Correlation between gene methylation status and clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C1Z60MVG
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Z60MVG
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 19848 genes and 3 clinical features across 61 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes.

- 3 genes correlated to 'GENDER'.

- DKFZP434L187 , CHTF8 , HAS3

- No genes correlated to 'AGE', and 'RACE'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature | Statistical test | Significant genes | Associated with |  | Associated with |
--- | --- | --- | --- | --- | --- | ---
AGE | Spearman correlation test | N=0 |  |  |  |
GENDER | Wilcoxon test | N=3 | male | N=3 | female | N=0
RACE | Kruskal-Wallis test | N=0 |  |  |  |

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE | Mean (SD) | 49.31 (14)
| Significant markers | N = 0

Clinical variable #2: 'GENDER'

3 genes related to 'GENDER'.

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

GENDER | Labels | N
| FEMALE | 40
| MALE | 21
| Significant markers | N = 3
| Higher in MALE | 3
| Higher in FEMALE | 0

List of 3 genes differentially expressed by 'GENDER'

Table S3. Get Full Table List of 3 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

| W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC
--- | --- | --- | --- | ---
DKFZP434L187 | 767 | 1.443e-07 | 0.00286 | 0.9131
CHTF8 | 718 | 6.306e-06 | 0.125 | 0.8548
HAS3 | 718 | 6.306e-06 | 0.125 | 0.8548

Clinical variable #3: 'RACE'

No gene related to 'RACE'.

Table S4. Basic characteristics of clinical feature: 'RACE'

RACE | Labels | N
| AMERICAN INDIAN OR ALASKA NATIVE | 1
| ASIAN | 3
| BLACK OR AFRICAN AMERICAN | 7
| WHITE | 48
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = PCPG-TP.meth.by_min_clin_corr.data.txt

- Clinical data file = PCPG-TP.merged_data.txt

- Number of patients = 61

- Number of genes = 19848

- 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

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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