This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 55 arm-level events and 5 clinical features across 31 patients, no significant finding detected with Q value < 0.25.
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No arm-level cnvs related to clinical features.
Clinical Features |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
ETHNICITY | ||
nCNV (%) | nWild-Type | Wilcoxon-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
1p gain | 10 (32%) | 21 |
0.916 (1.00) |
0.513 (1.00) |
0.252 (1.00) |
0.538 (1.00) |
1 (1.00) |
1q gain | 11 (35%) | 20 |
0.82 (1.00) |
0.561 (1.00) |
0.458 (1.00) |
0.584 (1.00) |
1 (1.00) |
2p gain | 11 (35%) | 20 |
0.341 (1.00) |
0.308 (1.00) |
0.273 (1.00) |
1 (1.00) |
0.535 (1.00) |
2q gain | 12 (39%) | 19 |
0.0487 (1.00) |
0.396 (1.00) |
0.149 (1.00) |
1 (1.00) |
0.265 (1.00) |
3p gain | 6 (19%) | 25 |
0.96 (1.00) |
0.426 (1.00) |
0.654 (1.00) |
1 (1.00) |
0.488 (1.00) |
3q gain | 9 (29%) | 22 |
0.81 (1.00) |
0.0805 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
6p gain | 6 (19%) | 25 |
0.153 (1.00) |
0.0549 (1.00) |
0.654 (1.00) |
0.121 (1.00) |
1 (1.00) |
6q gain | 6 (19%) | 25 |
0.27 (1.00) |
0.0558 (1.00) |
0.654 (1.00) |
0.121 (1.00) |
1 (1.00) |
7p gain | 28 (90%) | 3 |
0.16 (1.00) |
1 (1.00) |
0.6 (1.00) |
1 (1.00) |
0.0189 (1.00) |
7q gain | 27 (87%) | 4 |
0.301 (1.00) |
0.668 (1.00) |
1 (1.00) |
0.426 (1.00) |
0.0369 (1.00) |
8p gain | 25 (81%) | 6 |
0.88 (1.00) |
0.184 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
8q gain | 24 (77%) | 7 |
0.266 (1.00) |
0.361 (1.00) |
0.22 (1.00) |
1 (1.00) |
0.55 (1.00) |
9q gain | 3 (10%) | 28 |
0.284 (1.00) |
0.138 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
12q gain | 24 (77%) | 7 |
0.0614 (1.00) |
0.96 (1.00) |
1 (1.00) |
0.541 (1.00) |
0.55 (1.00) |
14q gain | 11 (35%) | 20 |
0.0859 (1.00) |
0.511 (1.00) |
1 (1.00) |
0.519 (1.00) |
1 (1.00) |
15q gain | 8 (26%) | 23 |
0.0266 (1.00) |
0.93 (1.00) |
0.433 (1.00) |
1 (1.00) |
0.55 (1.00) |
16p gain | 4 (13%) | 27 |
0.262 (1.00) |
0.0874 (1.00) |
0.101 (1.00) |
0.121 (1.00) |
1 (1.00) |
16q gain | 5 (16%) | 26 |
0.706 (1.00) |
0.167 (1.00) |
0.333 (1.00) |
0.219 (1.00) |
1 (1.00) |
17p gain | 9 (29%) | 22 |
0.827 (1.00) |
0.374 (1.00) |
0.113 (1.00) |
0.25 (1.00) |
0.537 (1.00) |
17q gain | 12 (39%) | 19 |
0.807 (1.00) |
0.243 (1.00) |
0.273 (1.00) |
0.603 (1.00) |
0.265 (1.00) |
19p gain | 5 (16%) | 26 |
0.0179 (1.00) |
0.568 (1.00) |
0.654 (1.00) |
1 (1.00) |
1 (1.00) |
20p gain | 9 (29%) | 22 |
0.183 (1.00) |
0.516 (1.00) |
0.433 (1.00) |
1 (1.00) |
0.537 (1.00) |
20q gain | 10 (32%) | 21 |
0.15 (1.00) |
0.346 (1.00) |
0.252 (1.00) |
1 (1.00) |
0.533 (1.00) |
21q gain | 27 (87%) | 4 |
0.443 (1.00) |
0.77 (1.00) |
0.6 (1.00) |
1 (1.00) |
0.349 (1.00) |
22q gain | 6 (19%) | 25 |
0.0752 (1.00) |
0.493 (1.00) |
0.0829 (1.00) |
1 (1.00) |
1 (1.00) |
xq gain | 6 (19%) | 25 |
0.0141 (1.00) |
0.939 (1.00) |
1 (1.00) |
0.519 (1.00) |
1 (1.00) |
3p loss | 12 (39%) | 19 |
0.684 (1.00) |
0.334 (1.00) |
0.716 (1.00) |
1 (1.00) |
1 (1.00) |
3q loss | 9 (29%) | 22 |
0.93 (1.00) |
0.774 (1.00) |
0.704 (1.00) |
0.603 (1.00) |
0.537 (1.00) |
4p loss | 23 (74%) | 8 |
0.175 (1.00) |
0.482 (1.00) |
0.433 (1.00) |
0.519 (1.00) |
0.55 (1.00) |
4q loss | 25 (81%) | 6 |
0.12 (1.00) |
0.492 (1.00) |
0.394 (1.00) |
1 (1.00) |
1 (1.00) |
5p loss | 21 (68%) | 10 |
0.138 (1.00) |
0.842 (1.00) |
0.0538 (1.00) |
1 (1.00) |
1 (1.00) |
5q loss | 19 (61%) | 12 |
0.529 (1.00) |
0.146 (1.00) |
0.00915 (1.00) |
0.576 (1.00) |
1 (1.00) |
6p loss | 5 (16%) | 26 |
0.726 (1.00) |
0.975 (1.00) |
0.654 (1.00) |
0.519 (1.00) |
0.422 (1.00) |
6q loss | 5 (16%) | 26 |
0.726 (1.00) |
0.975 (1.00) |
0.654 (1.00) |
0.519 (1.00) |
0.422 (1.00) |
9p loss | 18 (58%) | 13 |
0.779 (1.00) |
0.625 (1.00) |
0.722 (1.00) |
0.519 (1.00) |
0.558 (1.00) |
9q loss | 16 (52%) | 15 |
0.797 (1.00) |
0.714 (1.00) |
0.724 (1.00) |
0.237 (1.00) |
0.101 (1.00) |
10p loss | 20 (65%) | 11 |
0.057 (1.00) |
0.439 (1.00) |
1 (1.00) |
0.261 (1.00) |
0.281 (1.00) |
10q loss | 20 (65%) | 11 |
0.238 (1.00) |
0.704 (1.00) |
0.458 (1.00) |
0.261 (1.00) |
0.0367 (1.00) |
11p loss | 20 (65%) | 11 |
0.291 (1.00) |
0.704 (1.00) |
0.716 (1.00) |
0.603 (1.00) |
1 (1.00) |
11q loss | 24 (77%) | 7 |
0.193 (1.00) |
0.666 (1.00) |
0.685 (1.00) |
0.519 (1.00) |
0.55 (1.00) |
13q loss | 24 (77%) | 7 |
0.287 (1.00) |
1 (1.00) |
0.685 (1.00) |
0.519 (1.00) |
1 (1.00) |
14q loss | 5 (16%) | 26 |
0.154 (1.00) |
0.201 (1.00) |
1 (1.00) |
0.219 (1.00) |
1 (1.00) |
15q loss | 8 (26%) | 23 |
0.667 (1.00) |
0.154 (1.00) |
0.685 (1.00) |
0.584 (1.00) |
1 (1.00) |
16p loss | 10 (32%) | 21 |
0.112 (1.00) |
0.983 (1.00) |
0.704 (1.00) |
0.261 (1.00) |
0.0267 (1.00) |
16q loss | 10 (32%) | 21 |
0.112 (1.00) |
0.981 (1.00) |
0.704 (1.00) |
0.261 (1.00) |
0.0267 (1.00) |
17p loss | 10 (32%) | 21 |
0.29 (1.00) |
1 (1.00) |
0.458 (1.00) |
1 (1.00) |
1 (1.00) |
17q loss | 5 (16%) | 26 |
0.484 (1.00) |
0.402 (1.00) |
1 (1.00) |
0.426 (1.00) |
1 (1.00) |
18p loss | 25 (81%) | 6 |
0.176 (1.00) |
0.106 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
18q loss | 26 (84%) | 5 |
0.132 (1.00) |
0.133 (1.00) |
1 (1.00) |
0.426 (1.00) |
1 (1.00) |
19p loss | 14 (45%) | 17 |
0.21 (1.00) |
0.0206 (1.00) |
0.285 (1.00) |
0.576 (1.00) |
0.576 (1.00) |
19q loss | 17 (55%) | 14 |
0.233 (1.00) |
0.0025 (0.685) |
0.479 (1.00) |
0.603 (1.00) |
1 (1.00) |
20p loss | 8 (26%) | 23 |
0.365 (1.00) |
0.155 (1.00) |
0.685 (1.00) |
0.219 (1.00) |
1 (1.00) |
20q loss | 3 (10%) | 28 |
0.0411 (1.00) |
0.85 (1.00) |
0.226 (1.00) |
0.271 (1.00) |
|
22q loss | 15 (48%) | 16 |
0.373 (1.00) |
0.71 (1.00) |
0.479 (1.00) |
1 (1.00) |
0.6 (1.00) |
xq loss | 7 (23%) | 24 |
0.162 (1.00) |
0.816 (1.00) |
0.0373 (1.00) |
0.541 (1.00) |
0.55 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = TGCT-TP.merged_data.txt
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Number of patients = 31
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Number of significantly arm-level cnvs = 55
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Number of selected clinical features = 5
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Exclude regions 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.