Windowed Granger causal inference strategy improves discovery of gene regulatory networks

SWING improves inference of 10-node in silico networks. (A) Changes in AUPR and AUROC in GNW networks. Score changes to individual networks are shown in gray. The mean (red) and median (black) of each score distribution is shown. AUPR and AUROC increase when using SWING-RF or -PLSR compared with their respective base method. SWING-LASSO outperforms LASSO in the E. coli-derived networks. The expected score based on random for each metric is shown as a dashed line. n = 20 networks, kmin=1, kmax=3, and w=10 for all networks. P values were calculated by using the Wilcoxon signed-rank test, ***P < 0.001; **P < 0.01; *P < 0.05. (B) SWING and non-SWING methods are grouped according to similarity of ranked predictions for 40 10-node in silico networks via PCA. PC1 largely separates inference methods based on performance (SI Appendix, Fig. S2), while PC2 separates methods based on underlying base method. Networks inferred by various SWING parameter selections cluster together according to inference type, with SWING methods forming clusters distinct from corresponding base methods.

SWING promotes edges with apparent time delays and increases correlation between genes. The true network structure is provided in SI Appendix, Fig. S7B. (A) Edge rank comparison for E. coli SOS network when using RF and SWING-RF (blue, promoted edges; red, demoted edges; black, no change; gray, false edges; green, lexA → umuDC analyzed in B). We report the lag for edges with an apparent time delay. (B, Upper) Time series for lexA and umuDC show better alignment when umuDC is shifted by one time period, (B, Lower) which improves correlation between the genes.