This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 4 clinical features across 57 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one variables.
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2 variables correlated to 'AGE'.
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MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS
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No variables correlated to 'Time to Death', 'AGE_mutation.rate', and 'RACE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
AGE | Linear Regression Analysis | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.3-102.4 (median=18.4) |
censored | N = 22 | |
death | N = 34 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 69.72 (9.3) |
Significant variables | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
AGE | Mean (SD) | 69.72 (9.3) |
Significant variables | N = 0 |
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Expresson data file = UCS-TP.patients.counts_and_rates.txt
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Clinical data file = UCS-TP.merged_data.txt
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Number of patients = 57
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Number of variables = 2
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Number of clinical features = 4
For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R
For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' 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.