Correlation between mRNA expression and clinical features
Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)
22 February 2013 | analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C1KK990B
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1KK990B
Overview
Introduction

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

Summary

Testing the association between 17814 genes and 4 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', and 'TUMOR.STAGE'.

Results
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
TUMOR STAGE | Spearman correlation test | N=0 |  |  |  |

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'

GENDER | Labels | N
| FEMALE | 4
| 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)
|  | N
| 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.

Clinical variable #4: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S6. Basic characteristics of clinical feature: 'TUMOR.STAGE'

TUMOR.STAGE | Mean (SD) | 1.78 (1.1)
|  | N
| Stage 1 | 5
| Stage 2 | 2
| Stage 3 | 1
| Stage 4 | 1
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = KIRP-TP.medianexp.txt

- Clinical data file = KIRP-TP.clin.merged.picked.txt

- Number of patients = 16

- Number of genes = 17814

- Number of clinical features = 4

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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[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)
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