This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 165 genes and 4 clinical features across 408 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

19 genes correlated to 'AGE'.

ESR1ERALPHARV , NOTCH3NOTCH3RC , SETD2SETD2RNA , STMN1STATHMINRV , METCMET_PY1235RC , ...

No genes correlated to 'Time to Death', 'GENDER', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant genes  Associated with  Associated with  

Time to Death  Cox regression test  N=0  
AGE  Spearman correlation test  N=19  older  N=6  younger  N=13 
GENDER  t test  N=0  
RADIATIONS RADIATION REGIMENINDICATION  t test  N=0 
Time to Death  Duration (Months)  0.1189 (median=24.5) 
censored  N = 336  
death  N = 44  
Significant markers  N = 0 
AGE  Mean (SD)  57.85 (13) 
Significant markers  N = 19  
pos. correlated  6  
neg. correlated  13 
SpearmanCorr  corrP  Q  

ESR1ERALPHARV  0.384  8.695e16  1.43e13 
NOTCH3NOTCH3RC  0.276  1.442e08  2.36e06 
SETD2SETD2RNA  0.2441  6.005e07  9.79e05 
STMN1STATHMINRV  0.2352  1.552e06  0.000251 
METCMET_PY1235RC  0.2158  1.09e05  0.00175 
KITCKITRV  0.2144  1.249e05  0.002 
CDC2CDK1RV  0.2094  2.016e05  0.0032 
ARARRV  0.2091  2.055e05  0.00325 
CDH3PCADHERINRC  0.2017  4.058e05  0.00637 
PDK1PDK1_PS241RV  0.1972  6.056e05  0.00945 
GENDER  Labels  N 
FEMALE  403  
MALE  5  
Significant markers  N = 0 

Expresson data file = BRCATP.rppa.txt

Clinical data file = BRCATP.clin.merged.picked.txt

Number of patients = 408

Number of genes = 165

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. 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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.