This pipeline computes the correlation between significant copy number variation (cnv focal) genes and selected clinical features.
Testing the association between copy number variation 27 focal events and 5 clinical features across 10 patients, one significant finding detected with Q value < 0.25.
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del_20p12.1 cnv correlated to 'Time to Death'.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
GENDER | ||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | NULL | Fisher's exact test | |
del 20p12 1 | 3 (30%) | 7 |
0.00183 (0.19) |
0.848 (1.00) |
0.5 (1.00) |
1 (1.00) |
|
amp 4p16 3 | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
amp 4q35 1 | 4 (40%) | 6 |
0.0784 (1.00) |
0.424 (1.00) |
0.5 (1.00) |
0.524 (1.00) |
|
amp 5p15 33 | 6 (60%) | 4 |
0.467 (1.00) |
0.507 (1.00) |
1 (1.00) |
0.524 (1.00) |
|
amp 5q35 3 | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
amp 7p22 1 | 4 (40%) | 6 |
0.0933 (1.00) |
0.0542 (1.00) |
1 (1.00) |
1 (1.00) |
|
amp 12q14 1 | 7 (70%) | 3 |
0.87 (1.00) |
0.956 (1.00) |
1 (1.00) |
1 (1.00) |
|
amp 14q11 2 | 4 (40%) | 6 |
0.596 (1.00) |
0.907 (1.00) |
0.143 (1.00) |
1 (1.00) |
|
amp 16p13 3 | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
amp 16q22 1 | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
amp 16q24 2 | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
amp 19p13 12 | 7 (70%) | 3 |
0.87 (1.00) |
0.956 (1.00) |
1 (1.00) |
1 (1.00) |
|
amp 19q12 | 7 (70%) | 3 |
0.87 (1.00) |
0.956 (1.00) |
1 (1.00) |
1 (1.00) |
|
del 1p36 23 | 7 (70%) | 3 |
0.514 (1.00) |
0.672 (1.00) |
0.5 (1.00) |
1 (1.00) |
|
del 1q43 | 3 (30%) | 7 |
0.514 (1.00) |
0.416 (1.00) |
1 (1.00) |
||
del 4q34 3 | 4 (40%) | 6 |
0.0221 (1.00) |
0.516 (1.00) |
0.127 (1.00) |
0.524 (1.00) |
|
del 4q35 1 | 3 (30%) | 7 |
0.115 (1.00) |
0.421 (1.00) |
1 (1.00) |
||
del 7q32 3 | 3 (30%) | 7 |
0.596 (1.00) |
0.561 (1.00) |
1 (1.00) |
||
del 9p21 3 | 4 (40%) | 6 |
0.149 (1.00) |
0.935 (1.00) |
0.143 (1.00) |
0.524 (1.00) |
|
del 11p15 5 | 5 (50%) | 5 |
0.899 (1.00) |
0.769 (1.00) |
0.381 (1.00) |
1 (1.00) |
|
del 11q14 1 | 5 (50%) | 5 |
0.899 (1.00) |
0.769 (1.00) |
0.381 (1.00) |
1 (1.00) |
|
del 13q14 2 | 5 (50%) | 5 |
0.381 (1.00) |
0.883 (1.00) |
1 (1.00) |
1 (1.00) |
|
del 17q11 2 | 4 (40%) | 6 |
0.205 (1.00) |
0.258 (1.00) |
0.5 (1.00) |
1 (1.00) |
|
del 17q21 31 | 3 (30%) | 7 |
0.497 (1.00) |
0.399 (1.00) |
1 (1.00) |
||
del 17q24 2 | 5 (50%) | 5 |
0.308 (1.00) |
0.686 (1.00) |
0.127 (1.00) |
0.206 (1.00) |
|
del 18q21 2 | 5 (50%) | 5 |
0.449 (1.00) |
0.218 (1.00) |
0.381 (1.00) |
1 (1.00) |
|
del 22q12 1 | 6 (60%) | 4 |
0.994 (1.00) |
0.872 (1.00) |
1 (1.00) |
0.524 (1.00) |
P value = 0.00183 (logrank test), Q value = 0.19
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 4 | 10.2 - 121.2 (26.3) |
DEL PEAK 25(20P12.1) MUTATED | 3 | 3 | 11.3 - 18.1 (18.1) |
DEL PEAK 25(20P12.1) WILD-TYPE | 7 | 1 | 10.2 - 121.2 (37.1) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = ACC-TP.merged_data.txt
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Number of patients = 10
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Number of significantly focal cnvs = 27
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Number of selected clinical features = 5
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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.