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. 2024 Dec 25;7:1699. doi: 10.1038/s42003-024-07404-x

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

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Fig. 5 — a, b Disease enrichment results in DisGeNet diseases on top 1541 trait-related genes for SCZ (a) and ASD (b). The Y-axis lists disease with categories in alphabetical order. c–f Clusters of GO terms enrichment results on top 1541 genes for SCZ (overlapped terms in c, terms uniquely enriched by meanPPI method in d) and ASD (overlapped terms in (e), terms uniquely enriched by meanPPI method in (f). The size and color of the dots were proportional to the number of pathway genes and enrichment significance, respectively. The p-values were adjusted using Bonferroni correction. Clusters were generated from enriched GO terms by aPEAR (Advanced Pathway Enrichment Analysis Representation) package. It exploits the similarities between pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological theme of the experiment.