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. 2022 Sep 24;20:208. doi: 10.1186/s12915-022-01398-w

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© The Author(s) 2022

Open AccessThis 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. 4 — Filtering of genes based on network properties in the sea bass. Scatterplots of correlation between GS vs module membership of each module which were used for determination of interesting modules for sex trait in the gonadal transcriptome of sea bass using method #3 (A) and #2 and/or #4 (B). The modules were selected when GS was positively and significantly correlated to modular membership. Gene significance is defined as − log of the p-value of association of the gene with the trait. Module membership (k_ME) measures how correlated each gene is to a particular module eigengene [68]