Correlation between mRNAseq expression and clinical features

Sarcoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1R2106N

Overview

Introduction

This pipeline uses various statistical tests to identify mRNAs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 18170 genes and 4 clinical features across 111 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.

5 genes correlated to 'Time to Death'.

MRGPRF|116535 , POU4F1|5457 , COL11A2|1302 , DMRTA2|63950 , PRKCDBP|112464

16 genes correlated to 'GENDER'.

NCRNA00183|554203 , HDHD1A|8226 , CYORF15A|246126 , CA5BP|340591 , CYORF15B|84663 , ...

No genes correlated to 'AGE', and 'RACE'.

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 P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant genes	Associated with		Associated with
Time to Death	Cox regression test	N=5	shorter survival	N=3	longer survival	N=2
AGE	Spearman correlation test	N=0
GENDER	Wilcoxon test	N=16	male	N=16	female	N=0
RACE	Kruskal-Wallis test	N=0

Clinical variable #1: 'Time to Death'

5 genes related to 'Time to Death'.

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


Time to Death	Duration (Months)	0.1-143.4 (median=18.1)
	censored	N = 76
	death	N = 35

	Significant markers	N = 5
	associated with shorter survival	3
	associated with longer survival	2

List of 5 genes differentially expressed by 'Time to Death'

Table S2. Get Full Table List of 5 genes significantly associated with 'Time to Death' by Cox regression test

	HazardRatio	Wald_P	Q	C_index
MRGPRF\|116535	0.66	1.499e-06	0.027	0.291
POU4F1\|5457	1.61	1.636e-06	0.03	0.762
COL11A2\|1302	1.41	2.585e-06	0.047	0.661
DMRTA2\|63950	1.49	3.014e-06	0.055	0.712
PRKCDBP\|112464	0.46	5.193e-06	0.094	0.253

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	60.72 (14)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

16 genes related to 'GENDER'.

Table S4. Basic characteristics of clinical feature: 'GENDER'


GENDER	Labels	N
	FEMALE	60
	MALE	51

	Significant markers	N = 16
	Higher in MALE	16
	Higher in FEMALE	0

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'. 18 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
NCRNA00183\|554203	640	1.414e-07	0.00257	0.7908
HDHD1A\|8226	679	4.838e-07	0.00878	0.7781
CYORF15A\|246126	510	7.126e-07	0.0129	1
CA5BP\|340591	699	8.911e-07	0.0162	0.7716
CYORF15B\|84663	505	1.171e-06	0.0212	0.9902
PQLC3\|130814	2322	2.82e-06	0.0512	0.7588
GRK6\|2870	740	2.987e-06	0.0542	0.7582
PRPSAP1\|5635	753	4.333e-06	0.0786	0.7539
CBX1\|10951	754	4.457e-06	0.0809	0.7536
DBF4B\|80174	758	4.991e-06	0.0905	0.7523

Clinical variable #4: 'RACE'

No gene related to 'RACE'.

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


RACE	Labels	N
	ASIAN	4
	BLACK OR AFRICAN AMERICAN	8
	WHITE	74

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = SARC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 111
Number of genes = 18170
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

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] 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)

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