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. 2022 Feb 23;8:9. doi: 10.1038/s41540-022-00218-9

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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 By applying a light-up analysis, i.e. by changing the input values of each TF independently and subsequently observing the changes on the output layer, we were able to estimate how the TF-to-target mappings corresponded to TF bindings known from literature. We defined the TF-target covariance fold as the median value of the light-ups for the TF-target regulations found in a database divided by the respective backgrounds. b The light-up enrichments for the TF-target associations found in the TRRUST database are shown. Note that the expected value representing no biological relevance between TF-target mapping is 1 and that light-up values are compared to absolute Spearman rank correlation values, labeled ‘control’. We performed the same analysis with RegNetwork (c), and the interactions annotated with top confidence in DoRothEA (d).