Correlation between RPPA expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C1B56H28
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1B56H28
Overview
Introduction

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

Summary

Testing the association between 189 genes and 5 clinical features across 137 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

- 3 genes correlated to 'PATHOLOGY.T.STAGE'.

- EIF4G1|EIF4G-R-C , DVL3|DVL3-R-V , EEF2K|EEF2K-R-V

- No genes correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'COMPLETENESS.OF.RESECTION', and 'NUMBER.OF.LYMPH.NODES'.

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 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=3 | higher stage | N=3 | lower stage | N=0
PATHOLOGY N STAGE | t test | N=0 |  |  |  |
COMPLETENESS OF RESECTION | ANOVA test | N=0 |  |  |  |
NUMBER OF LYMPH NODES | 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) | 60.23 (7.1)
| Significant markers | N = 0

Clinical variable #2: 'PATHOLOGY.T.STAGE'

3 genes related to 'PATHOLOGY.T.STAGE'.

Table S2. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 2.65 (0.55)
|  | N
| 2 | 53
| 3 | 79
| 4 | 5
| Significant markers | N = 3
| pos. correlated | 3
| neg. correlated | 0

List of 3 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S3. Get Full Table List of 3 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
EIF4G1|EIF4G-R-C | 0.3464 | 3.38e-05 | 0.00639
DVL3|DVL3-R-V | 0.3391 | 5.053e-05 | 0.0095
EEF2K|EEF2K-R-V | 0.3255 | 0.0001041 | 0.0195

Figure S1. Get High-res Image As an example, this figure shows the association of EIF4G1|EIF4G-R-C to 'PATHOLOGY.T.STAGE'. P value = 3.38e-05 with Spearman correlation analysis.

Clinical variable #3: 'PATHOLOGY.N.STAGE'

No gene related to 'PATHOLOGY.N.STAGE'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 109
| class1 | 13
| Significant markers | N = 0

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S5. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 100
| R1 | 27
| RX | 3
| Significant markers | N = 0

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S6. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.2 (0.76)
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = PRAD-TP.rppa.txt

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

- Number of patients = 137

- Number of genes = 189

- Number of clinical features = 5

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

ANOVA analysis

For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' 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

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.

- 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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] 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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