Fig. 1: Representative gene–gene relationship changes with age, captured by pairwise correlation and a GRN-based approach.

a, Heatmap of pairwise Pearson correlation coefficients for selected cellular functions (modules) and tissues. Correlations were computed based on the original expression data (Original) or expression data reconstructed based on model predictions from c (Reconstructed), in young (20–29 years) or old (60–69 years) samples from Brain, Blood or pooled GTEx tissues (Cross-tissue). b,d, Quantification of within-module (colored) and between-module (beige) correlations for selected modules, in young (opaque) and old (transparent) samples. Correlations were computed based on the original expression data (b) or expression data reconstructed based on model predictions from c. d, Between-module correlations are presented for selected module pairs. A two-sided Mann–Whitney test was used to identify modules with age-related correlation changes: MRC (P = 0.0034 in the Original Brain data, P = 0.000094 in the Reconstructed Brain data, P = 0.018 in the Reconstructed Blood data, P = 0.047 in the Original Cross-tissue data, P = 0.00033 in the Reconstructed Cross-tissue data); MRC – Pol-II (P = 0.055 in the Original Brain data, P = 0.030 in the Reconstructed Brain data, P = 0.00083 in the Original Cross-tissue data, P = 0.0068 in the Reconstructed Cross-tissue data); Antigen binding – ECM (P = 0.023 in the Original Brain data, P = 4.7 × 10⁻¹⁰ in the Original Blood data, P = 0.00028 in the Reconstructed Blood data, P = 0.072 in the Original Cross-tissue data). c, Methodological approach used to capture gene–gene relationships in our regulatory model. Through a combination of regularized linear regression (LASSO) and stability selection, we identified stable predictors for each gene in the transcriptome—that is, genes whose expression pattern across the training data (cancer cell line transcriptomic data) is informative of the expression pattern of the target gene. A linear model was then fit to explain the expression pattern of the target gene (a) based on the pattern of the stable predictors (b and c). The weights of this linear model can then be used in other datasets to reconstruct the expression pattern of the target gene a based on the expression pattern of the stable predictors b and c observed in those datasets. Although the scheme shows two predictors, b and c, for target gene a, the number of predictor genes is not limited; rather, the optimal number of predictors is determined individually for each target gene (Methods). For illustrative purposes, gene sets were restricted to the following functions: respiratory chain complex members I–IV (MRC, GO:0045271, GO:0005749, GO:0005750 and GO:0005751, orange), components of collagen-containing ECM (GO:0062023, purple), Pol-II core complex members (GO:0005665, pink), nucleosome members (GO:0000786, dark green) and peptide antigen binding partners (GO:0042605, brown). ***P < 0.001; **P < 0.01; *P < 0.05; ^.P < 0.1. cor., correlation; Mito., mitochondrial.