Fig. 4: MethNet hubs control multiple genes and have an impact on patient survival.
From: MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data
a Distribution of regulatory potential as a function of methylation variance across cancers. b Comparison of the distribution of MethNet association per elements for hubs (n = 6,139) versus non-hubs (n = 239,416). Boxplots were drawn with the following parameters: box bounds correspond to 1st and 3rd quantiles, center mark corresponds to median, whisker length is 1.5 the height of the box (inter-quantile region) or up to the extrema of the distribution if they are closer to the box bound. c Mean effect of hub methylation (excluding cancer-specific effects) on overall survival across TCGA cancers. A two-sided Wilcoxon rank sum test with continuity correction is used to calculate the p-value (p-value = 5 × 10−11, nHub = 574, nCRE = 174,139). Boxplots were drawn with default parameters as in panel b. d–f Enrichment of regulatory potential hubs versus non-hub CREs with positive potential (see Methods for details). d Enrichment of hubs versus non-hubs CREs across ChromHMM (n = 121,517). Bar length correspond to mean effect of state versus Low Signal state and error bars correspond to 95% confidence interval. e Enrichment of hub versus non-hub CREs across chromatin remodelers and transcription factor binding sites f Enrichment of hub versus non-hub non-promoter CREs (n = 73,204) as a function H3K27ac chromatin connectivity from CD4-Naive, GM12878 and K562 cells. Bar height represents log odds (logit) effect size on the probability of a CRE being a hub as a function of its connectivity group, no loops is the intercept of the model. Error bars correspond to 95% confidence interval.
