| Abstract | We find that ANOVA, F24, and JTK_CYCLE consistently outperform the other three methods when data are limited and noisy; empirical JTK_CYCLE with asymmetry search gives the greatest sensitivity while controlling for the false discovery rate . |
| Conclusions | This enables control of the false discovery rate and testing waveforms beyond sinusoidal ones. |
| E 3 A A g Time s 'r r a E A AA Time Time | A common alternative to the Bonferroni correction is the Benjamini-Hochberg procedure [36], which seeks to control the false discovery rate (FDR). |
| Simulated data benchmarks | 6C and D, we see that the performance of the methods differs considerably when controlling for the false discovery rate (FDR). |
| Supporting Information | The vertical axis shows the number of genes With a p-value (P) (A and B) or false discovery rate (FDR, the Benjamini-Hochberg adjusted p-value) (C and D) below or equal to a significance threshold, shown on |
| Cancer-type-specific domain mutation landscapes across 21 cancer types | We identified ~ 100 cancer-type-specific significantly mutated domain instances (SMDs) in 21 cancer types (S2 Table; P-value = 10—7, Fisher’s Exact test, False Discovery Rate (FDR) <0.05). |
| Cancer-type-specific significantly-mutated domain instance analyses | We chose a P-value threshold (OL = 10—7) yielding a false discovery rate (FDR) of less than 0.05. |
| Cancer-type-specific significantly-mutated position based mutational hotspot analyses | False discovery rate analysis was performed using Benjamini & Hochberg FDR[142]. |