This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 43 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 | GENDER |
DISTANT METASTASIS |
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 | Fisher's exact test | |
3p gain | 0 (0%) | 22 |
0.107 (1.00) |
0.23 (1.00) |
0.169 (1.00) |
0.624 (1.00) |
|||
3q gain | 0 (0%) | 22 |
0.107 (1.00) |
0.23 (1.00) |
0.169 (1.00) |
0.624 (1.00) |
|||
4p gain | 0 (0%) | 15 |
0.486 (1.00) |
0.93 (1.00) |
0.414 (1.00) |
0.657 (1.00) |
0.326 (1.00) |
0.479 (1.00) |
|
4q gain | 0 (0%) | 16 |
0.486 (1.00) |
0.443 (1.00) |
0.677 (1.00) |
0.657 (1.00) |
0.381 (1.00) |
0.529 (1.00) |
|
7p gain | 0 (0%) | 16 |
0.595 (1.00) |
0.461 (1.00) |
0.208 (1.00) |
1 (1.00) |
0.192 (1.00) |
1 (1.00) |
|
7q gain | 0 (0%) | 16 |
0.595 (1.00) |
0.461 (1.00) |
0.208 (1.00) |
1 (1.00) |
0.192 (1.00) |
1 (1.00) |
|
8p gain | 0 (0%) | 17 |
0.0231 (1.00) |
0.145 (1.00) |
0.0421 (1.00) |
1 (1.00) |
0.00468 (1.00) |
0.54 (1.00) |
|
8q gain | 0 (0%) | 16 |
0.0231 (1.00) |
0.168 (1.00) |
0.033 (1.00) |
0.657 (1.00) |
0.025 (1.00) |
0.0981 (1.00) |
|
9p gain | 0 (0%) | 21 |
0.397 (1.00) |
0.264 (1.00) |
0.604 (1.00) |
0.101 (1.00) |
0.32 (1.00) |
||
9q gain | 0 (0%) | 21 |
0.397 (1.00) |
0.264 (1.00) |
0.604 (1.00) |
0.101 (1.00) |
0.32 (1.00) |
||
11p gain | 0 (0%) | 17 |
0.486 (1.00) |
0.478 (1.00) |
0.234 (1.00) |
1 (1.00) |
0.167 (1.00) |
0.846 (1.00) |
|
11q gain | 0 (0%) | 16 |
0.486 (1.00) |
0.528 (1.00) |
0.208 (1.00) |
0.657 (1.00) |
0.381 (1.00) |
0.529 (1.00) |
|
12p gain | 0 (0%) | 18 |
0.397 (1.00) |
0.361 (1.00) |
0.09 (1.00) |
1 (1.00) |
0.137 (1.00) |
1 (1.00) |
|
12q gain | 0 (0%) | 19 |
0.533 (1.00) |
0.701 (1.00) |
0.18 (1.00) |
1 (1.00) |
0.2 (1.00) |
1 (1.00) |
|
14q gain | 0 (0%) | 16 |
0.595 (1.00) |
0.461 (1.00) |
0.208 (1.00) |
1 (1.00) |
0.192 (1.00) |
1 (1.00) |
|
15q gain | 0 (0%) | 17 |
0.595 (1.00) |
0.313 (1.00) |
0.234 (1.00) |
1 (1.00) |
0.342 (1.00) |
1 (1.00) |
|
16p gain | 0 (0%) | 17 |
0.533 (1.00) |
0.603 (1.00) |
1 (1.00) |
1 (1.00) |
0.342 (1.00) |
0.846 (1.00) |
|
16q gain | 0 (0%) | 17 |
0.533 (1.00) |
0.603 (1.00) |
1 (1.00) |
1 (1.00) |
0.342 (1.00) |
0.846 (1.00) |
|
18p gain | 0 (0%) | 17 |
0.595 (1.00) |
0.651 (1.00) |
0.234 (1.00) |
1 (1.00) |
0.074 (1.00) |
1 (1.00) |
|
18q gain | 0 (0%) | 17 |
0.595 (1.00) |
0.651 (1.00) |
0.234 (1.00) |
1 (1.00) |
0.074 (1.00) |
1 (1.00) |
|
19p gain | 0 (0%) | 19 |
0.533 (1.00) |
0.625 (1.00) |
0.661 (1.00) |
1 (1.00) |
0.806 (1.00) |
0.611 (1.00) |
|
19q gain | 0 (0%) | 20 |
0.533 (1.00) |
0.486 (1.00) |
0.341 (1.00) |
1 (1.00) |
0.582 (1.00) |
0.884 (1.00) |
|
20p gain | 0 (0%) | 18 |
0.421 (1.00) |
0.518 (1.00) |
0.407 (1.00) |
0.231 (1.00) |
0.544 (1.00) |
||
20q gain | 0 (0%) | 18 |
0.421 (1.00) |
0.518 (1.00) |
0.407 (1.00) |
0.231 (1.00) |
0.544 (1.00) |
||
22q gain | 0 (0%) | 18 |
0.195 (1.00) |
0.717 (1.00) |
0.09 (1.00) |
0.679 (1.00) |
0.215 (1.00) |
||
Xq gain | 0 (0%) | 20 |
0.674 (1.00) |
0.536 (1.00) |
0.0464 (1.00) |
0.582 (1.00) |
0.884 (1.00) |
||
1p loss | 0 (0%) | 11 |
0.486 (1.00) |
0.903 (1.00) |
0.0419 (1.00) |
1 (1.00) |
0.838 (1.00) |
0.295 (1.00) |
|
1q loss | 0 (0%) | 11 |
0.486 (1.00) |
0.903 (1.00) |
0.0419 (1.00) |
1 (1.00) |
0.838 (1.00) |
0.295 (1.00) |
|
2p loss | 0 (0%) | 12 |
0.379 (1.00) |
0.234 (1.00) |
0.428 (1.00) |
1 (1.00) |
1 (1.00) |
0.104 (1.00) |
|
2q loss | 0 (0%) | 12 |
0.379 (1.00) |
0.234 (1.00) |
0.428 (1.00) |
1 (1.00) |
1 (1.00) |
0.104 (1.00) |
|
3p loss | 0 (0%) | 21 |
0.533 (1.00) |
0.277 (1.00) |
1 (1.00) |
1 (1.00) |
0.854 (1.00) |
||
3q loss | 0 (0%) | 21 |
0.533 (1.00) |
0.277 (1.00) |
1 (1.00) |
1 (1.00) |
0.854 (1.00) |
||
6p loss | 0 (0%) | 10 |
0.482 (1.00) |
0.69 (1.00) |
0.0992 (1.00) |
1 (1.00) |
0.709 (1.00) |
0.105 (1.00) |
|
6q loss | 0 (0%) | 10 |
0.482 (1.00) |
0.69 (1.00) |
0.0992 (1.00) |
1 (1.00) |
0.709 (1.00) |
0.105 (1.00) |
|
8p loss | 0 (0%) | 22 |
0.674 (1.00) |
0.146 (1.00) |
1 (1.00) |
0.101 (1.00) |
0.499 (1.00) |
||
10p loss | 0 (0%) | 13 |
0.379 (1.00) |
0.44 (1.00) |
0.238 (1.00) |
0.307 (1.00) |
0.802 (1.00) |
||
10q loss | 0 (0%) | 13 |
0.379 (1.00) |
0.44 (1.00) |
0.238 (1.00) |
0.307 (1.00) |
0.802 (1.00) |
||
13q loss | 0 (0%) | 12 |
0.379 (1.00) |
0.689 (1.00) |
0.111 (1.00) |
1 (1.00) |
0.695 (1.00) |
0.104 (1.00) |
|
17p loss | 0 (0%) | 10 |
0.482 (1.00) |
0.69 (1.00) |
0.0992 (1.00) |
1 (1.00) |
0.709 (1.00) |
0.105 (1.00) |
|
17q loss | 0 (0%) | 10 |
0.482 (1.00) |
0.69 (1.00) |
0.0992 (1.00) |
1 (1.00) |
0.709 (1.00) |
0.105 (1.00) |
|
18q loss | 0 (0%) | 22 |
0.421 (1.00) |
0.454 (1.00) |
0.565 (1.00) |
0.407 (1.00) |
0.317 (1.00) |
||
21q loss | 0 (0%) | 16 |
0.138 (1.00) |
0.336 (1.00) |
0.434 (1.00) |
0.381 (1.00) |
1 (1.00) |
||
Xq loss | 0 (0%) | 16 |
0.165 (1.00) |
0.981 (1.00) |
0.115 (1.00) |
0.702 (1.00) |
0.0845 (1.00) |
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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = KICH-TP.clin.merged.picked.txt
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Number of patients = 25
-
Number of significantly arm-level cnvs = 43
-
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 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.