This pipeline computes the correlation between significantly recurrent gene mutations and selected clinical features.
Testing the association between mutation status of 13 genes and 2 clinical features across 83 patients, 2 significant findings detected with Q value < 0.25.
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CLSTN1 mutation correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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PRR21 mutation correlated to 'AGE'.
Clinical Features |
AGE |
RADIATIONS RADIATION REGIMENINDICATION |
||
nMutated (%) | nWild-Type | t-test | Fisher's exact test | |
CLSTN1 | 3 (4%) | 80 |
0.093 (1.00) |
0.0086 (0.215) |
PRR21 | 4 (5%) | 79 |
0.00302 (0.0785) |
0.224 (1.00) |
NKX3-1 | 5 (6%) | 78 |
0.463 (1.00) |
1 (1.00) |
TP53 | 5 (6%) | 78 |
0.64 (1.00) |
1 (1.00) |
FRG1 | 4 (5%) | 79 |
0.0586 (1.00) |
0.224 (1.00) |
SPOP | 4 (5%) | 79 |
0.481 (1.00) |
1 (1.00) |
YBX1 | 3 (4%) | 80 |
0.784 (1.00) |
1 (1.00) |
CCNF | 3 (4%) | 80 |
0.643 (1.00) |
0.172 (1.00) |
AGT | 3 (4%) | 80 |
0.6 (1.00) |
1 (1.00) |
CTNNB1 | 3 (4%) | 80 |
0.992 (1.00) |
0.172 (1.00) |
DUSP27 | 3 (4%) | 80 |
0.824 (1.00) |
1 (1.00) |
OR4D5 | 3 (4%) | 80 |
0.092 (1.00) |
1 (1.00) |
OR6N1 | 3 (4%) | 80 |
0.367 (1.00) |
0.172 (1.00) |
P value = 0.0086 (Fisher's exact test), Q value = 0.21
nPatients | NO | YES |
---|---|---|
ALL | 5 | 78 |
CLSTN1 MUTATED | 2 | 1 |
CLSTN1 WILD-TYPE | 3 | 77 |
P value = 0.00302 (t-test), Q value = 0.079
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 83 | 61.1 (6.8) |
PRR21 MUTATED | 4 | 66.5 (2.1) |
PRR21 WILD-TYPE | 79 | 60.8 (6.8) |
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Mutation data file = PRAD-TP.mutsig.cluster.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 83
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Number of significantly mutated genes = 13
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Number of selected clinical features = 2
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Exclude genes that fewer than K tumors have mutations, K = 3
For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between tumors with and without gene mutations using 't.test' function in R
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.