Correlation between miRseq expression and clinical features

Adrenocortical Carcinoma (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/C14Q7SKB

Overview

Introduction

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

Summary

Testing the association between 518 miRs and 6 clinical features across 34 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one miRs.

1 miR correlated to 'AGE'.

HSA-MIR-556

2 miRs correlated to 'PATHOLOGY.N.STAGE'.

HSA-MIR-766 , HSA-MIR-95

No miRs correlated to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 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
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=1	older	N=1	younger	N=0
NEOPLASM DISEASESTAGE	ANOVA test	N=0
PATHOLOGY T STAGE	Spearman correlation test	N=0
PATHOLOGY N STAGE	t test	N=2	class1	N=2	class0	N=0
GENDER	t test	N=0

Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

Table S1. Basic characteristics of clinical feature: 'Time to Death'


Time to Death	Duration (Months)	6.9-121.2 (median=29.8)
	censored	N = 26
	death	N = 8

	Significant markers	N = 0

Clinical variable #2: 'AGE'

One miR related to 'AGE'.

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


AGE	Mean (SD)	50.82 (14)
	Significant markers	N = 1
	pos. correlated	1
	neg. correlated	0

List of one miR significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of one miR significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
HSA-MIR-556	0.7991	2.379e-05	0.0123

Figure S1. Get High-res Image As an example, this figure shows the association of HSA-MIR-556 to 'AGE'. P value = 2.38e-05 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S4. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	4
	STAGE II	13
	STAGE III	4
	STAGE IV	8

	Significant markers	N = 0

Clinical variable #4: 'PATHOLOGY.T.STAGE'

No miR related to 'PATHOLOGY.T.STAGE'.

Table S5. Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'


PATHOLOGY.T.STAGE	Mean (SD)	2.55 (1.1)
		N
	1	4
	2	14
	3	2
	4	9

	Significant markers	N = 0

Clinical variable #5: 'PATHOLOGY.N.STAGE'

2 miRs related to 'PATHOLOGY.N.STAGE'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'


PATHOLOGY.N.STAGE	Labels	N
	class0	26
	class1	4

	Significant markers	N = 2
	Higher in class1	2
	Higher in class0	0

List of 2 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

Table S7. Get Full Table List of 2 miRs differentially expressed by 'PATHOLOGY.N.STAGE'

	T(pos if higher in 'class1')	ttestP	Q	AUC
HSA-MIR-766	5.69	4.966e-06	0.00218	0.9231
HSA-MIR-95	5.14	9.735e-05	0.0427	0.9038

Figure S2. Get High-res Image As an example, this figure shows the association of HSA-MIR-766 to 'PATHOLOGY.N.STAGE'. P value = 4.97e-06 with T-test analysis.

Clinical variable #6: 'GENDER'

No miR related to 'GENDER'.

Table S8. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	17
	MALE	17

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = ACC-TP.miRseq_RPKM_log2.txt
Clinical data file = ACC-TP.merged_data.txt
Number of patients = 34
Number of miRs = 518
Number of clinical features = 6

Survival analysis

For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels

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] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)

[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[3] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

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

[5] 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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