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.

1 variable correlated to 'Time to Death'.

MUTATIONRATE_NONSYNONYMOUS

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

1 variable correlated to 'RACE'.

MUTATIONRATE_NONSYNONYMOUS

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
Clinical feature  Statistical test  Significant variables  Associated with  Associated with  

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  KruskalWallis 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  KruskalWallis test  N=1  
ETHNICITY  Wilcoxon test  N=0 
Time to Death  Duration (Months)  0.1120.6 (median=37.2) 
censored  N = 275  
death  N = 142  
Significant variables  N = 1  
associated with shorter survival  1  
associated with longer survival  0 
HazardRatio  Wald_P  Q  C_index  

MUTATIONRATE_NONSYNONYMOUS  Inf  0.04311  0.086  0.57 
AGE  Mean (SD)  60.69 (12) 
Significant variables  N = 2  
pos. correlated  2  
neg. correlated  0 
AGE  Mean (SD)  60.69 (12) 
Significant variables  N = 2 
NEOPLASM.DISEASESTAGE  Labels  N 
STAGE I  197  
STAGE II  40  
STAGE III  113  
STAGE IV  67  
Significant variables  N = 0 
PATHOLOGY.T.STAGE  Mean (SD)  1.93 (0.96) 
N  
1  202  
2  49  
3  160  
4  6  
Significant variables  N = 0 
PATHOLOGY.N.STAGE  Labels  N 
class0  192  
class1  12  
Significant variables  N = 0 
PATHOLOGY.M.STAGE  Labels  N 
M0  350  
M1  67  
Significant variables  N = 0 
GENDER  Labels  N 
FEMALE  146  
MALE  271  
Significant variables  N = 0 
No variable related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE  Mean (SD)  91.74 (21) 
Score  N  
0  1  
90  9  
100  13  
Significant variables  N = 0 
RACE  Labels  N 
ASIAN  7  
BLACK OR AFRICAN AMERICAN  14  
WHITE  390  
Significant variables  N = 1 
ANOVA_P  Q  

MUTATIONRATE_NONSYNONYMOUS  0.04699  0.094 

Expresson data file = KIRCTP.patients.counts_and_rates.txt

Clinical data file = KIRCTP.merged_data.txt

Number of patients = 417

Number of variables = 2

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. KaplanMeier 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 twotailed P values were estimated using 'cor.test' function in R
For multiclass clinical features (ordinal or nominal), oneway analysis of variance (Howell 2002) was applied to compare the log2expression levels between different clinical classes using 'anova' function in R
For twoclass clinical features, twotailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2expression 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.