This pipeline computes the correlation between significant copy number variation (cnv) genes and molecular subtypes.
Testing the association between copy number variation of 9 peak regions and molecular subtype 'METHLYATION_CNMF' across 18 patients, one significant finding detected with Q value < 0.25.
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Amp Peak 1(2p16.1) cnvs correlated to 'METHLYATION_CNMF'.
Molecular subtypes |
METHLYATION CNMF |
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nCNV (%) | nWild-Type | Fisher's exact test | |
Amp Peak 1(2p16 1) | 0 (0%) | 12 |
0.00905 (0.0814) |
Del Peak 2(1q43) | 0 (0%) | 15 |
0.206 (1.00) |
Del Peak 3(2q22 3) | 0 (0%) | 14 |
1 (1.00) |
Del Peak 4(6q14 1) | 0 (0%) | 14 |
1 (1.00) |
Del Peak 5(6q23 3) | 0 (0%) | 15 |
1 (1.00) |
Del Peak 7(9p21 3) | 0 (0%) | 14 |
0.576 (1.00) |
Del Peak 9(13q33 3) | 0 (0%) | 15 |
1 (1.00) |
Del Peak 10(15q15 1) | 0 (0%) | 13 |
1 (1.00) |
Del Peak 11(16p13 13) | 0 (0%) | 15 |
0.206 (1.00) |
P value = 0.00905 (Fisher's exact test), Q value = 0.081
nPatients | CLUS_1 | CLUS_2 |
---|---|---|
ALL | 9 | 9 |
AMP PEAK 1(2P16.1) CNV | 0 | 6 |
AMP PEAK 1(2P16.1) WILD-TYPE | 9 | 3 |
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Copy number data file = All Lesions File (all_lesions.conf_##.txt, where ## is the confidence level). The all lesions file is from GISTIC pipeline and summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the first column, and the samples are listed across the first row, starting in column 10.
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Molecular subtype file = DLBC-TP.transferedmergedcluster.txt
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Number of patients = 18
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Number of copy number variation regions = 9
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Number of molecular subtypes = 1: 'METHLYATION_CNMF'
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Exclude regions 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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.