Correlation between miRseq expression and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
17 October 2014 | analyses__2014_10_17
Maintainer Information
Citation Information
doi:10.7908/C1TH8KMT
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/C1TH8KMT
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 657 miRs and 4 clinical features across 13 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one miRs.

- 1 miR correlated to 'PATHOLOGY.T.STAGE'.

- HSA-MIR-3199-2

- No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.N.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 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 |  |  |  |
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=0 | lower stage | N=1
PATHOLOGY N STAGE | Wilcoxon test | N=0 |  |  |  |

Clinical variable #1: 'AGE'

No miR related to 'AGE'.

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

AGE | Mean (SD) | 30.92 (7.4)
| Significant markers | N = 0

Clinical variable #2: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S2. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE | Labels | N
| STAGE I | 2
| STAGE IB | 1
| STAGE IIA | 1
| STAGE IIC | 1
| STAGE IIIC | 1
| STAGE IS | 7
| Significant markers | N = 0

Clinical variable #3: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

Table S3. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE | Mean (SD) | 1.69 (0.63)
|  | N
| 1 | 5
| 2 | 7
| 3 | 1
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one miR differentially expressed by 'PATHOLOGY.T.STAGE'

Table S4. Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-3199-2 | -0.8559 | 0.000382 | 0.25

Clinical variable #4: 'PATHOLOGY.N.STAGE'

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

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE | Labels | N
| class0 | 5
| class1 | 2
| Significant markers | N = 0

Methods & Data
Input

- Expresson data file = TGCT-TP.miRseq_RPKM_log2.txt

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

- Number of patients = 13

- Number of miRs = 657

- 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

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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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)
Copyright © 2015 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle