Fig. 7

Comparing differential expression and differential correlation with TP53 in samples with and without p53 mutations. For each gene, we plot both DGCA’s calculated differential correlation z-score between that gene and TP53 in p53 non-mutated breast cancer samples and p53-mutated samples (x-axis), as well as limma’s differential expression t statistic for that gene’s differential expression between the same p53 wildtype samples and p53-mutated samples (y-axis). When differential correlation z-scores are calculated on positive correlation values only a, the Spearman correlation between these two measures is not significant (ρ = 0.08, p-value = 0.15), and when differential correlation z-scores are calculated across all correlation values b, the Spearman correlation between these two measures is also not significant (ρ = 0.06, p-value = 0.30). The blue line represents a linear model of the best fit, with the grey lines representing 95% confidence intervals, computed using ggplot2