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Cell-Type-Specific Predictive Network Yields Novel Insights into Mouse Embryonic Stem Cell Self-Renewal and Cell Fate

Figure 3

Network Performance Evaluations.

A. Computational assessment of network performance using standard machine learning metrics showed that precision at 10% recall was 90%, and 60% at 25% recall, before and after regularization and out of bag averaging to correct for overfitting to noise. The area under the Receiver Operating Characteristic (ROC) curve (AUC) for the mESC network was 0.7479; after regularization and out of bag averaging, the AUC was 0.7165. B. We conducted 4-fold network cross validations by removing 25% of edges in the gold standard (4-fold Gold Standard). ROC curves showed a small amount of overfitting, most apparent in cross validations for which we removed 25% of genes (rather than edges) from the network training set (Figure S1). C. We conducted 20 bootstrap runs, using 70–30 split (training to test) of the gold standard answer file, and performed out-of-bag averaging to produce a single network. The relatively flat trend of AUC over out-of-bag-averaging runs confirms the minimal amount of overfitting and produced a single network with high confidence inference scores.

Figure 3