Kidney Renal Papillary Cell Carcinoma: Correlation between mRNA expression and clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)

This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.


Testing the association between 17814 genes and 3 clinical features across 16 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

  • 2 genes correlated to 'GENDER'.

    • RPS4Y1 ,  NAP1L3

  • 1 gene correlated to 'PATHOLOGY.T'.

    • SHKBP1

  • No genes correlated to 'AGE'

Overview of the results

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 genes that are significantly associated with each clinical feature at Q value < 0.05.

Clinical feature Statistical test Significant genes Associated with                 Associated with
AGE Spearman correlation test   N=0        
GENDER t test N=2 male N=1 female N=1
PATHOLOGY T Spearman correlation test N=1 higher pT N=0 lower pT N=1
Clinical variable #1: 'AGE'

No gene related to 'AGE'.

Table S1.  Basic characteristics of clinical feature: 'AGE'

AGE Mean (SD) 57.94 (11)
  Significant markers N = 0
Clinical variable #2: 'GENDER'

2 genes related to 'GENDER'.

Table S2.  Basic characteristics of clinical feature: 'GENDER'

  MALE 12
  Significant markers N = 2
  Higher in MALE 1
  Higher in FEMALE 1
List of 2 genes differentially expressed by 'GENDER'

Table S3.  Get Full Table List of 2 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
RPS4Y1 8.47 7.923e-07 0.0141 1
NAP1L3 -8.33 1.936e-06 0.0345 1

Figure S1.  Get High-res Image As an example, this figure shows the association of RPS4Y1 to 'GENDER'. P value = 7.92e-07 with T-test analysis.

Clinical variable #3: 'PATHOLOGY.T'

One gene related to 'PATHOLOGY.T'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.T'

PATHOLOGY.T Mean (SD) 1.69 (0.7)
  T1 7
  T2 7
  T3 2
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

Table S5.  Get Full Table List of one gene significantly correlated to 'PATHOLOGY.T' by Spearman correlation test

SpearmanCorr corrP Q
SHKBP1 -0.9131 7.931e-07 0.0141

Figure S2.  Get High-res Image As an example, this figure shows the association of SHKBP1 to 'PATHOLOGY.T'. P value = 7.93e-07 with Spearman correlation analysis.

Methods & Data
  • Expresson data file = KIRP.medianexp.txt

  • Clinical data file = KIRP.clin.merged.picked.txt

  • Number of patients = 16

  • Number of genes = 17814

  • Number of clinical features = 3

Correlation analysis

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

Student's t-test analysis

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

Q value calculation

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.

Download Results

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

[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)