Fig. 2: MethNet identifies putative activating and repressing distal associations.
From: MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data
a Distribution of HumanMethylation450 probes neighboring a protein-coding gene as a function of the window size. At 1 Mbp the average gene has 400 potential regulators. b Histogram of the robustness of MethNet associations as measured by the number of TCGA cancers it appears in. c Performance of MethNet models, measured by the ratio of explained variance (R2), as a function of dataset size. Trend line is fit with linear regression. Shaded area corresponds to 95% confidence interval of the mean performance given the number of samples in a cancer study (n = 24). d MethNet association effect as a function of distance. Associations are grouped based on their ranked distance to a gene, where −1 includes associations from the first CpG island or non-island upstream of the promoter, and positive distance refers to associations downstream of the TES. For every group of potential associations, we calculated the average distance, mean coefficient, and probability of a MethNet association. e Examples of regulatory effects recovered by MethNet in BRCA. Left: A repressive association between IFNγ and a CTCF binding site 250 kb upstream of the promoter. Right: An activating association between GSTT1 and a non-coding RNA 10 kb downstream of the promoter. The linear regression line that is fit models the mean expression as a function of methylation status of the CRE. Shaded area corresponds to 95% confidence interval (n = 868). f Spearman correlation coefficient for the association shown in panel e across all TCGA cancers. We observe that the signal is robust across all cancers (n = 24).
