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. 2021 Aug 12;15:53. doi: 10.1186/s40246-021-00350-3

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© The Author(s) 2021

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. 5 — WGCNA to identify similar genes networks of cholesterogenic genes and glycolytic genes. A The relationship of soft threshold and TOM-based dissimilarity (left). The relationship of soft threshold and mean connectivity (right). B After the dynamic of cut and merged, a total of 6 gene modules were finally generated. C Heat map for the correlation of gene modules and phenotypes. D Scatter plot depicting the correlation between gene significance and module membership of genes in turquoise network. E Scatter plot depicting the correlation between gene significance and module membership of genes in yellow network. WGCNA, weighted correlation network analysis; TOM, topological overlap matrix. And P < 0.05 is defined as statistically significant