This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 56 arm-level events and 2 clinical features across 14 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 | ||
nCNV (%) | nWild-Type | logrank test | t-test | |
1P GAIN MUTATION ANALYSIS | 7 (50%) | 7 |
0.74 (1.00) |
0.272 (1.00) |
1Q GAIN MUTATION ANALYSIS | 8 (57%) | 6 |
0.282 (1.00) |
0.116 (1.00) |
2P GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.0878 (1.00) |
0.4 (1.00) |
2Q GAIN MUTATION ANALYSIS | 3 (21%) | 11 |
0.364 (1.00) |
0.141 (1.00) |
3P GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.436 (1.00) |
0.93 (1.00) |
3Q GAIN MUTATION ANALYSIS | 5 (36%) | 9 |
0.493 (1.00) |
0.427 (1.00) |
4P GAIN MUTATION ANALYSIS | 3 (21%) | 11 |
0.963 (1.00) |
0.73 (1.00) |
5P GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.548 (1.00) |
0.354 (1.00) |
6P GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.986 (1.00) |
0.859 (1.00) |
6Q GAIN MUTATION ANALYSIS | 5 (36%) | 9 |
0.46 (1.00) |
0.581 (1.00) |
8P GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.115 (1.00) |
0.742 (1.00) |
8Q GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.256 (1.00) |
0.575 (1.00) |
10P GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.179 (1.00) |
0.205 (1.00) |
10Q GAIN MUTATION ANALYSIS | 5 (36%) | 9 |
0.887 (1.00) |
0.841 (1.00) |
11Q GAIN MUTATION ANALYSIS | 3 (21%) | 11 |
0.465 (1.00) |
0.7 (1.00) |
12P GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.289 (1.00) |
0.686 (1.00) |
12Q GAIN MUTATION ANALYSIS | 3 (21%) | 11 |
0.177 (1.00) |
0.356 (1.00) |
13Q GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.152 (1.00) |
0.685 (1.00) |
16P GAIN MUTATION ANALYSIS | 3 (21%) | 11 |
0.361 (1.00) |
0.13 (1.00) |
17P GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.652 (1.00) |
0.207 (1.00) |
17Q GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.844 (1.00) |
0.686 (1.00) |
18P GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.367 (1.00) |
0.0641 (1.00) |
19P GAIN MUTATION ANALYSIS | 6 (43%) | 8 |
0.912 (1.00) |
0.994 (1.00) |
19Q GAIN MUTATION ANALYSIS | 7 (50%) | 7 |
0.586 (1.00) |
0.678 (1.00) |
20P GAIN MUTATION ANALYSIS | 9 (64%) | 5 |
0.942 (1.00) |
0.169 (1.00) |
20Q GAIN MUTATION ANALYSIS | 11 (79%) | 3 |
0.392 (1.00) |
0.116 (1.00) |
21Q GAIN MUTATION ANALYSIS | 7 (50%) | 7 |
0.71 (1.00) |
0.00744 (0.833) |
XQ GAIN MUTATION ANALYSIS | 4 (29%) | 10 |
0.768 (1.00) |
0.893 (1.00) |
3P LOSS MUTATION ANALYSIS | 5 (36%) | 9 |
0.364 (1.00) |
0.588 (1.00) |
3Q LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.314 (1.00) |
0.736 (1.00) |
4P LOSS MUTATION ANALYSIS | 6 (43%) | 8 |
0.144 (1.00) |
0.293 (1.00) |
4Q LOSS MUTATION ANALYSIS | 8 (57%) | 6 |
0.0314 (1.00) |
0.151 (1.00) |
5Q LOSS MUTATION ANALYSIS | 5 (36%) | 9 |
0.202 (1.00) |
0.531 (1.00) |
7P LOSS MUTATION ANALYSIS | 3 (21%) | 11 |
0.338 (1.00) |
0.354 (1.00) |
7Q LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.923 (1.00) |
0.133 (1.00) |
8P LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.323 (1.00) |
0.886 (1.00) |
9P LOSS MUTATION ANALYSIS | 7 (50%) | 7 |
0.127 (1.00) |
0.396 (1.00) |
9Q LOSS MUTATION ANALYSIS | 8 (57%) | 6 |
0.434 (1.00) |
0.159 (1.00) |
10P LOSS MUTATION ANALYSIS | 3 (21%) | 11 |
0.136 (1.00) |
0.0596 (1.00) |
11P LOSS MUTATION ANALYSIS | 7 (50%) | 7 |
0.015 (1.00) |
0.47 (1.00) |
11Q LOSS MUTATION ANALYSIS | 6 (43%) | 8 |
0.144 (1.00) |
0.774 (1.00) |
12P LOSS MUTATION ANALYSIS | 5 (36%) | 9 |
0.903 (1.00) |
0.791 (1.00) |
12Q LOSS MUTATION ANALYSIS | 3 (21%) | 11 |
0.823 (1.00) |
0.937 (1.00) |
13Q LOSS MUTATION ANALYSIS | 6 (43%) | 8 |
0.822 (1.00) |
0.894 (1.00) |
14Q LOSS MUTATION ANALYSIS | 9 (64%) | 5 |
0.331 (1.00) |
0.47 (1.00) |
15Q LOSS MUTATION ANALYSIS | 9 (64%) | 5 |
0.463 (1.00) |
0.223 (1.00) |
16P LOSS MUTATION ANALYSIS | 7 (50%) | 7 |
0.434 (1.00) |
0.435 (1.00) |
16Q LOSS MUTATION ANALYSIS | 9 (64%) | 5 |
0.202 (1.00) |
0.928 (1.00) |
17P LOSS MUTATION ANALYSIS | 6 (43%) | 8 |
0.667 (1.00) |
0.958 (1.00) |
18P LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.436 (1.00) |
0.93 (1.00) |
18Q LOSS MUTATION ANALYSIS | 3 (21%) | 11 |
0.436 (1.00) |
0.983 (1.00) |
19P LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.56 (1.00) |
0.971 (1.00) |
19Q LOSS MUTATION ANALYSIS | 4 (29%) | 10 |
0.69 (1.00) |
0.603 (1.00) |
21Q LOSS MUTATION ANALYSIS | 5 (36%) | 9 |
0.765 (1.00) |
0.0336 (1.00) |
22Q LOSS MUTATION ANALYSIS | 8 (57%) | 6 |
0.84 (1.00) |
0.554 (1.00) |
XQ LOSS MUTATION ANALYSIS | 3 (21%) | 11 |
0.0679 (1.00) |
0.247 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Clinical data file = UCS-TP.clin.merged.picked.txt
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Number of patients = 14
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Number of significantly arm-level cnvs = 56
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Number of selected clinical features = 2
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Exclude regions 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 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.