This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and molecular subtypes.
Testing the association between copy number variation 24 arm-level events and 7 molecular subtypes across 28 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
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No arm-level cnvs related to molecular subtypes.
Clinical Features |
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 | Chi-square test | Fisher's exact test | Fisher's exact test | |
1q gain | 4 (14%) | 24 |
1 (1.00) |
1 (1.00) |
0.601 (1.00) |
0.326 (1.00) |
0.359 (1.00) |
0.28 (1.00) |
1 (1.00) |
3p gain | 5 (18%) | 23 |
0.823 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.529 (1.00) |
1 (1.00) |
0.326 (1.00) |
3q gain | 6 (21%) | 22 |
0.692 (1.00) |
1 (1.00) |
0.634 (1.00) |
1 (1.00) |
0.673 (1.00) |
1 (1.00) |
0.638 (1.00) |
6p gain | 3 (11%) | 25 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.596 (1.00) |
0.326 (1.00) |
0.751 (1.00) |
1 (1.00) |
7p gain | 8 (29%) | 20 |
0.287 (1.00) |
1 (1.00) |
0.4 (1.00) |
1 (1.00) |
0.499 (1.00) |
1 (1.00) |
0.415 (1.00) |
7q gain | 7 (25%) | 21 |
0.616 (1.00) |
0.67 (1.00) |
0.207 (1.00) |
0.678 (1.00) |
0.365 (1.00) |
1 (1.00) |
1 (1.00) |
10p gain | 3 (11%) | 25 |
1 (1.00) |
0.583 (1.00) |
1 (1.00) |
0.596 (1.00) |
0.566 (1.00) |
1 (1.00) |
1 (1.00) |
11p gain | 4 (14%) | 24 |
0.478 (1.00) |
0.6 (1.00) |
0.265 (1.00) |
0.0978 (1.00) |
0.709 (1.00) |
1 (1.00) |
0.613 (1.00) |
11q gain | 8 (29%) | 20 |
0.547 (1.00) |
0.686 (1.00) |
0.194 (1.00) |
0.385 (1.00) |
0.222 (1.00) |
0.632 (1.00) |
1 (1.00) |
12p gain | 3 (11%) | 25 |
1 (1.00) |
0.583 (1.00) |
0.533 (1.00) |
1 (1.00) |
0.906 (1.00) |
1 (1.00) |
1 (1.00) |
12q gain | 4 (14%) | 24 |
0.478 (1.00) |
1 (1.00) |
0.265 (1.00) |
0.596 (1.00) |
0.709 (1.00) |
1 (1.00) |
0.613 (1.00) |
16p gain | 4 (14%) | 24 |
0.478 (1.00) |
0.6 (1.00) |
1 (1.00) |
0.596 (1.00) |
0.435 (1.00) |
0.28 (1.00) |
0.128 (1.00) |
16q gain | 4 (14%) | 24 |
0.0918 (1.00) |
0.6 (1.00) |
1 (1.00) |
1 (1.00) |
0.276 (1.00) |
1 (1.00) |
0.613 (1.00) |
18p gain | 5 (18%) | 23 |
0.823 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.838 (1.00) |
1 (1.00) |
1 (1.00) |
18q gain | 5 (18%) | 23 |
0.823 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.838 (1.00) |
1 (1.00) |
1 (1.00) |
21q gain | 6 (21%) | 22 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.165 (1.00) |
0.0148 (1.00) |
0.314 (1.00) |
0.124 (1.00) |
xq gain | 3 (11%) | 25 |
0.179 (1.00) |
1 (1.00) |
0.533 (1.00) |
1 (1.00) |
0.393 (1.00) |
0.751 (1.00) |
0.535 (1.00) |
1p loss | 3 (11%) | 25 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.596 (1.00) |
0.607 (1.00) |
1 (1.00) |
1 (1.00) |
6q loss | 3 (11%) | 25 |
1 (1.00) |
1 (1.00) |
0.533 (1.00) |
0.222 (1.00) |
0.607 (1.00) |
1 (1.00) |
1 (1.00) |
8p loss | 4 (14%) | 24 |
1 (1.00) |
0.311 (1.00) |
0.116 (1.00) |
0.326 (1.00) |
0.551 (1.00) |
1 (1.00) |
1 (1.00) |
15q loss | 5 (18%) | 23 |
0.312 (1.00) |
0.639 (1.00) |
0.315 (1.00) |
0.648 (1.00) |
0.433 (1.00) |
1 (1.00) |
0.621 (1.00) |
16q loss | 4 (14%) | 24 |
1 (1.00) |
1 (1.00) |
0.601 (1.00) |
1 (1.00) |
0.575 (1.00) |
1 (1.00) |
1 (1.00) |
17p loss | 3 (11%) | 25 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.607 (1.00) |
0.751 (1.00) |
0.535 (1.00) |
xq loss | 3 (11%) | 25 |
0.179 (1.00) |
0.583 (1.00) |
0.284 (1.00) |
0.596 (1.00) |
0.15 (1.00) |
0.0862 (1.00) |
0.274 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Molecular subtypes file = DLBC-TP.transferedmergedcluster.txt
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Number of patients = 28
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Number of significantly arm-level cnvs = 24
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Number of molecular subtypes = 7
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Exclude genes that fewer than K tumors have mutations, K = 3
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.
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.