Correlation between miRseq expression and clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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/C1BK1B0J

Overview

Introduction

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

Summary

Testing the association between 528 miRs and 2 clinical features across 10 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one miRs.

No miRs correlated to 'AGE', and 'GENDER'.

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 Q value < 0.05.

Clinical feature	Statistical test	Significant miRs	Associated with		Associated with
AGE	Spearman correlation test	N=0
GENDER	t test	N=0

Clinical variable #1: 'AGE'

No miR related to 'AGE'.

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


AGE	Mean (SD)	48.3 (11)
	Significant markers	N = 0

Clinical variable #2: 'GENDER'

No miR related to 'GENDER'.

Table S2. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	7
	MALE	3

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = PCPG-TP.miRseq_RPKM_log2.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 10
Number of miRs = 528
Number of clinical features = 2

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

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[3] 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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