(NF1_Any_Mutants cohort)
This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 19 arm-level results and 7 clinical features across 25 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE |
PRIMARY SITE OF DISEASE |
GENDER |
LYMPH NODE METASTASIS |
TUMOR STAGECODE |
NEOPLASM DISEASESTAGE |
||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | t-test | Chi-square test | |
Amp Peak 1(1p12) | 8 (32%) | 17 |
0.601 (1.00) |
0.995 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.345 (1.00) |
|
Amp Peak 2(1q44) | 17 (68%) | 8 |
0.355 (1.00) |
0.668 (1.00) |
0.861 (1.00) |
0.205 (1.00) |
0.697 (1.00) |
0.0923 (1.00) |
|
Amp Peak 3(7p22 3) | 14 (56%) | 11 |
0.347 (1.00) |
0.579 (1.00) |
0.501 (1.00) |
0.0421 (1.00) |
0.0642 (1.00) |
0.727 (1.00) |
|
Amp Peak 4(7q36 1) | 14 (56%) | 11 |
0.454 (1.00) |
0.959 (1.00) |
0.501 (1.00) |
0.234 (1.00) |
0.661 (1.00) |
0.383 (1.00) |
|
Amp Peak 5(12p12 1) | 3 (12%) | 22 |
0.448 (1.00) |
0.571 (1.00) |
0.763 (1.00) |
0.527 (1.00) |
|||
Amp Peak 6(15q26 2) | 8 (32%) | 17 |
0.039 (1.00) |
0.407 (1.00) |
0.736 (1.00) |
0.359 (1.00) |
0.344 (1.00) |
0.062 (1.00) |
|
Amp Peak 7(17q25 3) | 15 (60%) | 10 |
0.451 (1.00) |
0.0109 (1.00) |
0.577 (1.00) |
0.667 (1.00) |
1 (1.00) |
0.812 (1.00) |
|
Del Peak 1(1p36 31) | 6 (24%) | 19 |
0.424 (1.00) |
0.327 (1.00) |
0.065 (1.00) |
0.344 (1.00) |
0.87 (1.00) |
0.188 (1.00) |
|
Del Peak 2(1p13 2) | 9 (36%) | 16 |
0.0221 (1.00) |
0.294 (1.00) |
0.547 (1.00) |
0.394 (1.00) |
0.533 (1.00) |
0.724 (1.00) |
|
Del Peak 3(2q37 3) | 8 (32%) | 17 |
0.797 (1.00) |
0.59 (1.00) |
0.445 (1.00) |
1 (1.00) |
0.528 (1.00) |
0.247 (1.00) |
|
Del Peak 4(3q23) | 3 (12%) | 22 |
0.853 (1.00) |
0.357 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
||
Del Peak 5(6q23 2) | 14 (56%) | 11 |
0.857 (1.00) |
0.162 (1.00) |
0.158 (1.00) |
0.234 (1.00) |
0.323 (1.00) |
0.445 (1.00) |
|
Del Peak 6(9p21 3) | 19 (76%) | 6 |
0.854 (1.00) |
0.0375 (1.00) |
0.83 (1.00) |
0.624 (1.00) |
0.87 (1.00) |
0.642 (1.00) |
|
Del Peak 7(10p15 1) | 11 (44%) | 14 |
0.36 (1.00) |
0.494 (1.00) |
0.663 (1.00) |
0.389 (1.00) |
1 (1.00) |
0.633 (1.00) |
|
Del Peak 8(10q24 33) | 12 (48%) | 13 |
0.557 (1.00) |
0.544 (1.00) |
1 (1.00) |
0.0968 (1.00) |
1 (1.00) |
0.682 (1.00) |
|
Del Peak 9(11q24 3) | 19 (76%) | 6 |
0.938 (1.00) |
0.991 (1.00) |
0.0811 (1.00) |
1 (1.00) |
0.401 (1.00) |
0.752 (1.00) |
|
Del Peak 10(13q34) | 7 (28%) | 18 |
0.718 (1.00) |
0.81 (1.00) |
0.719 (1.00) |
0.64 (1.00) |
0.142 (1.00) |
0.39 (1.00) |
|
Del Peak 11(14q23 3) | 6 (24%) | 19 |
0.503 (1.00) |
0.33 (1.00) |
0.322 (1.00) |
1 (1.00) |
0.209 (1.00) |
0.642 (1.00) |
|
Del Peak 12(16q12 1) | 12 (48%) | 13 |
0.126 (1.00) |
0.899 (1.00) |
0.336 (1.00) |
0.411 (1.00) |
0.229 (1.00) |
0.356 (1.00) |
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Mutation data file = all_lesions.conf_99.cnv.cluster.txt
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Clinical data file = SKCM-NF1_Any_Mutants.clin.merged.picked.txt
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Number of patients = 25
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Number of significantly arm-level cnvs = 19
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Number of selected clinical features = 7
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Exclude genes that fewer than K tumors have mutations, K = 3
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R
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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.