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. 2022 May 17;23:117. doi: 10.1186/s13059-022-02681-3

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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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — A Selection of high-attribution features from models trained with protein-coding gene expression, lncRNA gene expression or splice junction usage. Dotted lines show cutoffs used; purple points around coordinates (0,0) show features selected in neutral sets. B Median attribution values of 100 protein-coding genes, lncRNAs or splice junctions with the highest positive and negative attributions across tumor tissues. C Median attribution values of genes associated with cancer from the COSMIC database. D Overlap between COSMIC oncogenes and TSGs, and genes with high positive (top panel) or negative (bottom panel) attribution values, or between COSMIC tier 1 genes (high confidence for causal role in cancer) and tier 2 genes (some evidence of causal involvement in cancer), and all high-attribution genes (central panel). E Overlap between genes associated with cancer and genes harboring junctions with high attribution values. In both D and E, enrichment or depletion factors were calculated from the ratio of observed vs. expected overlapping genes between sets, and p-values were calculated using the hypergeometric test