This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.
Testing the association between mutation status of 6 genes and 8 molecular subtypes across 39 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
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No gene mutations related to molecuar subtypes.
Clinical Features |
CN CNMF |
METHLYATION CNMF |
MRNASEQ CNMF |
MRNASEQ CHIERARCHICAL |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
MIRSEQ MATURE CNMF |
MIRSEQ MATURE CHIERARCHICAL |
||
nMutated (%) | nWild-Type | Fisher's exact test | Chi-square test | Chi-square test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | Fisher's exact test | |
PIK3CA | 9 (23%) | 30 |
0.0409 (1.00) |
0.469 (1.00) |
0.36 (1.00) |
0.567 (1.00) |
0.88 (1.00) |
0.669 (1.00) |
1 (1.00) |
0.0855 (1.00) |
TMCC1 | 4 (10%) | 35 |
0.293 (1.00) |
0.698 (1.00) |
0.599 (1.00) |
0.628 (1.00) |
0.556 (1.00) |
0.378 (1.00) |
0.458 (1.00) |
|
UGT3A2 | 3 (8%) | 36 |
0.548 (1.00) |
0.118 (1.00) |
0.52 (1.00) |
0.55 (1.00) |
0.556 (1.00) |
0.397 (1.00) |
0.0312 (1.00) |
|
CDC27 | 5 (13%) | 34 |
0.435 (1.00) |
0.514 (1.00) |
0.545 (1.00) |
0.174 (1.00) |
0.456 (1.00) |
0.302 (1.00) |
0.578 (1.00) |
0.0279 (1.00) |
MAPK1 | 3 (8%) | 36 |
0.0387 (1.00) |
0.641 (1.00) |
0.655 (1.00) |
0.73 (1.00) |
0.0885 (1.00) |
0.556 (1.00) |
0.397 (1.00) |
0.783 (1.00) |
UGT2B10 | 3 (8%) | 36 |
0.408 (1.00) |
0.655 (1.00) |
0.52 (1.00) |
1 (1.00) |
1 (1.00) |
0.397 (1.00) |
0.146 (1.00) |
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Mutation data file = CESC-TP.mutsig.cluster.txt
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Molecular subtypes file = CESC-TP.transferedmergedcluster.txt
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Number of patients = 39
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Number of significantly mutated genes = 6
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Number of Molecular subtypes = 8
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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.