Extended Data Fig. 6: Benchmarking of the performance of MutPanning for cancer gene identification. | Nature Genetics

Extended Data Fig. 6: Benchmarking of the performance of MutPanning for cancer gene identification.

From: Identification of cancer driver genes based on nucleotide context

Extended Data Fig. 6

We benchmarked the performance of our method against 7 previously published methods for cancer gene identification based on the sequencing data of 11,873 samples spanning 28 different cancer types. The exact number of samples per cancer type can be found in Extended Data Fig. 5. To benchmark the performance of a method, we sorted genes according to the significance values (adjusted for multiple testing) returned by the method. As a conservative approximation of the true-positive rate we used Cancer Gene Census (CGC) genes (a, b, c) and OncoKB genes (d, e, f) to derive ROC and precision-recall curves. We quantified the performance of each method as the area under the ROC curve (AUC) for the top 150 (a, d) or 1000 (b, e) non-CGC/OncoKB genes, respectively. Further, we determined the precision at 5% recall for each method (c, f). We normalized these measures to the maximum within each cancer type.

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