Correlation between miRseq expression and clinical features

Prostate Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

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.

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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