Data | To correct for these multiple comparisons we apply the Benjamini-Hoch-berg procedure [17] to control for the false discovery rate a. |
Data | For example, if 100 comorbidities are identified with a false discovery rate a of a = 0.01, the eXpected number of false positives among these comorbidities is one. |
Data | We Will therefore be interested in the recall R(a) as a function of the false discovery rate a. R(a) is the probability that a diabetic comorbidity listed in Table 1 is also identified by our co-occurrence analysis at a given level of a. |
Results/Discussion | Each diagnosis where the null hypothesis of statistical independence with either DM1 or DMZ can be rejected with a given value of the false discovery rate in at least one of the age groups is identified as a comorbidity. |
Results/Discussion | A false discovery rate of a = 0.001 gives a list of 75 significant comorbidities and a recall of R(a = 0.001) = 0.59. |
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]. |