Correlation between copy number variations of arm-level result and selected clinical features
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between copy number variation 74 arm-level events and 2 clinical features across 32 patients, no significant finding detected with Q value < 0.25.

  • No arm-level cnvs related to clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 74 arm-level events and 2 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, no significant finding detected.

Clinical
Features 		Time
to
Death 		AGE
nCNV (%) nWild-Type logrank test t-test
1p gain 13 (41%) 19 0.759
(1.00) 		0.269
(1.00)
1q gain 16 (50%) 16 0.5
(1.00) 		0.378
(1.00)
2p gain 11 (34%) 21 0.0631
(1.00) 		0.371
(1.00)
2q gain 10 (31%) 22 0.332
(1.00) 		0.464
(1.00)
3p gain 6 (19%) 26 0.599
(1.00) 		0.482
(1.00)
3q gain 10 (31%) 22 0.647
(1.00) 		0.345
(1.00)
4p gain 3 (9%) 29 0.508
(1.00) 		0.759
(1.00)
5p gain 13 (41%) 19 0.0666
(1.00) 		0.563
(1.00)
5q gain 3 (9%) 29 0.574
(1.00) 		0.695
(1.00)
6p gain 15 (47%) 17 0.175
(1.00) 		0.115
(1.00)
6q gain 14 (44%) 18 0.0954
(1.00) 		0.0356
(1.00)
7p gain 9 (28%) 23 0.208
(1.00) 		0.0548
(1.00)
7q gain 7 (22%) 25 0.476
(1.00) 		0.715
(1.00)
8p gain 10 (31%) 22 0.382
(1.00) 		0.372
(1.00)
8q gain 15 (47%) 17 0.993
(1.00) 		0.121
(1.00)
9p gain 4 (12%) 28 0.605
(1.00) 		0.664
(1.00)
10p gain 10 (31%) 22 0.156
(1.00) 		0.217
(1.00)
10q gain 9 (28%) 23 0.281
(1.00) 		0.385
(1.00)
11p gain 3 (9%) 29 0.139
(1.00) 		0.827
(1.00)
11q gain 4 (12%) 28 0.4
(1.00) 		0.576
(1.00)
12p gain 11 (34%) 21 0.0256
(1.00) 		0.984
(1.00)
12q gain 5 (16%) 27 0.564
(1.00) 		0.941
(1.00)
13q gain 9 (28%) 23 0.52
(1.00) 		0.902
(1.00)
16p gain 7 (22%) 25 0.0553
(1.00) 		0.0306
(1.00)
16q gain 4 (12%) 28 0.186
(1.00) 		0.273
(1.00)
17p gain 5 (16%) 27 0.296
(1.00) 		0.626
(1.00)
17q gain 10 (31%) 22 0.791
(1.00) 		0.506
(1.00)
18p gain 9 (28%) 23 0.264
(1.00) 		0.143
(1.00)
18q gain 8 (25%) 24 0.555
(1.00) 		0.11
(1.00)
19p gain 12 (38%) 20 0.265
(1.00) 		0.826
(1.00)
19q gain 16 (50%) 16 0.373
(1.00) 		0.639
(1.00)
20p gain 23 (72%) 9 0.976
(1.00) 		0.235
(1.00)
20q gain 26 (81%) 6 0.643
(1.00) 		0.211
(1.00)
21q gain 10 (31%) 22 0.944
(1.00) 		0.186
(1.00)
22q gain 4 (12%) 28 0.236
(1.00) 		0.0308
(1.00)
xq gain 9 (28%) 23 0.581
(1.00) 		0.712
(1.00)
1p loss 5 (16%) 27 0.544
(1.00) 		0.414
(1.00)
1q loss 5 (16%) 27 0.544
(1.00) 		0.414
(1.00)
3p loss 11 (34%) 21 0.234
(1.00) 		0.712
(1.00)
3q loss 9 (28%) 23 0.175
(1.00) 		0.708
(1.00)
4p loss 20 (62%) 12 0.195
(1.00) 		0.496
(1.00)
4q loss 22 (69%) 10 0.114
(1.00) 		0.992
(1.00)
5p loss 6 (19%) 26 0.12
(1.00) 		0.325
(1.00)
5q loss 9 (28%) 23 0.967
(1.00) 		0.153
(1.00)
6p loss 3 (9%) 29 0.417
(1.00) 		0.904
(1.00)
6q loss 4 (12%) 28 0.0264
(1.00) 		0.483
(1.00)
7p loss 6 (19%) 26 0.543
(1.00) 		0.311
(1.00)
7q loss 7 (22%) 25 0.525
(1.00) 		0.176
(1.00)
8p loss 12 (38%) 20 0.787
(1.00) 		0.432
(1.00)
8q loss 5 (16%) 27 0.232
(1.00) 		0.139
(1.00)
9p loss 16 (50%) 16 0.838
(1.00) 		0.208
(1.00)
9q loss 19 (59%) 13 0.82
(1.00) 		0.177
(1.00)
10p loss 14 (44%) 18 0.212
(1.00) 		0.0489
(1.00)
10q loss 11 (34%) 21 0.568
(1.00) 		0.231
(1.00)
11p loss 16 (50%) 16 0.00833
(1.00) 		0.527
(1.00)
11q loss 15 (47%) 17 0.533
(1.00) 		0.608
(1.00)
12p loss 10 (31%) 22 0.738
(1.00) 		0.8
(1.00)
12q loss 9 (28%) 23 0.375
(1.00) 		0.437
(1.00)
13q loss 14 (44%) 18 0.954
(1.00) 		0.432
(1.00)
14q loss 18 (56%) 14 0.126
(1.00) 		0.474
(1.00)
15q loss 18 (56%) 14 0.371
(1.00) 		0.462
(1.00)
16p loss 18 (56%) 14 0.364
(1.00) 		0.542
(1.00)
16q loss 22 (69%) 10 0.998
(1.00) 		0.804
(1.00)
17p loss 19 (59%) 13 0.208
(1.00) 		0.0609
(1.00)
17q loss 9 (28%) 23 0.103
(1.00) 		0.00191
(0.283)
18p loss 8 (25%) 24 0.613
(1.00) 		0.82
(1.00)
18q loss 8 (25%) 24 0.957
(1.00) 		0.811
(1.00)
19p loss 11 (34%) 21 0.848
(1.00) 		0.948
(1.00)
19q loss 9 (28%) 23 0.438
(1.00) 		0.775
(1.00)
20p loss 4 (12%) 28 0.843
(1.00) 		0.816
(1.00)
20q loss 3 (9%) 29 0.525
(1.00) 		0.563
(1.00)
21q loss 8 (25%) 24 0.528
(1.00) 		0.344
(1.00)
22q loss 21 (66%) 11 0.19
(1.00) 		0.708
(1.00)
xq loss 12 (38%) 20 0.145
(1.00) 		0.987
(1.00)
Methods & Data
Input

  • Copy number data file = transformed.cor.cli.txt

  • Clinical data file = UCS-TP.merged_data.txt

  • Number of patients = 32

  • Number of significantly arm-level cnvs = 74

  • Number of selected clinical features = 2

  • Exclude regions that fewer than K tumors have mutations, K = 3

Survival analysis

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

Student's t-test analysis

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

Q value calculation

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.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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