Correlation between copy number variations of arm-level result and molecular subtypes
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and molecular subtypes.

Summary

Testing the association between copy number variation 50 arm-level events and 8 molecular subtypes across 48 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 7p gain cnv correlated to 'CN_CNMF'.

  • 7q gain cnv correlated to 'CN_CNMF'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between significant copy number variation of 50 arm-level events and 8 molecular subtypes. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 2 significant findings detected.

Clinical
Features 		CN
CNMF 		METHLYATION
CNMF 		MRNASEQ
CNMF 		MRNASEQ
CHIERARCHICAL 		MIRSEQ
CNMF 		MIRSEQ
CHIERARCHICAL 		MIRSEQ
MATURE
CNMF 		MIRSEQ
MATURE
CHIERARCHICAL
nCNV (%) nWild-Type Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
7p gain 15 (31%) 33 1.04e-05
(0.00379) 		0.534
(1.00) 		1
(1.00) 		0.459
(1.00) 		0.925
(1.00) 		0.887
(1.00) 		1
(1.00) 		0.124
(1.00)
7q gain 13 (27%) 35 7.71e-06
(0.00282) 		1
(1.00) 		0.67
(1.00) 		0.122
(1.00) 		0.663
(1.00) 		0.988
(1.00) 		1
(1.00) 		0.382
(1.00)
1q gain 6 (12%) 42 0.197
(1.00) 		0.666
(1.00) 		1
(1.00) 		0.641
(1.00) 		0.416
(1.00) 		0.315
(1.00) 		1
(1.00) 		0.844
(1.00)
2p gain 6 (12%) 42 0.669
(1.00) 		0.666
(1.00) 		1
(1.00) 		1
(1.00) 		0.608
(1.00) 		1
(1.00) 		0.22
(1.00)
2q gain 6 (12%) 42 0.669
(1.00) 		0.666
(1.00) 		1
(1.00) 		1
(1.00) 		0.604
(1.00) 		1
(1.00) 		0.223
(1.00)
3p gain 10 (21%) 38 0.0363
(1.00) 		0.286
(1.00) 		1
(1.00) 		0.677
(1.00) 		0.635
(1.00) 		0.402
(1.00) 		0.707
(1.00) 		0.348
(1.00)
3q gain 13 (27%) 35 0.0188
(1.00) 		0.193
(1.00) 		1
(1.00) 		0.516
(1.00) 		0.767
(1.00) 		0.566
(1.00) 		1
(1.00) 		0.249
(1.00)
5p gain 7 (15%) 41 0.687
(1.00) 		1
(1.00) 		1
(1.00) 		0.339
(1.00) 		0.637
(1.00) 		0.356
(1.00) 		0.118
(1.00)
5q gain 6 (12%) 42 1
(1.00) 		1
(1.00) 		0.464
(1.00) 		0.66
(1.00) 		0.796
(1.00) 		0.613
(1.00) 		0.535
(1.00)
6p gain 6 (12%) 42 0.197
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.863
(1.00) 		0.644
(1.00) 		0.881
(1.00)
6q gain 4 (8%) 44 0.0199
(1.00) 		0.609
(1.00) 		1
(1.00) 		0.711
(1.00) 		1
(1.00) 		0.0844
(1.00) 		0.874
(1.00)
8p gain 7 (15%) 41 0.687
(1.00) 		0.416
(1.00) 		0.206
(1.00) 		0.102
(1.00) 		0.00554
(1.00) 		0.428
(1.00) 		0.257
(1.00)
8q gain 8 (17%) 40 0.451
(1.00) 		0.245
(1.00) 		0.206
(1.00) 		0.148
(1.00) 		0.0279
(1.00) 		0.428
(1.00) 		0.258
(1.00)
9p gain 7 (15%) 41 0.687
(1.00) 		1
(1.00) 		1
(1.00) 		0.776
(1.00) 		0.638
(1.00) 		1
(1.00) 		0.466
(1.00)
9q gain 7 (15%) 41 0.687
(1.00) 		1
(1.00) 		1
(1.00) 		0.481
(1.00) 		0.292
(1.00) 		1
(1.00) 		0.266
(1.00)
10p gain 4 (8%) 44 0.0199
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.581
(1.00) 		0.638
(1.00)
10q gain 4 (8%) 44 0.286
(1.00) 		0.609
(1.00) 		0.464
(1.00) 		0.424
(1.00) 		0.78
(1.00) 		0.0844
(1.00) 		0.259
(1.00)
11p gain 9 (19%) 39 0.451
(1.00) 		0.461
(1.00) 		0.6
(1.00) 		0.147
(1.00) 		0.717
(1.00) 		0.164
(1.00) 		0.433
(1.00) 		0.227
(1.00)
11q gain 13 (27%) 35 0.0959
(1.00) 		0.193
(1.00) 		0.686
(1.00) 		0.0876
(1.00) 		0.45
(1.00) 		0.0739
(1.00) 		0.468
(1.00) 		0.396
(1.00)
12p gain 7 (15%) 41 1
(1.00) 		0.416
(1.00) 		0.583
(1.00) 		0.111
(1.00) 		0.817
(1.00) 		0.27
(1.00) 		0.384
(1.00) 		0.862
(1.00)
12q gain 9 (19%) 39 1
(1.00) 		0.137
(1.00) 		1
(1.00) 		0.147
(1.00) 		0.649
(1.00) 		0.138
(1.00) 		0.433
(1.00) 		0.488
(1.00)
13q gain 5 (10%) 43 1
(1.00) 		1
(1.00) 		0.206
(1.00) 		0.134
(1.00) 		0.661
(1.00) 		0.0346
(1.00) 		0.839
(1.00)
16p gain 7 (15%) 41 0.0114
(1.00) 		0.416
(1.00) 		0.6
(1.00) 		0.208
(1.00) 		0.327
(1.00) 		0.0294
(1.00) 		0.384
(1.00) 		0.513
(1.00)
16q gain 7 (15%) 41 0.0967
(1.00) 		0.0971
(1.00) 		0.6
(1.00) 		1
(1.00) 		0.327
(1.00) 		0.0148
(1.00) 		0.384
(1.00) 		0.708
(1.00)
17q gain 3 (6%) 45 0.554
(1.00) 		0.234
(1.00) 		0.464
(1.00) 		0.607
(1.00) 		0.0357
(1.00) 		0.581
(1.00) 		0.498
(1.00)
18p gain 13 (27%) 35 0.0959
(1.00) 		1
(1.00) 		1
(1.00) 		0.677
(1.00) 		0.359
(1.00) 		0.121
(1.00) 		0.504
(1.00) 		0.523
(1.00)
18q gain 14 (29%) 34 0.0491
(1.00) 		1
(1.00) 		0.655
(1.00) 		1
(1.00) 		0.454
(1.00) 		0.114
(1.00) 		0.323
(1.00) 		0.405
(1.00)
19p gain 3 (6%) 45 0.554
(1.00) 		1
(1.00) 		0.464
(1.00) 		0.684
(1.00) 		0.692
(1.00) 		0.452
(1.00)
19q gain 3 (6%) 45 0.554
(1.00) 		1
(1.00) 		0.464
(1.00) 		0.69
(1.00) 		0.688
(1.00) 		0.452
(1.00)
20p gain 5 (10%) 43 0.372
(1.00) 		0.348
(1.00) 		1
(1.00) 		0.289
(1.00) 		0.374
(1.00) 		1
(1.00) 		0.703
(1.00)
20q gain 4 (8%) 44 1
(1.00) 		0.609
(1.00) 		0.464
(1.00) 		0.655
(1.00) 		0.817
(1.00) 		0.581
(1.00) 		0.3
(1.00)
21q gain 10 (21%) 38 1
(1.00) 		1
(1.00) 		1
(1.00) 		0.426
(1.00) 		0.0274
(1.00) 		0.186
(1.00) 		0.258
(1.00) 		0.371
(1.00)
xq gain 6 (12%) 42 0.0297
(1.00) 		0.188
(1.00) 		1
(1.00) 		0.384
(1.00) 		0.521
(1.00) 		0.219
(1.00) 		0.158
(1.00) 		0.105
(1.00)
1p loss 3 (6%) 45 0.554
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.847
(1.00) 		1
(1.00)
3p loss 5 (10%) 43 1
(1.00) 		1
(1.00) 		1
(1.00) 		0.856
(1.00) 		0.959
(1.00) 		1
(1.00) 		0.664
(1.00)
3q loss 4 (8%) 44 1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.581
(1.00) 		0.524
(1.00)
4p loss 3 (6%) 45 0.554
(1.00) 		1
(1.00) 		0.464
(1.00) 		0.153
(1.00) 		0.688
(1.00) 		1
(1.00)
4q loss 4 (8%) 44 0.286
(1.00) 		0.609
(1.00) 		1
(1.00) 		0.466
(1.00) 		0.232
(1.00) 		0.613
(1.00) 		0.876
(1.00)
6q loss 7 (15%) 41 1
(1.00) 		0.416
(1.00) 		1
(1.00) 		0.387
(1.00) 		0.708
(1.00) 		0.724
(1.00) 		0.0754
(1.00) 		0.513
(1.00)
8p loss 8 (17%) 40 0.0446
(1.00) 		1
(1.00) 		0.311
(1.00) 		0.355
(1.00) 		0.198
(1.00) 		0.0253
(1.00) 		1
(1.00) 		0.934
(1.00)
8q loss 4 (8%) 44 0.286
(1.00) 		1
(1.00) 		1
(1.00) 		0.657
(1.00) 		0.156
(1.00) 		1
(1.00)
13q loss 3 (6%) 45 0.554
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.848
(1.00) 		1
(1.00)
15q loss 7 (15%) 41 0.0114
(1.00) 		0.416
(1.00) 		0.639
(1.00) 		0.452
(1.00) 		0.126
(1.00) 		0.371
(1.00) 		0.197
(1.00) 		0.745
(1.00)
16q loss 4 (8%) 44 0.0199
(1.00) 		1
(1.00) 		1
(1.00) 		0.639
(1.00) 		0.0954
(1.00) 		0.127
(1.00) 		0.239
(1.00) 		0.662
(1.00)
17p loss 9 (19%) 39 0.0195
(1.00) 		1
(1.00) 		1
(1.00) 		0.763
(1.00) 		0.887
(1.00) 		0.502
(1.00) 		0.384
(1.00) 		0.27
(1.00)
17q loss 4 (8%) 44 0.286
(1.00) 		0.609
(1.00) 		1
(1.00) 		0.712
(1.00) 		0.489
(1.00) 		0.0844
(1.00) 		0.463
(1.00)
18p loss 5 (10%) 43 0.372
(1.00) 		0.348
(1.00) 		0.484
(1.00) 		0.555
(1.00) 		0.249
(1.00) 		0.158
(1.00) 		0.216
(1.00)
18q loss 4 (8%) 44 0.286
(1.00) 		0.609
(1.00) 		1
(1.00) 		0.654
(1.00) 		1
(1.00) 		0.581
(1.00) 		0.524
(1.00)
22q loss 3 (6%) 45 0.056
(1.00) 		1
(1.00) 		1
(1.00) 		1
(1.00) 		0.438
(1.00) 		1
(1.00)
xq loss 5 (10%) 43 0.372
(1.00) 		0.348
(1.00) 		0.583
(1.00) 		0.566
(1.00) 		0.333
(1.00) 		0.0139
(1.00) 		0.356
(1.00) 		0.286
(1.00)
'7p gain' versus 'CN_CNMF'

P value = 1.04e-05 (Fisher's exact test), Q value = 0.0038

Table S1.  Gene #10: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2
ALL 29 19
7P GAIN MUTATED 2 13
7P GAIN WILD-TYPE 27 6

Figure S1.  Get High-res Image Gene #10: '7p gain' versus Molecular Subtype #1: 'CN_CNMF'

'7q gain' versus 'CN_CNMF'

P value = 7.71e-06 (Fisher's exact test), Q value = 0.0028

Table S2.  Gene #11: '7q gain' versus Molecular Subtype #1: 'CN_CNMF'

nPatients CLUS_1 CLUS_2
ALL 29 19
7Q GAIN MUTATED 1 12
7Q GAIN WILD-TYPE 28 7

Figure S2.  Get High-res Image Gene #11: '7q gain' versus Molecular Subtype #1: 'CN_CNMF'

Methods & Data
Input

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

  • Molecular subtypes file = DLBC-TP.transferedmergedcluster.txt

  • Number of patients = 48

  • Number of significantly arm-level cnvs = 50

  • Number of molecular subtypes = 8

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

Fisher's exact test

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

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] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[2] 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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