This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 11 clinical features across 417 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.
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1 variable correlated to 'Time to Death'.
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MUTATIONRATE_NONSYNONYMOUS
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2 variables correlated to 'AGE'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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2 variables correlated to 'AGE_mutation.rate'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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1 variable correlated to 'RACE'.
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MUTATIONRATE_NONSYNONYMOUS
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No variables correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of variables that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.
Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
AGE | Linear Regression Analysis | N=2 | ||||
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Wilcoxon test | N=0 | ||||
PATHOLOGY M STAGE | Wilcoxon test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RACE | Kruskal-Wallis test | N=1 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-120.6 (median=37.2) |
censored | N = 275 | |
death | N = 142 | |
Significant variables | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of one variable significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
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MUTATIONRATE_NONSYNONYMOUS | Inf | 0.04311 | 0.086 | 0.57 |
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 60.69 (12) |
Significant variables | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
Table S4. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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MUTATIONRATE_NONSYNONYMOUS | 0.3724 | 3.677e-15 | 7.35e-15 |
MUTATIONRATE_SILENT | 0.3336 | 2.703e-12 | 2.7e-12 |
Table S5. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 60.69 (12) |
Significant variables | N = 2 |
Table S6. Get Full Table List of 2 variables significantly correlated to 'AGE' by Linear regression analysis [lm (mutation rate ~ age)]. Compared to a correlation analysis testing for interdependence of the variables, a regression model attempts to describe the dependence of a variable on one (or more) explanatory variables assuming that there is a one-way causal effect from the explanatory variable(s) to the response variable. If 'Residuals vs Fitted' plot (a standard residual plot) shows a random pattern indicating a good fit for a linear model, it explains linear regression relationship between Mutation rate and age factor. Adj.R-squared (= Explained variation / Total variation) indicates regression model's explanatory power.
Adj.R.squared | F | P | Residual.std.err | DF | coef(intercept) | coef.p(intercept) | |
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MUTATIONRATE_NONSYNONYMOUS | 0.141 | 69.4 | 1.19e-15 | 5.82e-07 | 415 | 1.97e-08 ( 3.32e-07 ) | 1.19e-15 ( 0.0235 ) |
MUTATIONRATE_SILENT | 0.116 | 55.7 | 5.02e-13 | 1.95e-07 | 415 | 5.91e-09 ( 7.62e-08 ) | 5.02e-13 ( 0.121 ) |
Table S7. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 197 | |
STAGE II | 40 | |
STAGE III | 113 | |
STAGE IV | 67 | |
Significant variables | N = 0 |
Table S8. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'
PATHOLOGY.T.STAGE | Mean (SD) | 1.93 (0.96) |
N | ||
1 | 202 | |
2 | 49 | |
3 | 160 | |
4 | 6 | |
Significant variables | N = 0 |
Table S9. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'
PATHOLOGY.N.STAGE | Labels | N |
class0 | 192 | |
class1 | 12 | |
Significant variables | N = 0 |
Table S10. Basic characteristics of clinical feature: 'PATHOLOGY.M.STAGE'
PATHOLOGY.M.STAGE | Labels | N |
M0 | 350 | |
M1 | 67 | |
Significant variables | N = 0 |
Table S11. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 146 | |
MALE | 271 | |
Significant variables | N = 0 |
No variable related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S12. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 91.74 (21) |
Score | N | |
0 | 1 | |
90 | 9 | |
100 | 13 | |
Significant variables | N = 0 |
Table S13. Basic characteristics of clinical feature: 'RACE'
RACE | Labels | N |
ASIAN | 7 | |
BLACK OR AFRICAN AMERICAN | 14 | |
WHITE | 390 | |
Significant variables | N = 1 |
Table S14. Get Full Table List of one variable differentially expressed by 'RACE'
ANOVA_P | Q | |
---|---|---|
MUTATIONRATE_NONSYNONYMOUS | 0.04699 | 0.094 |
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Expresson data file = KIRC-TP.patients.counts_and_rates.txt
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Clinical data file = KIRC-TP.merged_data.txt
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Number of patients = 417
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Number of variables = 2
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Number of clinical features = 11
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 two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes using 't.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.