This pipeline computes the correlation between significant copy number variation (cnv focal) genes and molecular subtypes.
Testing the association between copy number variation 24 focal 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 focal cnvs related to molecuar 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 | |
amp 1q31 1 | 9 (32%) | 19 |
0.192 (1.00) |
0.228 (1.00) |
0.0346 (1.00) |
0.0461 (1.00) |
0.536 (1.00) |
0.874 (1.00) |
0.406 (1.00) |
amp 2p15 | 8 (29%) | 20 |
0.00641 (1.00) |
0.686 (1.00) |
0.669 (1.00) |
1 (1.00) |
0.00518 (0.87) |
0.436 (1.00) |
1 (1.00) |
amp 3q27 3 | 7 (25%) | 21 |
0.616 (1.00) |
0.396 (1.00) |
1 (1.00) |
0.385 (1.00) |
0.147 (1.00) |
0.418 (1.00) |
0.0646 (1.00) |
amp 8q24 12 | 6 (21%) | 22 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.349 (1.00) |
0.291 (1.00) |
1 (1.00) |
amp 12q13 12 | 5 (18%) | 23 |
0.312 (1.00) |
1 (1.00) |
0.128 (1.00) |
0.326 (1.00) |
0.575 (1.00) |
1 (1.00) |
1 (1.00) |
amp 16p11 2 | 6 (21%) | 22 |
0.11 (1.00) |
0.173 (1.00) |
0.375 (1.00) |
0.648 (1.00) |
0.185 (1.00) |
0.842 (1.00) |
0.638 (1.00) |
amp xq27 3 | 6 (21%) | 22 |
0.11 (1.00) |
0.655 (1.00) |
0.375 (1.00) |
0.648 (1.00) |
0.717 (1.00) |
0.842 (1.00) |
0.638 (1.00) |
del 1p22 1 | 4 (14%) | 24 |
0.478 (1.00) |
1 (1.00) |
0.601 (1.00) |
0.326 (1.00) |
0.846 (1.00) |
1 (1.00) |
1 (1.00) |
del 1p13 1 | 7 (25%) | 21 |
0.285 (1.00) |
0.67 (1.00) |
0.207 (1.00) |
0.0329 (1.00) |
0.67 (1.00) |
0.418 (1.00) |
0.204 (1.00) |
del 1q43 | 7 (25%) | 21 |
0.285 (1.00) |
1 (1.00) |
0.674 (1.00) |
1 (1.00) |
0.977 (1.00) |
1 (1.00) |
1 (1.00) |
del 2q23 1 | 6 (21%) | 22 |
1 (1.00) |
1 (1.00) |
0.634 (1.00) |
0.648 (1.00) |
0.838 (1.00) |
1 (1.00) |
0.621 (1.00) |
del 6q14 1 | 9 (32%) | 19 |
0.467 (1.00) |
0.435 (1.00) |
0.417 (1.00) |
0.103 (1.00) |
0.163 (1.00) |
0.874 (1.00) |
0.219 (1.00) |
del 6q23 3 | 7 (25%) | 21 |
0.285 (1.00) |
0.0836 (1.00) |
0.0302 (1.00) |
0.385 (1.00) |
0.222 (1.00) |
1 (1.00) |
1 (1.00) |
del 8p23 1 | 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) |
del 8q12 1 | 4 (14%) | 24 |
1 (1.00) |
0.311 (1.00) |
0.116 (1.00) |
0.326 (1.00) |
0.846 (1.00) |
1 (1.00) |
1 (1.00) |
del 9p21 3 | 9 (32%) | 19 |
0.0481 (1.00) |
0.114 (1.00) |
0.417 (1.00) |
0.42 (1.00) |
0.0149 (1.00) |
0.281 (1.00) |
0.219 (1.00) |
del 10q23 31 | 5 (18%) | 23 |
0.823 (1.00) |
0.639 (1.00) |
0.315 (1.00) |
0.648 (1.00) |
0.687 (1.00) |
1 (1.00) |
1 (1.00) |
del 13q14 2 | 3 (11%) | 25 |
1 (1.00) |
1 (1.00) |
1 (1.00) |
1 (1.00) |
0.15 (1.00) |
0.751 (1.00) |
1 (1.00) |
del 13q33 3 | 4 (14%) | 24 |
0.787 (1.00) |
0.102 (1.00) |
0.265 (1.00) |
0.222 (1.00) |
0.0188 (1.00) |
0.751 (1.00) |
0.535 (1.00) |
del 15q15 1 | 8 (29%) | 20 |
0.413 (1.00) |
1 (1.00) |
0.4 (1.00) |
0.678 (1.00) |
0.486 (1.00) |
1 (1.00) |
0.678 (1.00) |
del 15q21 1 | 9 (32%) | 19 |
0.656 (1.00) |
1 (1.00) |
0.677 (1.00) |
1 (1.00) |
0.633 (1.00) |
1 (1.00) |
1 (1.00) |
del 16p13 13 | 4 (14%) | 24 |
0.478 (1.00) |
0.311 (1.00) |
0.601 (1.00) |
0.326 (1.00) |
0.0852 (1.00) |
0.28 (1.00) |
0.264 (1.00) |
del 16q23 1 | 5 (18%) | 23 |
0.823 (1.00) |
0.639 (1.00) |
0.315 (1.00) |
0.648 (1.00) |
0.609 (1.00) |
1 (1.00) |
0.621 (1.00) |
del 17q24 1 | 4 (14%) | 24 |
0.0918 (1.00) |
1 (1.00) |
0.601 (1.00) |
1 (1.00) |
0.276 (1.00) |
0.502 (1.00) |
0.613 (1.00) |
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Copy number data file = transformed.cor.cli.txt
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Molecular subtype file = DLBC-TP.transferedmergedcluster.txt
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Number of patients = 28
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Number of significantly focal cnvs = 24
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Number of molecular subtypes = 7
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Exclude genes that fewer than K tumors have alterations, 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.