This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.
Testing the association between mutation status of 5 genes and 8 molecular subtypes across 39 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
-
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 | 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 | |
PIK3CA | 9 (23%) | 30 |
0.098 (1.00) |
0.224 (1.00) |
0.387 (1.00) |
0.734 (1.00) |
0.88 (1.00) |
0.669 (1.00) |
1 (1.00) |
0.113 (1.00) |
TMCC1 | 4 (10%) | 35 |
1 (1.00) |
0.489 (1.00) |
0.0537 (1.00) |
0.628 (1.00) |
0.556 (1.00) |
0.793 (1.00) |
1 (1.00) |
|
CDC27 | 5 (13%) | 34 |
0.397 (1.00) |
0.234 (1.00) |
0.128 (1.00) |
0.0681 (1.00) |
0.456 (1.00) |
0.302 (1.00) |
0.389 (1.00) |
0.112 (1.00) |
UGT3A2 | 3 (8%) | 36 |
1 (1.00) |
0.0166 (0.631) |
0.0517 (1.00) |
0.55 (1.00) |
0.556 (1.00) |
1 (1.00) |
0.184 (1.00) |
|
MAPK1 | 3 (8%) | 36 |
0.141 (1.00) |
1 (1.00) |
0.398 (1.00) |
0.68 (1.00) |
0.0885 (1.00) |
0.556 (1.00) |
0.4 (1.00) |
1 (1.00) |
-
Mutation data file = CESC-TP.mutsig.cluster.txt
-
Molecular subtypes file = CESC-TP.transferedmergedcluster.txt
-
Number of patients = 39
-
Number of significantly mutated genes = 5
-
Number of Molecular subtypes = 8
-
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 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.