Correlation between miRseq expression and clinical features
Pheochromocytoma and Paraganglioma (Primary solid tumor)
15 July 2014 | analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C19C6W7T
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19C6W7T
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 524 miRs and 3 clinical features across 61 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, no clinical feature related to at least one miRs.

- No miRs correlated to 'AGE', 'GENDER', 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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

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

Clinical variable #1: 'AGE'

No miR 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'

No miR related to 'GENDER'.

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

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

Clinical variable #3: 'RACE'

No miR related to 'RACE'.

Table S3. 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.miRseq_RPKM_log2.txt

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

- Number of patients = 61

- Number of miRs = 524

- 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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