This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 474 genes and 3 clinical features across 81 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

HSAMIR30C2

5 genes correlated to 'NEOADJUVANT.THERAPY'.

HSAMIR200B , HSAMIR200C , HSAMIR3615 , HSAMIR29B2 , HSAMIR3605

No genes correlated to 'AGE'
Complete statistical result table is provided in Supplement Table 1
Clinical feature  Statistical test  Significant genes  Associated with  Associated with  

AGE  Spearman correlation test  N=0  
RADIATIONS RADIATION REGIMENINDICATION  t test  N=1  yes  N=0  no  N=1 
NEOADJUVANT THERAPY  t test  N=5  yes  N=1  no  N=4 
AGE  Mean (SD)  61.11 (6.7) 
Significant markers  N = 0 
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION  Labels  N 
NO  5  
YES  76  
Significant markers  N = 1  
Higher in YES  0  
Higher in NO  1 
T(pos if higher in 'YES')  ttestP  Q  AUC  

HSAMIR30C2  5.51  4.417e05  0.0194  0.8289 
NEOADJUVANT.THERAPY  Labels  N 
NO  3  
YES  78  
Significant markers  N = 5  
Higher in YES  1  
Higher in NO  4 
T(pos if higher in 'YES')  ttestP  Q  AUC  

HSAMIR200B  7.63  8.53e10  2.87e07  0.9145 
HSAMIR200C  9.14  1.405e09  4.72e07  0.8846 
HSAMIR3615  6.5  2.763e06  0.000926  0.7911 
HSAMIR29B2  9.32  5.163e05  0.0172  0.9444 
HSAMIR3605  7.53  0.000125  0.0416  0.8846 

Expresson data file = PRAD.miRseq_RPKM_log2.txt

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

Number of patients = 81

Number of genes = 474

Number of clinical features = 3
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.