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

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.

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)

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