the whiskers are outliers. Source data are provided as a Source Data file.