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. 2022 Oct 20;13:6226. doi: 10.1038/s41467-022-33385-8

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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. 3 — a MOFA of proteome, transcriptome, and genome dataset identified 11 latent factors (LF) each explaining at least 1.5% of variance. Explained variances per factor and dataset are color-coded. b Hazard ratios from Cox regression of LFs with time to next treatment (TTNT). LF1, LF2, and LF9 were significantly (FDR < 10%, blue) associated with TTNT. P values (Wald-test) are shown on the right. Mean and 95% confidence intervals are shown. n = 61 biologically independent patient samples. c Genes, transcripts, and proteins with the strongest weights loaded onto LF1, LF2, and LF9. Weights were scaled between genetics (divided by two), proteomics, and transcriptomics (times ten) to achieve similar ranges. d Hazard ratios from Cox regression for TTNT with genes and proteins with strong weights for LF1, LF2, and LF9. Significant associations (p < 0.05) are colored in blue. P values (Wald-test) are shown on the right. Mean and 95% confidence intervals are shown. n = 72 biologically independent patient samples. Source data are provided as a Source Data file.