Correlation between miRseq expression and clinical features
Prostate Adenocarcinoma (Primary solid tumor)
23 September 2013 | analyses__2013_09_23
Maintainer Information
Citation Information
doi:10.7908/C12N50NN
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/C12N50NN
Overview
Introduction

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

Summary

Testing the association between 470 genes and 5 clinical features across 164 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

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

- HSA-MIR-30A , HSA-MIR-3676 , HSA-MIR-190 , HSA-MIR-598 , HSA-MIR-486

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

- HSA-MIR-378

- No genes correlated to 'AGE', 'PATHOLOGY.N.STAGE', 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 |  |  |  |
PATHOLOGY T STAGE | Spearman correlation test | N=5 | higher stage | N=0 | lower stage | N=5
PATHOLOGY N STAGE | 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) | 60.17 (6.9)
| Significant markers | N = 0

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

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

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

PATHOLOGY.T.STAGE | Mean (SD) | 2.6 (0.54)
|  | N
| 2 | 70
| 3 | 89
| 4 | 4
| Significant markers | N = 5
| pos. correlated | 0
| neg. correlated | 5

List of 5 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S3. Get Full Table List of 5 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-30A | -0.3348 | 1.251e-05 | 0.00588
HSA-MIR-3676 | -0.3217 | 2.983e-05 | 0.014
HSA-MIR-190 | -0.3094 | 6.162e-05 | 0.0288
HSA-MIR-598 | -0.3042 | 7.901e-05 | 0.0369
HSA-MIR-486 | -0.3002 | 9.878e-05 | 0.046

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-30A to 'PATHOLOGY.T.STAGE'. P value = 1.25e-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 | 130
| class1 | 14
| 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 | 122
| R1 | 30
| RX | 3
| Significant markers | N = 0

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

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

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

NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.19 (0.72)
| 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 S7. Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test

| SpearmanCorr | corrP | Q
--- | --- | --- | ---
HSA-MIR-378 | -0.3274 | 6.19e-05 | 0.0291

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-378 to 'NUMBER.OF.LYMPH.NODES'. P value = 6.19e-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 = 164

- Number of genes = 470

- 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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