This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 4 genes and 3 clinical features across 61 patients, one significant finding detected with Q value < 0.25.
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RET mutation correlated to 'RACE'.
Table 1. Get Full Table Overview of the association between mutation status of 4 genes and 3 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.
|
Clinical Features |
AGE | GENDER | RACE | ||
| nMutated (%) | nWild-Type | Wilcoxon-test | Fisher's exact test | Fisher's exact test | |
| RET | 4 (7%) | 57 |
0.726 (1.00) |
1 (1.00) |
0.00801 (0.0961) |
| HRAS | 5 (8%) | 56 |
0.572 (1.00) |
0.154 (1.00) |
1 (1.00) |
| EPAS1 | 4 (7%) | 57 |
0.0778 (0.855) |
0.602 (1.00) |
0.307 (1.00) |
| NF1 | 7 (11%) | 54 |
0.138 (1.00) |
1 (1.00) |
0.747 (1.00) |
P value = 0.00801 (Fisher's exact test), Q value = 0.096
Table S1. Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #3: 'RACE'
| nPatients | AMERICAN INDIAN OR ALASKA NATIVE | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
|---|---|---|---|---|
| ALL | 1 | 3 | 7 | 48 |
| RET MUTATED | 1 | 1 | 1 | 1 |
| RET WILD-TYPE | 0 | 2 | 6 | 47 |
Figure S1. Get High-res Image Gene #4: 'RET MUTATION STATUS' versus Clinical Feature #3: 'RACE'
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Mutation data file = transformed.cor.cli.txt
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Clinical data file = PCPG-TP.merged_data.txt
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Number of patients = 61
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Number of significantly mutated genes = 4
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Number of selected clinical features = 3
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Exclude genes that fewer than K tumors have mutations, K = 3
For binary or multi-class clinical features (nominal or ordinal), two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.