| 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 domain mutation landscapes across 21 cancer types | Enrichment for Cancer Census genes was both strong and significant (~ 12-fold enrichment; P-value 2 5X 10—34, Fisher’s Exact test ), and suggests the remaining 54 genes that are not already known to be cancer drivers represent good candidates. |
| Cancer-type-specific domain mutation landscapes across 21 cancer types | Of the 94 genes encoding cancer type-specific SMDs, 24 were found in the Sleeping Beauty dataset (~ 3-fold enrichment; P-Value 2 7X 10—06, Fisher’s Exact test ). |
| Cancer-type-specific positioning of mutations within a given gene | These 52 genes were enriched for evidence of involvement in cancer, with 16 being Cancer Census genes (enrichment factor ~ 11.9; P-value = 6.7 X1043, Fisher’s Exact test), and 15 being candidate cancer genes according to the Sleeping Beauty screen (enrichment factor ~ 4.5; P-value = 1.9 X10'6, Fisher’s Exact test ). |
| Cancer-type-specific significantly-mutated domain instance analyses | We analyzed the tendency of SMDs to co-occur in the same patient sample using Fisher’s Exact test (“stats” package in R). |
| Cancer-type-specific significantly-mutated domain instance analyses | Overlap between our candidate gene set and Cancer Census genes and the Sleeping Beauty gene sets was also analyzed using the Fisher’s Exact Test (“stats” package in R). |
| Cancer-type-specific significantly-mutated position based mutational hotspot analyses | We calculated the mutational hotspots within each domain instance encoded by a single gene based on Fisher’s Exact test with a P-Value cutoff 0.01 (FDR <0.05). |
| Mutational trends of oncoproteins and tumor suppressor proteins | Functional sites were significantly overrepresented among oncogenic mutational hotspots (Odds ratio = 10.0, P = 0.0006, Fisher’s Exact Test ). |
| Biological validation analysis | First we identify the set of GO terms (pathways) that are significantly enriched within the given set of seed genes using Fisher’s exact test (Bonferroni corrected p-value<0.5). |
| Biological validation analysis | The statistical significance of an observed number is then determined using Fisher’s exact test . |
| Interaction patterns of disease proteins within the Interactome | We found that only between ~ 1%-5% of the communities detected by the different methods are significantly enriched (p-value < 0.05, Fisher’s exact test ) with any set of disease proteins (Fig. |
| Pairwise covariation | But, as the total sample size tends to infinity, ranking based on LR is asymptotically equivalent to ranking based on Fisher’s exact test of independence [64]. |
| Supporting Information | PR-PR pairs ranked by Fisher exact test p-Value calculated from 2013 HIVDB sequences. |
| Supporting Information | We show a comparison between PR-PR pair rankings calculated using Fisher’s exact test on a MSA provided by the Stanford HIVDB [24] dated 4/29/2013 and the permutation test |