Supplementary figures List of Figures 1 Evaluation metrics for individual comparisons. .3 2 Effect of number of top edges selected on algorithm rankings . .5 3 Predictable regulators for the Gasch dataset . .7 4 Predictable regulators for the Tran (FBS) dataset. .9 5 Predictable regulators for the Zhao dataset. 11 6 Predictable regulators for the Shalek dataset. 13 7 Jaccard similarity between top edge sets of inferred networks. 15 8 F-score similarity between top edge sets. 17 9 Comparison of Jaccard index and F-score as similarity metrics. 19 1 F-score AUPR Predictable TFs Perturb 0.04 0.04 0.06 0.05 0.04 0.05 0.05 0.04 0.03 0.06 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.02 0.03 0.03 0 2 2 2 1 1 6 1 0 2 0 Gasch ChIP 0.02 0.01 0.03 0.02 0.03 0.03 0.04 0.02 0.02 0.04 0.02 0.04 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 0.04 0 1 3 1 2 2 14 0 0 3 0 Perturb+ChIP 0.02 0.02 0.03 0.02 0.02 0.03 0.04 0.02 0.01 0.04 0.01 0.03 0.03 0.04 0.04 0.03 0.04 0.06 0.04 0.03 0.05 0.03 2 1 4 3 4 6 24 0 4 6 0 Perturb 0.04 0.03 0.03 0.05 0.04 0.04 0.03 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.02 2 1 2 9 2 7 2 1 2 0 Jackson ChIP 0.02 0.01 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.01 0.03 0.03 0.03 0.04 0.04 0.04 0.04 0.03 0.03 0.03 7 0 3 18 9 9 9 0 3 0 Perturb+ChIP 0.02 0.01 0.01 0.03 0.03 0.02 0.03 0.01 0.03 0.01 0.02 0.02 0.03 0.03 0.03 0.03 0.04 0.02 0.04 0.02 10 0 3 25 15 11 7 0 15 0 Perturb 0.07 0.09 0.11 0.10 0.12 0.10 0.06 0.08 0.07 0.11 0.06 0.08 0.09 0.10 0.10 0.10 0.09 0.08 0.09 0.07 0.10 0.07 4 1 6 6 6 7 0 4 1 7 1 Tran(A2S) ChIP 0.05 0.04 0.06 0.06 0.06 0.06 0.05 0.05 0.03 0.06 0.04 0.18 0.17 0.19 0.20 0.19 0.20 0.20 0.19 0.16 0.20 0.18 9 3 5 15 12 13 13 3 1 11 0 Perturb+ChIP 0.02 0.03 0.01 0.02 0.02 0.02 0.01 0.01 0.03 0.02 0.01 0.08 0.09 0.09 0.08 0.08 0.08 0.06 0.07 0.10 0.08 0.08 6 6 11 13 13 6 1 5 9 9 0 Perturb 0.09 0.08 0.10 0.12 0.11 0.11 0.06 0.08 0.09 0.11 0.06 0.08 0.08 0.09 0.10 0.09 0.09 0.07 0.08 0.09 0.11 0.07 6 1 3 8 6 7 1 5 5 6 0 Tran(FBS) ChIP 0.05 0.04 0.05 0.06 0.06 0.06 0.05 0.05 0.04 0.06 0.04 0.18 0.17 0.18 0.20 0.19 0.20 0.19 0.19 0.17 0.20 0.17 7 4 0 10 8 10 7 9 2 11 0 Perturb+ChIP 0.02 0.03 0.02 0.02 0.02 0.02 0.01 0.01 0.03 0.03 0.01 0.08 0.08 0.09 0.08 0.07 0.07 0.06 0.07 0.10 0.08 0.07 10 2 12 10 12 8 1 6 14 9 0 Perturb 0.00 0.11 0.12 0.12 0.13 0.03 0.13 0.05 0.05 0.11 0.10 0.11 0.11 0.06 0.12 0.06 0 5 4 5 2 1 7 0 Zhao ChIP 0.03 0.04 0.06 0.06 0.06 0.01 0.05 0.03 0.15 0.19 0.19 0.19 0.20 0.15 0.20 0.16 3 6 12 15 10 0 11 0 Perturb+ChIP 0.00 0.01 0.02 0.02 0.01 0.02 0.01 0.01 0.07 0.10 0.10 0.10 0.08 0.12 0.10 0.09 0 6 12 13 0 8 16 0 Perturb 0.07 0.02 0.04 0.11 0.08 0.14 0.04 0.08 0.04 0.08 0.05 0.39 0.33 0.34 0.40 0.43 0.44 0.35 0.47 0.35 0.41 0.38 10 0 0 11 19 19 0 19 0 18 0 Shalek ChIP 0.05 0.03 0.04 0.09 0.06 0.11 0.04 0.06 0.04 0.06 0.04 0.48 0.45 0.44 0.47 0.49 0.50 0.45 0.52 0.45 0.48 0.47 11 0 0 9 20 15 0 16 0 16 0 Perturb+ChIP 0.03 0.01 0.00 0.05 0.02 0.08 0.03 0.04 0.01 0.04 0.02 0.21 0.37 0.19 0.44 0.25 0.47 0.37 0.45 0.34 0.45 0.35 6 0 0 8 17 19 1 20 0 16 0 Perturb 0.09 0.02 0.11 0.08 0.11 0.10 0.03 0.12 0.03 0.12 0.05 0.08 0.06 0.09 0.08 0.09 0.09 0.06 0.10 0.07 0.10 0.07 2 0 4 2 7 3 0 3 1 5 0 Han ChIP 0.02 0.02 0.02 0.02 0.02 0.02 0.03 0.03 0.01 0.02 0.02 0.33 0.32 0.30 0.34 0.37 0.35 0.38 0.38 0.28 0.34 0.32 6 0 11 4 10 8 26 13 1 14 0 Perturb+ChIP 0.02 0.01 0.02 0.02 0.03 0.02 0.00 0.02 0.02 0.03 0.01 0.09 0.10 0.13 0.09 0.09 0.09 0.07 0.08 0.11 0.10 0.08 8 6 13 14 22 9 1 5 4 15 0 PIDC PIDC PIDC LEAP Scribe LEAP Scribe LEAP Scribe MERLIN SCENICSCODE SILGGMPearsonRandom MERLIN SCENICSCODE SILGGMPearsonRandom MERLIN SCENICSCODE SILGGMPearsonRandom InferelatorkNN-DREMI InferelatorkNN-DREMI InferelatorkNN-DREMI 2 Supplemental Fig 1. Evaluation metrics for individual comparisons. Heat maps depicting the per-comparison evalu- ation metrics. From left to right: F-score of top 5,000 edges, AUPR of global network, and count of predictable TFs. Algorithms are ordered alphabetically, followed by the Pearson and random networks. 3 Gasch Jackson Sridharan(A2S) 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 Spearman rank correlation Spearman rank correlation 0.2 0.2 Spearman rank correlation 0.2 Perturb Perturb Perturb ChIP ChIP ChIP 0.0 Perturb+ChIP 0.0 Perturb+ChIP 0.0 Perturb+ChIP 500 300 500 500 300 300 1,000 3,000 3,000 5,000 1,000 5,000 1,000 3,000 5,000 10,000 30,000 50,000 10,000 30,000 50,000 50,000 10,000 30,000 Number of top (k) edges) Number of top (k) edges) Number of top (k) edges) Sridharan(FBS) Zhao Shalek 1.0 1.0 1.0 0.8 0.8 0.8 0.6 0.6 0.6 0.4 0.4 0.4 Spearman rank correlation Spearman rank correlation 0.2 Spearman rank correlation 0.2 0.2 Perturb Perturb Perturb ChIP ChIP ChIP 0.0 Perturb+ChIP 0.0 Perturb+ChIP 0.0 Perturb+ChIP 500 500 300 500 300 300 3,000 5,000 1,000 3,000 5,000 1,000 1,000 3,000 5,000 10,000 50,000 10,000 10,000 30,000 30,000 50,000 50,000 30,000 Number of top (k) edges) Number of top (k) edges) Number of top (k) edges) Han 1.0 0.8 0.6 0.4 Spearman rank correlation 0.2 Perturb ChIP 0.0 Perturb+ChIP 300 500 1,000 3,000 5,000 10,000 50,000 30,000 Number of top (k) edges) 4 Supplemental Fig 2. Effect of number of top edges selected on algorithm rankings. We measured the F-score of each algorithm with respect to each experimentally derived network using the top 100, 300, 500, 1,000, 3,000, 5,000, 10,000, 30,000, and 50,000 edges.