Detecting Cancer Outlier Genes with Potential Rearrangement Using Gene Expression Data and Biological Networks
Figure 2
Evaluation of EigFusion performance on synthetic and real cancer data, AUC values are used to assess the performance of fusion gene detection methods. ROC curves were plotted as 1-specificity versus sensitivity of the methods. We plotted ROC curves for each method in several cancer samples size (x-axis) and found the area under the curve (AUC) as a measure of performance. (a) Using synthetic data, COPA and KS showed poor performance over all cases; on the other hand, ORT, GTI, and OS showed that poor performance is affected by the ratio of the size cancer samples to normal samples. (b) Applying all the methods on real prostate data (Singh data) showed that EigFusion outperforms the other methods.
