This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 61 arm-level results and 3 clinical features across 58 patients, 2 significant findings detected with Q value < 0.25.
-
18p loss cnv correlated to 'AGE'.
-
18q loss cnv correlated to 'AGE'.
Clinical Features |
Time to Death |
AGE | GENDER | ||
nCNV (%) | nWild-Type | logrank test | t-test | Fisher's exact test | |
18p loss | 7 (12%) | 51 |
0.0228 (1.00) |
0.000543 (0.0971) |
0.696 (1.00) |
18q loss | 9 (16%) | 49 |
0.207 (1.00) |
0.00106 (0.189) |
0.71 (1.00) |
1p gain | 9 (16%) | 49 |
0.816 (1.00) |
0.135 (1.00) |
1 (1.00) |
1q gain | 32 (55%) | 26 |
0.733 (1.00) |
0.709 (1.00) |
0.18 (1.00) |
2p gain | 8 (14%) | 50 |
0.749 (1.00) |
0.113 (1.00) |
0.124 (1.00) |
2q gain | 7 (12%) | 51 |
0.877 (1.00) |
0.223 (1.00) |
0.0864 (1.00) |
4p gain | 4 (7%) | 54 |
0.939 (1.00) |
0.456 (1.00) |
0.13 (1.00) |
5p gain | 18 (31%) | 40 |
0.295 (1.00) |
0.22 (1.00) |
1 (1.00) |
5q gain | 13 (22%) | 45 |
0.193 (1.00) |
0.195 (1.00) |
1 (1.00) |
6p gain | 13 (22%) | 45 |
0.186 (1.00) |
0.575 (1.00) |
0.751 (1.00) |
6q gain | 8 (14%) | 50 |
0.00649 (1.00) |
0.51 (1.00) |
0.698 (1.00) |
7p gain | 18 (31%) | 40 |
0.83 (1.00) |
0.545 (1.00) |
0.237 (1.00) |
7q gain | 19 (33%) | 39 |
0.843 (1.00) |
0.532 (1.00) |
0.254 (1.00) |
8p gain | 11 (19%) | 47 |
0.11 (1.00) |
0.581 (1.00) |
0.296 (1.00) |
8q gain | 29 (50%) | 29 |
0.26 (1.00) |
0.729 (1.00) |
0.585 (1.00) |
9p gain | 3 (5%) | 55 |
0.826 (1.00) |
0.547 (1.00) |
|
9q gain | 3 (5%) | 55 |
0.826 (1.00) |
0.547 (1.00) |
|
10p gain | 5 (9%) | 53 |
0.17 (1.00) |
0.385 (1.00) |
0.341 (1.00) |
12q gain | 3 (5%) | 55 |
0.898 (1.00) |
0.547 (1.00) |
|
15q gain | 5 (9%) | 53 |
0.822 (1.00) |
0.324 (1.00) |
0.341 (1.00) |
16p gain | 3 (5%) | 55 |
0.00854 (1.00) |
0.147 (1.00) |
1 (1.00) |
17p gain | 3 (5%) | 55 |
0.473 (1.00) |
0.708 (1.00) |
0.547 (1.00) |
17q gain | 16 (28%) | 42 |
0.0885 (1.00) |
0.864 (1.00) |
0.546 (1.00) |
18q gain | 3 (5%) | 55 |
0.931 (1.00) |
0.621 (1.00) |
1 (1.00) |
19p gain | 5 (9%) | 53 |
0.751 (1.00) |
0.622 (1.00) |
0.0528 (1.00) |
19q gain | 7 (12%) | 51 |
0.786 (1.00) |
0.826 (1.00) |
0.0864 (1.00) |
20p gain | 12 (21%) | 46 |
0.244 (1.00) |
0.0839 (1.00) |
0.32 (1.00) |
20q gain | 13 (22%) | 45 |
0.369 (1.00) |
0.0571 (1.00) |
0.515 (1.00) |
21q gain | 4 (7%) | 54 |
0.69 (1.00) |
0.616 (1.00) |
0.13 (1.00) |
22q gain | 6 (10%) | 52 |
0.165 (1.00) |
0.0665 (1.00) |
0.657 (1.00) |
Xq gain | 4 (7%) | 54 |
0.233 (1.00) |
0.222 (1.00) |
1 (1.00) |
1p loss | 10 (17%) | 48 |
0.672 (1.00) |
0.54 (1.00) |
0.471 (1.00) |
1q loss | 3 (5%) | 55 |
0.857 (1.00) |
0.132 (1.00) |
1 (1.00) |
2q loss | 3 (5%) | 55 |
0.0103 (1.00) |
1 (1.00) |
|
3p loss | 5 (9%) | 53 |
0.731 (1.00) |
0.766 (1.00) |
0.341 (1.00) |
3q loss | 3 (5%) | 55 |
0.638 (1.00) |
0.235 (1.00) |
0.547 (1.00) |
4p loss | 9 (16%) | 49 |
0.517 (1.00) |
0.87 (1.00) |
1 (1.00) |
4q loss | 14 (24%) | 44 |
0.526 (1.00) |
0.768 (1.00) |
0.544 (1.00) |
5q loss | 4 (7%) | 54 |
0.00996 (1.00) |
0.956 (1.00) |
1 (1.00) |
6q loss | 9 (16%) | 49 |
0.253 (1.00) |
0.775 (1.00) |
0.262 (1.00) |
7p loss | 4 (7%) | 54 |
0.486 (1.00) |
0.665 (1.00) |
0.13 (1.00) |
7q loss | 7 (12%) | 51 |
0.944 (1.00) |
0.43 (1.00) |
0.241 (1.00) |
8p loss | 25 (43%) | 33 |
0.407 (1.00) |
0.544 (1.00) |
0.783 (1.00) |
8q loss | 7 (12%) | 51 |
0.652 (1.00) |
0.655 (1.00) |
0.696 (1.00) |
9p loss | 13 (22%) | 45 |
0.761 (1.00) |
0.391 (1.00) |
0.751 (1.00) |
9q loss | 11 (19%) | 47 |
0.621 (1.00) |
0.408 (1.00) |
0.729 (1.00) |
10p loss | 3 (5%) | 55 |
0.71 (1.00) |
0.029 (1.00) |
0.547 (1.00) |
10q loss | 12 (21%) | 46 |
0.525 (1.00) |
0.581 (1.00) |
0.741 (1.00) |
11p loss | 5 (9%) | 53 |
0.685 (1.00) |
0.276 (1.00) |
0.341 (1.00) |
11q loss | 8 (14%) | 50 |
0.252 (1.00) |
0.369 (1.00) |
1 (1.00) |
12p loss | 4 (7%) | 54 |
0.345 (1.00) |
0.547 (1.00) |
0.615 (1.00) |
12q loss | 3 (5%) | 55 |
0.365 (1.00) |
0.556 (1.00) |
0.547 (1.00) |
13q loss | 20 (34%) | 38 |
0.225 (1.00) |
0.157 (1.00) |
0.776 (1.00) |
14q loss | 19 (33%) | 39 |
0.672 (1.00) |
0.564 (1.00) |
0.569 (1.00) |
15q loss | 7 (12%) | 51 |
0.916 (1.00) |
0.365 (1.00) |
1 (1.00) |
16p loss | 11 (19%) | 47 |
0.397 (1.00) |
0.882 (1.00) |
1 (1.00) |
16q loss | 18 (31%) | 40 |
0.866 (1.00) |
0.896 (1.00) |
0.395 (1.00) |
17p loss | 24 (41%) | 34 |
0.23 (1.00) |
0.479 (1.00) |
0.786 (1.00) |
19p loss | 3 (5%) | 55 |
0.0697 (1.00) |
0.805 (1.00) |
0.547 (1.00) |
21q loss | 8 (14%) | 50 |
0.0767 (1.00) |
0.943 (1.00) |
0.0413 (1.00) |
22q loss | 9 (16%) | 49 |
0.617 (1.00) |
0.688 (1.00) |
0.0593 (1.00) |
P value = 0.000543 (t-test), Q value = 0.097
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 54 | 61.8 (14.1) |
18P LOSS MUTATED | 7 | 73.6 (6.4) |
18P LOSS WILD-TYPE | 47 | 60.0 (14.2) |
P value = 0.00106 (t-test), Q value = 0.19
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 54 | 61.8 (14.1) |
18Q LOSS MUTATED | 9 | 71.6 (6.9) |
18Q LOSS WILD-TYPE | 45 | 59.8 (14.4) |
-
Mutation data file = broad_values_by_arm.mutsig.cluster.txt
-
Clinical data file = LIHC.clin.merged.picked.txt
-
Number of patients = 58
-
Number of significantly arm-level cnvs = 61
-
Number of selected clinical features = 3
-
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.