Figure 1.

Construction of the Abiotic Stress GRN by Ensemble Reverse-Engineering.

(A) An abiotic stress microarray compendium and TFs from PlantTFDB were subjected to reverse-engineering, resulting in four network inference solutions: LeMoNe_qopt25R, LeMoNe_qopt50R, ClrR, and TwixTrixR.

(B) The Venn diagram illustrates the percentage of 785,913 unique regulatory interactions predicted by each of the four network inference solutions and their overlap.

(C) The regulatory predictions were combined by rank aggregation into three ensembles: union, mean reciprocal rank, and average rank.

(D) The top 200,014 predictions from the average rank ensemble made the abiotic stress GRN. Target genes were subsequently clustered into modules of coregulated genes and only the most important regulating TFs per module (≤10) were retained, generating the abiotic stress module GRN.