Correlation between mRNA expression and clinical features

Lung Adenocarcinoma (MOLECULAR_NONSMOKER)

07 February 2013 | awg_luad__2013_02_07

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TB150D

Overview

Introduction

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

Summary

Testing the association between 17814 genes and 8 clinical features across 9 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.

No genes correlated to 'AGE', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'TUMOR.STAGE', 'NUMBERPACKYEARSSMOKED', 'TOBACCOSMOKINGHISTORYINDICATOR', and 'YEAROFTOBACCOSMOKINGONSET'.

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
AGE	Spearman correlation test	N=0
GENDER	t test	N=0
PATHOLOGY T	t test	N=0
PATHOLOGY N	Spearman correlation test	N=0
TUMOR STAGE	Spearman correlation test	N=0
NUMBERPACKYEARSSMOKED	Spearman correlation test	N=0
TOBACCOSMOKINGHISTORYINDICATOR	ANOVA test	N=0
YEAROFTOBACCOSMOKINGONSET	Spearman correlation test	N=0

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	65.89 (12)
	Significant markers	N = 0

Clinical variable #2: 'GENDER'

No gene related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	5
	MALE	4

	Significant markers	N = 0

Clinical variable #3: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S3. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Labels	N
	T1	2
	T2	7

	Significant markers	N = 0

Clinical variable #4: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S4. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.78 (0.97)
		N
	N0	5
	N1	1
	N2	3

	Significant markers	N = 0

Clinical variable #5: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S5. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	1.89 (1.2)
		N
	Stage 1	5
	Stage 2	1
	Stage 3	2
	Stage 4	1

	Significant markers	N = 0

Clinical variable #6: 'NUMBERPACKYEARSSMOKED'

No gene related to 'NUMBERPACKYEARSSMOKED'.

Table S6. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'


NUMBERPACKYEARSSMOKED	Mean (SD)	46.5 (11)
	Value	N
	30	1
	50	2
	56	1

	Significant markers	N = 0

Clinical variable #7: 'TOBACCOSMOKINGHISTORYINDICATOR'

No gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.

Table S7. Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'


TOBACCOSMOKINGHISTORYINDICATOR	Labels	N
	CURRENT REFORMED SMOKER FOR > 15 YEARS	2
	CURRENT SMOKER	3
	LIFELONG NON-SMOKER	4

	Significant markers	N = 0

Clinical variable #8: 'YEAROFTOBACCOSMOKINGONSET'

No gene related to 'YEAROFTOBACCOSMOKINGONSET'.

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


YEAROFTOBACCOSMOKINGONSET	Mean (SD)	1963.4 (9.7)
	Value	N
	1955	1
	1956	1
	1962	1
	1965	1
	1979	1

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = MOLECULAR_NONSMOKER.medianexp.txt
Clinical data file = MOLECULAR_NONSMOKER.clin.merged.picked.txt
Number of patients = 9
Number of genes = 17814
Number of clinical features = 8

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)

Made with Nozzle