(NF1_Any_Mutants cohort)
This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 38 arm-level results and 7 clinical features across 25 patients, one significant finding detected with Q value < 0.25.
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9p loss cnv correlated to 'AGE'.
Table 1. Get Full Table Overview of the association between significant copy number variation of 38 arm-level results and 7 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, one significant finding detected.
Clinical Features |
Time to Death |
AGE |
PRIMARY SITE OF DISEASE |
GENDER |
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 | Chi-square test | |
9p loss | 16 (64%) | 9 |
0.33 (1.00) |
0.000199 (0.0423) |
0.412 (1.00) |
0.661 (1.00) |
0.893 (1.00) |
0.719 (1.00) |
|
1p gain | 4 (16%) | 21 |
0.632 (1.00) |
0.973 (1.00) |
1 (1.00) |
1 (1.00) |
0.481 (1.00) |
||
1q gain | 9 (36%) | 16 |
0.423 (1.00) |
0.433 (1.00) |
0.353 (1.00) |
0.394 (1.00) |
0.1 (1.00) |
0.349 (1.00) |
|
3p gain | 4 (16%) | 21 |
0.611 (1.00) |
0.61 (1.00) |
1 (1.00) |
1 (1.00) |
0.481 (1.00) |
0.151 (1.00) |
|
3q gain | 5 (20%) | 20 |
0.989 (1.00) |
0.963 (1.00) |
0.812 (1.00) |
1 (1.00) |
0.352 (1.00) |
0.117 (1.00) |
|
5p gain | 4 (16%) | 21 |
0.711 (1.00) |
0.626 (1.00) |
0.57 (1.00) |
1 (1.00) |
0.0803 (1.00) |
||
5q gain | 3 (12%) | 22 |
0.97 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.188 (1.00) |
||
6p gain | 11 (44%) | 14 |
0.682 (1.00) |
0.327 (1.00) |
0.367 (1.00) |
1 (1.00) |
0.323 (1.00) |
0.316 (1.00) |
|
7p gain | 10 (40%) | 15 |
0.181 (1.00) |
0.756 (1.00) |
0.196 (1.00) |
0.194 (1.00) |
0.533 (1.00) |
0.812 (1.00) |
|
7q gain | 9 (36%) | 16 |
0.197 (1.00) |
0.827 (1.00) |
0.547 (1.00) |
0.087 (1.00) |
0.406 (1.00) |
0.512 (1.00) |
|
8p gain | 5 (20%) | 20 |
0.514 (1.00) |
0.951 (1.00) |
0.812 (1.00) |
0.283 (1.00) |
0.838 (1.00) |
0.17 (1.00) |
|
8q gain | 6 (24%) | 19 |
0.665 (1.00) |
0.949 (1.00) |
0.576 (1.00) |
0.344 (1.00) |
0.654 (1.00) |
0.349 (1.00) |
|
14q gain | 4 (16%) | 21 |
0.504 (1.00) |
0.496 (1.00) |
0.57 (1.00) |
0.605 (1.00) |
0.788 (1.00) |
||
15q gain | 4 (16%) | 21 |
0.957 (1.00) |
0.383 (1.00) |
0.0808 (1.00) |
1 (1.00) |
0.188 (1.00) |
||
17q gain | 5 (20%) | 20 |
0.0124 (1.00) |
0.17 (1.00) |
0.347 (1.00) |
1 (1.00) |
0.292 (1.00) |
0.912 (1.00) |
|
18p gain | 3 (12%) | 22 |
0.0422 (1.00) |
0.25 (1.00) |
0.565 (1.00) |
1 (1.00) |
0.0378 (1.00) |
0.42 (1.00) |
|
20p gain | 6 (24%) | 19 |
0.289 (1.00) |
0.144 (1.00) |
0.107 (1.00) |
0.0593 (1.00) |
0.401 (1.00) |
0.349 (1.00) |
|
20q gain | 8 (32%) | 17 |
0.71 (1.00) |
0.487 (1.00) |
0.621 (1.00) |
0.359 (1.00) |
0.144 (1.00) |
0.487 (1.00) |
|
22q gain | 9 (36%) | 16 |
0.187 (1.00) |
0.462 (1.00) |
1 (1.00) |
0.394 (1.00) |
0.202 (1.00) |
0.577 (1.00) |
|
1p loss | 3 (12%) | 22 |
0.214 (1.00) |
0.152 (1.00) |
1 (1.00) |
1 (1.00) |
0.188 (1.00) |
||
4p loss | 4 (16%) | 21 |
0.22 (1.00) |
0.275 (1.00) |
0.57 (1.00) |
0.481 (1.00) |
0.587 (1.00) |
||
4q loss | 4 (16%) | 21 |
0.997 (1.00) |
0.204 (1.00) |
0.57 (1.00) |
1 (1.00) |
0.188 (1.00) |
||
5p loss | 4 (16%) | 21 |
0.139 (1.00) |
0.567 (1.00) |
0.204 (1.00) |
0.269 (1.00) |
0.684 (1.00) |
0.77 (1.00) |
|
5q loss | 6 (24%) | 19 |
0.357 (1.00) |
0.959 (1.00) |
0.138 (1.00) |
0.129 (1.00) |
0.133 (1.00) |
0.738 (1.00) |
|
6q loss | 10 (40%) | 15 |
0.593 (1.00) |
0.0557 (1.00) |
0.577 (1.00) |
0.667 (1.00) |
0.105 (1.00) |
0.42 (1.00) |
|
9q loss | 13 (52%) | 12 |
0.56 (1.00) |
0.0565 (1.00) |
0.249 (1.00) |
0.673 (1.00) |
0.784 (1.00) |
0.294 (1.00) |
|
10p loss | 8 (32%) | 17 |
0.601 (1.00) |
1 (1.00) |
0.359 (1.00) |
0.401 (1.00) |
0.986 (1.00) |
||
10q loss | 8 (32%) | 17 |
0.205 (1.00) |
1 (1.00) |
0.359 (1.00) |
0.401 (1.00) |
0.752 (1.00) |
||
11p loss | 9 (36%) | 16 |
0.62 (1.00) |
0.377 (1.00) |
0.152 (1.00) |
0.394 (1.00) |
0.0303 (1.00) |
0.383 (1.00) |
|
11q loss | 9 (36%) | 16 |
0.975 (1.00) |
0.472 (1.00) |
0.152 (1.00) |
0.087 (1.00) |
0.714 (1.00) |
0.188 (1.00) |
|
12q loss | 3 (12%) | 22 |
0.875 (1.00) |
0.763 (1.00) |
1 (1.00) |
0.209 (1.00) |
0.188 (1.00) |
||
13q loss | 6 (24%) | 19 |
0.197 (1.00) |
0.885 (1.00) |
0.689 (1.00) |
1 (1.00) |
0.0898 (1.00) |
0.236 (1.00) |
|
14q loss | 3 (12%) | 22 |
0.996 (1.00) |
1 (1.00) |
0.231 (1.00) |
0.481 (1.00) |
0.587 (1.00) |
||
16q loss | 4 (16%) | 21 |
0.944 (1.00) |
0.626 (1.00) |
1 (1.00) |
1 (1.00) |
0.326 (1.00) |
||
17p loss | 5 (20%) | 20 |
0.485 (1.00) |
0.755 (1.00) |
0.812 (1.00) |
1 (1.00) |
0.135 (1.00) |
0.278 (1.00) |
|
18p loss | 4 (16%) | 21 |
0.559 (1.00) |
1 (1.00) |
0.57 (1.00) |
0.605 (1.00) |
0.716 (1.00) |
||
18q loss | 3 (12%) | 22 |
0.61 (1.00) |
0.763 (1.00) |
1 (1.00) |
0.684 (1.00) |
0.854 (1.00) |
||
21q loss | 7 (28%) | 18 |
0.571 (1.00) |
0.978 (1.00) |
0.719 (1.00) |
0.64 (1.00) |
0.87 (1.00) |
0.31 (1.00) |
P value = 0.000199 (t-test), Q value = 0.042
Table S1. Gene #25: '9p loss mutation analysis' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 25 | 66.3 (14.3) |
9P LOSS MUTATED | 16 | 73.2 (11.7) |
9P LOSS WILD-TYPE | 9 | 53.9 (9.3) |
Figure S1. Get High-res Image Gene #25: '9p loss mutation analysis' versus Clinical Feature #2: 'AGE'

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Mutation data file = broad_values_by_arm.mutsig.cluster.txt
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Clinical data file = SKCM-NF1_Any_Mutants.clin.merged.picked.txt
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Number of patients = 25
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Number of significantly arm-level cnvs = 38
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Number of selected clinical features = 7
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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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.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.