This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.
Testing the association between copy number variation 34 arm-level events and 5 clinical features across 10 patients, no significant finding detected with Q value < 0.25.
-
No arm-level cnvs related to clinical features.
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 | |
4p gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
4q gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
5p gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
5q gain | 4 (40%) | 6 |
0.514 (1.00) |
0.994 (1.00) |
0.714 (1.00) |
0.524 (1.00) |
|
7p gain | 3 (30%) | 7 |
0.163 (1.00) |
0.136 (1.00) |
0.714 (1.00) |
1 (1.00) |
|
7q gain | 3 (30%) | 7 |
0.163 (1.00) |
0.136 (1.00) |
0.714 (1.00) |
1 (1.00) |
|
8p gain | 4 (40%) | 6 |
0.28 (1.00) |
0.734 (1.00) |
1 (1.00) |
1 (1.00) |
|
8q gain | 5 (50%) | 5 |
0.28 (1.00) |
0.658 (1.00) |
1 (1.00) |
1 (1.00) |
|
10q gain | 3 (30%) | 7 |
0.596 (1.00) |
0.456 (1.00) |
1 (1.00) |
||
12p gain | 6 (60%) | 4 |
0.378 (1.00) |
0.487 (1.00) |
0.381 (1.00) |
0.524 (1.00) |
|
12q gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
16p gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
16q gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
19p gain | 7 (70%) | 3 |
0.87 (1.00) |
0.956 (1.00) |
1 (1.00) |
1 (1.00) |
|
19q gain | 6 (60%) | 4 |
0.467 (1.00) |
0.507 (1.00) |
1 (1.00) |
0.524 (1.00) |
|
20p gain | 3 (30%) | 7 |
0.175 (1.00) |
0.191 (1.00) |
0.167 (1.00) |
||
20q gain | 5 (50%) | 5 |
0.0784 (1.00) |
0.971 (1.00) |
0.524 (1.00) |
0.206 (1.00) |
|
21q gain | 5 (50%) | 5 |
0.799 (1.00) |
0.528 (1.00) |
1 (1.00) |
1 (1.00) |
|
1p loss | 5 (50%) | 5 |
0.799 (1.00) |
0.287 (1.00) |
1 (1.00) |
1 (1.00) |
|
1q loss | 4 (40%) | 6 |
0.899 (1.00) |
0.113 (1.00) |
0.714 (1.00) |
1 (1.00) |
|
3p loss | 3 (30%) | 7 |
0.0793 (1.00) |
0.423 (1.00) |
1 (1.00) |
||
8p loss | 3 (30%) | 7 |
0.87 (1.00) |
0.0886 (1.00) |
0.5 (1.00) |
1 (1.00) |
|
9p loss | 3 (30%) | 7 |
0.596 (1.00) |
0.561 (1.00) |
1 (1.00) |
||
9q loss | 3 (30%) | 7 |
0.596 (1.00) |
0.561 (1.00) |
1 (1.00) |
||
11p loss | 5 (50%) | 5 |
0.899 (1.00) |
0.769 (1.00) |
0.381 (1.00) |
1 (1.00) |
|
11q loss | 5 (50%) | 5 |
0.899 (1.00) |
0.769 (1.00) |
0.381 (1.00) |
1 (1.00) |
|
13q loss | 5 (50%) | 5 |
0.381 (1.00) |
0.883 (1.00) |
1 (1.00) |
1 (1.00) |
|
15q loss | 4 (40%) | 6 |
0.28 (1.00) |
0.587 (1.00) |
1 (1.00) |
0.524 (1.00) |
|
17p loss | 5 (50%) | 5 |
0.043 (1.00) |
0.797 (1.00) |
0.19 (1.00) |
1 (1.00) |
|
17q loss | 3 (30%) | 7 |
0.497 (1.00) |
0.399 (1.00) |
1 (1.00) |
||
18p loss | 4 (40%) | 6 |
0.987 (1.00) |
0.617 (1.00) |
0.714 (1.00) |
0.524 (1.00) |
|
18q loss | 4 (40%) | 6 |
0.987 (1.00) |
0.617 (1.00) |
0.714 (1.00) |
0.524 (1.00) |
|
22q loss | 6 (60%) | 4 |
0.994 (1.00) |
0.872 (1.00) |
1 (1.00) |
0.524 (1.00) |
|
xq loss | 3 (30%) | 7 |
0.149 (1.00) |
0.0324 (1.00) |
0.143 (1.00) |
1 (1.00) |
-
Copy number data file = transformed.cor.cli.txt
-
Clinical data file = ACC-TP.merged_data.txt
-
Number of patients = 10
-
Number of significantly arm-level cnvs = 34
-
Number of selected clinical features = 5
-
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 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.