Correlation between miRseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
23 May 2013 | analyses__2013_05_23
Maintainer Information
Citation Information
doi:10.7908/C1N58JF5
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1N58JF5
Overview
Introduction

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

Summary

Testing the association between 475 genes and 4 clinical features across 155 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

- 1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.

- HSA-MIR-378

- No genes correlated to 'AGE', 'LYMPH.NODE.METASTASIS', and 'COMPLETENESS.OF.RESECTION'.

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 |  |  |  |
LYMPH NODE METASTASIS | t test | N=0 |  |  |  |
COMPLETENESS OF RESECTION | ANOVA test | N=0 |  |  |  |
NUMBER OF LYMPH NODES | Spearman correlation test | N=1 | higher number.of.lymph.nodes | N=0 | lower number.of.lymph.nodes | N=1

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE | Mean (SD) | 60.37 (6.8)
| Significant markers | N = 0

Clinical variable #2: 'LYMPH.NODE.METASTASIS'

No gene related to 'LYMPH.NODE.METASTASIS'.

Table S2. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS | Labels | N
| N0 | 122
| N1 | 14
| Significant markers | N = 0

Clinical variable #3: 'COMPLETENESS.OF.RESECTION'

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

Table S3. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION | Labels | N
| R0 | 118
| R1 | 29
| RX | 2
| Significant markers | N = 0

Clinical variable #4: 'NUMBER.OF.LYMPH.NODES'

One gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S4. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.2 (0.74)
| Significant markers | N = 1
| pos. correlated | 0
| neg. correlated | 1

List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

Table S5. Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-378 | -0.3386 | 5.515e-05 | 0.0261

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-378 to 'NUMBER.OF.LYMPH.NODES'. P value = 5.52e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Methods & Data
Input

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

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

- Number of patients = 155

- Number of genes = 475

- 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

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

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] 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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