This pipeline computes the correlation between significantly recurrent gene mutations and molecular subtypes.
Testing the association between mutation status of 7 genes and 8 molecular subtypes across 100 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
-
No gene mutations related to molecuar subtypes.
Clinical Features |
MRNA CNMF |
MRNA CHIERARCHICAL |
CN CNMF |
METHLYATION CNMF |
MRNASEQ CNMF |
MRNASEQ CHIERARCHICAL |
MIRSEQ CNMF |
MIRSEQ 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 | |
CDC27 | 4 (4%) | 96 |
0.402 (1.00) |
0.679 (1.00) |
0.84 (1.00) |
1 (1.00) |
0.417 (1.00) |
0.387 (1.00) |
||
MET | 8 (8%) | 92 |
1 (1.00) |
0.208 (1.00) |
0.467 (1.00) |
0.773 (1.00) |
0.752 (1.00) |
0.123 (1.00) |
0.132 (1.00) |
|
IL32 | 4 (4%) | 96 |
0.2 (1.00) |
0.0983 (1.00) |
0.649 (1.00) |
0.603 (1.00) |
0.183 (1.00) |
0.805 (1.00) |
||
PCF11 | 7 (7%) | 93 |
1 (1.00) |
0.441 (1.00) |
0.265 (1.00) |
0.0438 (1.00) |
0.677 (1.00) |
0.401 (1.00) |
0.949 (1.00) |
|
SFRS2IP | 5 (5%) | 95 |
0.34 (1.00) |
0.601 (1.00) |
0.116 (1.00) |
0.681 (1.00) |
0.456 (1.00) |
0.924 (1.00) |
||
NF2 | 6 (6%) | 94 |
0.169 (1.00) |
0.136 (1.00) |
0.23 (1.00) |
0.104 (1.00) |
0.439 (1.00) |
1 (1.00) |
||
LGI4 | 4 (4%) | 96 |
0.824 (1.00) |
0.814 (1.00) |
0.686 (1.00) |
1 (1.00) |
0.0481 (1.00) |
0.221 (1.00) |
-
Mutation data file = KIRP-TP.mutsig.cluster.txt
-
Molecular subtypes file = KIRP-TP.transferedmergedcluster.txt
-
Number of patients = 100
-
Number of significantly mutated genes = 7
-
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.