Figure 1.
Construction of gene regulatory networks by integrating targeted perturbation data with binding data.
The relations in constructed gene regulatory network correspond to direct regulatory interactions.
Table 1.
Assessment of currently available genome-scale gold-standard networks used by prior gene network reverse-engineering studies.
Figure 2.
Gold-standard gene regulatory network #1.
Transcription factors are shown with large blue circles, and other genes are shown with small green circles. Edges in the network represent direct regulatory interactions. Inhibiting edges are shown with red, and excitatory edges are shown with black.
Figure 3.
Direct regulatory interactions between transcription factors in gold-standard gene regulatory network #1.
Inhibiting edges are shown with red, and excitatory edges are shown with black.
Figure 4.
Topological analysis of gold-standard gene regulatory network #1.
The analysis was performed in Cytoscape with NetworkAnalyzer.
Table 2.
Overlapping identified binding with regulatory relations results in gold-standard networks with direct regulatory relations.
Table 3.
Sensitivity and specificity.
Table 4.
Euclidean distance from the optimal algorithm with sensitivity = 1 and specificity = 1.
Figure 5.
ROC curve of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to 13 datasets.
Figure 6.
ROC curves of the Pareto frontier for sensitivity/specificity pairs obtained by application of 18 network reverse-engineering approaches to datasets of each type.
Table 5.
Positive predictive value (PPV) and negative predictive value (NPV).
Table 6.
Euclidean distance from the optimal algorithm with PPV = 1 and NPV = 1.
Table 7.
Recall (sensitivity) and precision (PPV).
Table 8.
Euclidean distance from the optimal algorithm with Sensitivity = 1 and PPV = 1.
Figure 7.
Example scatter-plot of transcription factor connectivity versus the accuracy (combined PPV/NPV metric) of reconstructing their sub-networks.
The left panel shows the scatter-plot and the right panel shows the null distribution for establishing statistical significance of the observed correlation.
Table 9.
Number of networks that have significant correlations between transcription factor connectivity and accuracy of reconstructing their sub-networks.
Table 10.
Datasets used for gene regulatory network reverse-engineering.
Table 11.
Statistical approaches used for gene regulatory network reverse-engineering.