Correlation between mRNAseq expression and clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Liver Hepatocellular Carcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1MW2F33

Overview

Introduction

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

Summary

Testing the association between 17848 genes and 4 clinical features across 60 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

8 genes correlated to 'GENDER'.

XIST|7503 , TSIX|9383 , ZFY|7544 , KDM5C|8242 , EIF1AX|1964 , ...

1 gene correlated to 'COMPLETENESS.OF.RESECTION'.

SLC16A11|162515

No genes correlated to 'Time to Death', and 'AGE'.

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
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=0
GENDER	t test	N=8	male	N=4	female	N=4
COMPLETENESS OF RESECTION	ANOVA test	N=1

Clinical variable #1: 'Time to Death'

No gene related to 'Time to Death'.

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


Time to Death	Duration (Months)	0.1-83.6 (median=14.3)
	censored	N = 32
	death	N = 23

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	59.66 (15)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

8 genes related to 'GENDER'.

Table S3. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	23
	MALE	37

	Significant markers	N = 8
	Higher in MALE	4
	Higher in FEMALE	4

List of 8 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 8 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
XIST\|7503	-14.22	1.96e-15	3.5e-11	0.9861
TSIX\|9383	-9.48	5.074e-10	9.05e-06	0.9814
ZFY\|7544	12.05	2.187e-08	0.00039	0.9919
KDM5C\|8242	-6.19	6.706e-08	0.0012	0.8719
EIF1AX\|1964	-6.31	7.425e-08	0.00132	0.8754
NLGN4Y\|22829	9.56	1.155e-07	0.00206	0.9811
PRKY\|5616	9.24	6.111e-07	0.0109	0.9842
TERF1\|7013	5.46	1.228e-06	0.0219	0.8496

Figure S1. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.96e-15 with T-test analysis.

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

One gene related to 'COMPLETENESS.OF.RESECTION'.

Table S5. Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'


COMPLETENESS.OF.RESECTION	Labels	N
	R0	43
	R1	7
	R2	1
	RX	6

	Significant markers	N = 1

List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S6. Get Full Table List of one gene differentially expressed by 'COMPLETENESS.OF.RESECTION'

	ANOVA_P	Q
SLC16A11\|162515	5.168e-08	0.000922

Figure S2. Get High-res Image As an example, this figure shows the association of SLC16A11|162515 to 'COMPLETENESS.OF.RESECTION'. P value = 5.17e-08 with ANOVA analysis.

Methods & Data

Input

Expresson data file = LIHC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = LIHC-TP.clin.merged.picked.txt
Number of patients = 60
Number of genes = 17848
Number of clinical features = 4

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

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

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

[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)

Made with Nozzle