This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 42 focal events and 6 clinical features across 33 patients, no significant finding detected with Q value < 0.25.
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No focal cnvs related to clinical features.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
GENDER | ||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
amp 1q22 | 6 (18%) | 27 |
0.398 (1.00) |
0.0765 (1.00) |
0.849 (1.00) |
0.83 (1.00) |
0.557 (1.00) |
1 (1.00) |
amp 4p16 3 | 16 (48%) | 17 |
0.096 (1.00) |
0.916 (1.00) |
0.9 (1.00) |
0.763 (1.00) |
1 (1.00) |
1 (1.00) |
amp 4q35 1 | 12 (36%) | 21 |
0.108 (1.00) |
0.343 (1.00) |
0.5 (1.00) |
0.248 (1.00) |
1 (1.00) |
0.481 (1.00) |
amp 5p15 33 | 23 (70%) | 10 |
0.966 (1.00) |
0.733 (1.00) |
1 (1.00) |
1 (1.00) |
0.55 (1.00) |
0.708 (1.00) |
amp 5q35 3 | 23 (70%) | 10 |
0.0154 (1.00) |
0.485 (1.00) |
0.925 (1.00) |
0.4 (1.00) |
1 (1.00) |
0.057 (1.00) |
amp 6p21 31 | 5 (15%) | 28 |
0.252 (1.00) |
0.214 (1.00) |
0.531 (1.00) |
0.424 (1.00) |
1 (1.00) |
1 (1.00) |
amp 6q24 3 | 7 (21%) | 26 |
0.623 (1.00) |
0.876 (1.00) |
1 (1.00) |
0.66 (1.00) |
0.557 (1.00) |
1 (1.00) |
amp 7p22 1 | 18 (55%) | 15 |
0.194 (1.00) |
0.223 (1.00) |
0.772 (1.00) |
0.656 (1.00) |
0.13 (1.00) |
1 (1.00) |
amp 9q31 3 | 9 (27%) | 24 |
0.462 (1.00) |
0.558 (1.00) |
0.404 (1.00) |
0.394 (1.00) |
0.069 (1.00) |
0.438 (1.00) |
amp 12q14 1 | 25 (76%) | 8 |
0.462 (1.00) |
0.926 (1.00) |
0.715 (1.00) |
0.83 (1.00) |
0.557 (1.00) |
0.688 (1.00) |
amp 14q11 2 | 7 (21%) | 26 |
0.0191 (1.00) |
0.442 (1.00) |
0.00405 (1.00) |
0.0779 (1.00) |
0.0181 (1.00) |
1 (1.00) |
amp 16p13 3 | 19 (58%) | 14 |
0.229 (1.00) |
0.987 (1.00) |
0.9 (1.00) |
1 (1.00) |
0.632 (1.00) |
0.296 (1.00) |
amp 16q22 1 | 17 (52%) | 16 |
0.293 (1.00) |
0.88 (1.00) |
1 (1.00) |
1 (1.00) |
0.602 (1.00) |
0.0844 (1.00) |
amp 16q24 2 | 17 (52%) | 16 |
0.293 (1.00) |
0.88 (1.00) |
1 (1.00) |
1 (1.00) |
0.602 (1.00) |
0.0844 (1.00) |
amp 17q25 3 | 5 (15%) | 28 |
0.353 (1.00) |
0.686 (1.00) |
0.693 (1.00) |
0.788 (1.00) |
0.538 (1.00) |
0.335 (1.00) |
amp 19p13 12 | 19 (58%) | 14 |
0.607 (1.00) |
0.553 (1.00) |
0.665 (1.00) |
1 (1.00) |
0.632 (1.00) |
0.728 (1.00) |
amp 19q12 | 18 (55%) | 15 |
0.454 (1.00) |
0.31 (1.00) |
0.643 (1.00) |
0.883 (1.00) |
0.613 (1.00) |
0.491 (1.00) |
amp xp11 22 | 12 (36%) | 21 |
0.507 (1.00) |
0.0247 (1.00) |
0.95 (1.00) |
1 (1.00) |
1 (1.00) |
0.721 (1.00) |
amp xq28 | 13 (39%) | 20 |
0.113 (1.00) |
0.0114 (1.00) |
0.706 (1.00) |
0.883 (1.00) |
1 (1.00) |
0.728 (1.00) |
del 1p36 23 | 17 (52%) | 16 |
0.486 (1.00) |
0.399 (1.00) |
0.523 (1.00) |
0.679 (1.00) |
0.602 (1.00) |
1 (1.00) |
del 1q43 | 9 (27%) | 24 |
0.798 (1.00) |
0.0863 (1.00) |
0.476 (1.00) |
0.711 (1.00) |
0.284 (1.00) |
1 (1.00) |
del 2q22 1 | 12 (36%) | 21 |
0.862 (1.00) |
0.773 (1.00) |
0.76 (1.00) |
1 (1.00) |
0.611 (1.00) |
1 (1.00) |
del 2q37 3 | 12 (36%) | 21 |
0.308 (1.00) |
0.793 (1.00) |
1 (1.00) |
0.559 (1.00) |
1 (1.00) |
1 (1.00) |
del 3q13 31 | 10 (30%) | 23 |
0.0972 (1.00) |
0.349 (1.00) |
0.343 (1.00) |
0.4 (1.00) |
0.284 (1.00) |
1 (1.00) |
del 4q34 3 | 11 (33%) | 22 |
0.043 (1.00) |
0.489 (1.00) |
0.404 (1.00) |
0.117 (1.00) |
1 (1.00) |
1 (1.00) |
del 4q35 1 | 12 (36%) | 21 |
0.108 (1.00) |
0.902 (1.00) |
0.0453 (1.00) |
0.117 (1.00) |
1 (1.00) |
0.481 (1.00) |
del 6p24 3 | 6 (18%) | 27 |
0.644 (1.00) |
0.847 (1.00) |
0.502 (1.00) |
0.83 (1.00) |
0.169 (1.00) |
1 (1.00) |
del 6q26 | 10 (30%) | 23 |
0.129 (1.00) |
0.625 (1.00) |
0.195 (1.00) |
0.172 (1.00) |
0.0952 (1.00) |
0.708 (1.00) |
del 7q32 3 | 4 (12%) | 29 |
0.568 (1.00) |
0.655 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.601 (1.00) |
del 9p23 | 10 (30%) | 23 |
0.237 (1.00) |
0.703 (1.00) |
0.783 (1.00) |
0.644 (1.00) |
1 (1.00) |
0.259 (1.00) |
del 9p21 3 | 13 (39%) | 20 |
0.113 (1.00) |
0.759 (1.00) |
0.95 (1.00) |
0.883 (1.00) |
0.632 (1.00) |
0.0324 (1.00) |
del 10q23 1 | 7 (21%) | 26 |
0.0903 (1.00) |
0.863 (1.00) |
0.712 (1.00) |
0.832 (1.00) |
0.225 (1.00) |
0.398 (1.00) |
del 11p15 5 | 16 (48%) | 17 |
0.63 (1.00) |
0.68 (1.00) |
0.72 (1.00) |
1 (1.00) |
0.315 (1.00) |
0.494 (1.00) |
del 11q14 1 | 15 (45%) | 18 |
0.965 (1.00) |
0.643 (1.00) |
0.364 (1.00) |
0.883 (1.00) |
1 (1.00) |
0.732 (1.00) |
del 13q14 2 | 19 (58%) | 14 |
0.119 (1.00) |
0.538 (1.00) |
0.425 (1.00) |
0.448 (1.00) |
0.613 (1.00) |
0.728 (1.00) |
del 14q21 2 | 5 (15%) | 28 |
0.599 (1.00) |
0.434 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.175 (1.00) |
del 17q11 2 | 13 (39%) | 20 |
0.0524 (1.00) |
0.474 (1.00) |
0.195 (1.00) |
0.172 (1.00) |
0.611 (1.00) |
0.296 (1.00) |
del 17q21 31 | 12 (36%) | 21 |
0.19 (1.00) |
0.476 (1.00) |
0.404 (1.00) |
0.394 (1.00) |
0.584 (1.00) |
0.481 (1.00) |
del 17q24 2 | 13 (39%) | 20 |
0.249 (1.00) |
0.536 (1.00) |
0.069 (1.00) |
0.409 (1.00) |
0.611 (1.00) |
0.728 (1.00) |
del 18q21 2 | 17 (52%) | 16 |
0.594 (1.00) |
0.104 (1.00) |
0.825 (1.00) |
0.875 (1.00) |
1 (1.00) |
0.303 (1.00) |
del 20p12 1 | 7 (21%) | 26 |
0.0102 (1.00) |
0.401 (1.00) |
0.502 (1.00) |
0.554 (1.00) |
0.557 (1.00) |
0.398 (1.00) |
del 22q12 1 | 18 (55%) | 15 |
0.0648 (1.00) |
0.156 (1.00) |
0.215 (1.00) |
0.679 (1.00) |
0.602 (1.00) |
0.491 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = ACC-TP.merged_data.txt
-
Number of patients = 33
-
Number of significantly focal cnvs = 42
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Number of selected clinical features = 6
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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 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.