Fig 5. Network propagation improves the correlation between mRNA and protein levels of ageing tissues (brain and liver) and PCa samples.

A, B: correlations between propagated scores from mRNA log₂ fold changes and protein weighted mean log₂ fold changes from old brain samples with varying spreading coefficients for HD (A) and RWR (B). C, D: Correlations of propagated scores from mRNA and protein log₂ fold changes of liver samples with HD (C) and RWR (D). Dashed red lines: local maximal correlations from the between-dataset consistency analysis. Blue circles: average maximal correlations from the within-dataset consistency analysis. E, F: Correlations between propagated mRNA log₂ fold changes and corresponding propagated protein log₂ fold changes across the values of the spreading parameter for the 63 PCa tumor samples with HD (E) and RWR (F). Each gray curve corresponds to a tumor sample while the ‘average’ curve is depicted in blue. Average maximal correlations are circled. Correlations were calculated using genes that were expressed and quantified with both RNA-Sequencing and MS proteomics as well as present in the functional interaction network (n = 1,772 genes for brain, n = 1,670 for liver, n = 1,828 for PCa).