(All_Primary 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 29 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 | Chi-square test | t-test | Chi-square test | |
Amp Peak 1(5p15 33) | 7 (24%) | 22 |
1 (1.00) |
0.965 (1.00) |
0.692 (1.00) |
0.642 (1.00) |
0.464 (1.00) |
0.52 (1.00) |
|
Amp Peak 2(8q24 3) | 14 (48%) | 15 |
0.667 (1.00) |
0.101 (1.00) |
1 (1.00) |
1 (1.00) |
0.652 (1.00) |
0.615 (1.00) |
|
Amp Peak 4(13q31 3) | 10 (34%) | 19 |
0.0143 (1.00) |
0.603 (1.00) |
1 (1.00) |
1 (1.00) |
0.613 (1.00) |
0.379 (1.00) |
|
Del Peak 1(1p22 1) | 6 (21%) | 23 |
1 (1.00) |
0.966 (1.00) |
0.627 (1.00) |
0.339 (1.00) |
0.201 (1.00) |
0.0694 (1.00) |
|
Del Peak 2(1q41) | 5 (17%) | 24 |
0.519 (1.00) |
0.987 (1.00) |
0.553 (1.00) |
0.633 (1.00) |
0.419 (1.00) |
0.958 (1.00) |
|
Del Peak 3(4q34 3) | 8 (28%) | 21 |
1 (1.00) |
0.502 (1.00) |
0.748 (1.00) |
0.675 (1.00) |
0.169 (1.00) |
0.349 (1.00) |
|
Del Peak 4(5q31 3) | 9 (31%) | 20 |
0.123 (1.00) |
0.433 (1.00) |
0.076 (1.00) |
0.675 (1.00) |
0.0728 (1.00) |
0.56 (1.00) |
|
Del Peak 5(6q14 1) | 10 (34%) | 19 |
0.519 (1.00) |
0.901 (1.00) |
1 (1.00) |
0.431 (1.00) |
0.94 (1.00) |
0.73 (1.00) |
|
Del Peak 6(6q27) | 13 (45%) | 16 |
0.212 (1.00) |
0.919 (1.00) |
0.737 (1.00) |
1 (1.00) |
0.514 (1.00) |
0.579 (1.00) |
|
Del Peak 7(6q27) | 13 (45%) | 16 |
0.212 (1.00) |
0.919 (1.00) |
0.737 (1.00) |
1 (1.00) |
0.514 (1.00) |
0.579 (1.00) |
|
Del Peak 8(9p24 2) | 20 (69%) | 9 |
0.229 (1.00) |
0.266 (1.00) |
1 (1.00) |
0.675 (1.00) |
0.296 (1.00) |
0.668 (1.00) |
|
Del Peak 9(9p21 3) | 23 (79%) | 6 |
0.717 (1.00) |
0.162 (1.00) |
1 (1.00) |
0.633 (1.00) |
0.56 (1.00) |
0.838 (1.00) |
|
Del Peak 10(10p15 3) | 17 (59%) | 12 |
0.981 (1.00) |
0.159 (1.00) |
0.404 (1.00) |
0.694 (1.00) |
0.187 (1.00) |
0.463 (1.00) |
|
Del Peak 11(10q23 31) | 19 (66%) | 10 |
0.981 (1.00) |
0.15 (1.00) |
0.376 (1.00) |
1 (1.00) |
0.481 (1.00) |
0.731 (1.00) |
|
Del Peak 12(11q24 3) | 14 (48%) | 15 |
0.683 (1.00) |
0.858 (1.00) |
1 (1.00) |
1 (1.00) |
0.652 (1.00) |
0.329 (1.00) |
|
Del Peak 13(12q24 32) | 9 (31%) | 20 |
1 (1.00) |
0.372 (1.00) |
0.364 (1.00) |
1 (1.00) |
0.296 (1.00) |
0.524 (1.00) |
|
Del Peak 14(15q15 1) | 7 (24%) | 22 |
0.519 (1.00) |
0.463 (1.00) |
0.692 (1.00) |
1 (1.00) |
0.267 (1.00) |
0.739 (1.00) |
|
Del Peak 15(16q23 3) | 8 (28%) | 21 |
0.0143 (1.00) |
0.851 (1.00) |
0.748 (1.00) |
1 (1.00) |
0.644 (1.00) |
0.883 (1.00) |
|
Del Peak 16(22q12 1) | 4 (14%) | 25 |
1 (1.00) |
0.473 (1.00) |
1 (1.00) |
0.568 (1.00) |
0.299 (1.00) |
0.958 (1.00) |
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Mutation data file = all_lesions.conf_99.cnv.cluster.txt
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Clinical data file = SKCM-All_Primary.clin.merged.picked.txt
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Number of patients = 29
-
Number of significantly arm-level cnvs = 19
-
Number of selected clinical features = 7
-
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.