SKCM-TM: Correlation between mRNAseq expression and clinical features

Maintained by Juok Cho (Broad Institute)

Overview

Introduction

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

Summary

Testing the association between 18101 genes and 3 clinical features across 124 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

3 genes correlated to 'AGE'.

F7|2155 , XCL2|6846 , FAM19A5|25817

16 genes correlated to 'GENDER'.

ZFY|7544 , XIST|7503 , PRKY|5616 , RPS4Y1|6192 , DDX3Y|8653 , ...

No genes correlated to 'Time to Death'

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=3	older	N=0	younger	N=3
GENDER	t test	N=16	male	N=12	female	N=4

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)	0.2-131.1 (median=53.4)
	censored	N = 6
	death	N = 8

	Significant markers	N = 0

Clinical variable #2: 'AGE'

3 genes related to 'AGE'.

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


AGE	Mean (SD)	56.27 (14)
	Significant markers	N = 3
	pos. correlated	0
	neg. correlated	3

List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

Table S3. Get Full Table List of 3 genes significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
F7\|2155	-0.9455	0	0
XCL2\|6846	-0.8901	0	0
FAM19A5\|25817	-0.9187	1.331e-06	0.0241

Figure S1. Get High-res Image As an example, this figure shows the association of F7|2155 to 'AGE'. P value = 0 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #3: 'GENDER'

16 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	45
	MALE	79

	Significant markers	N = 16
	Higher in MALE	12
	Higher in FEMALE	4

List of top 10 genes differentially expressed by 'GENDER'

Table S5. Get Full Table List of top 10 genes differentially expressed by 'GENDER'

	T(pos if higher in 'MALE')	ttestP	Q	AUC
ZFY\|7544	27.44	3.916e-51	7.09e-47	0.9979
XIST\|7503	-19.2	1.514e-36	2.74e-32	0.9768
PRKY\|5616	21.9	2.516e-34	4.55e-30	0.9983
RPS4Y1\|6192	25.09	1.013e-32	1.83e-28	1
DDX3Y\|8653	27.44	1.552e-30	2.81e-26	1
KDM5D\|8284	25.8	3.24e-23	5.86e-19	1
TSIX\|9383	-13.26	4.944e-22	8.94e-18	0.9731
EIF1AY\|9086	23.58	3.337e-18	6.03e-14	1
TTTY15\|64595	19.52	7.032e-16	1.27e-11	0.9968
USP9Y\|8287	18.51	4.141e-15	7.49e-11	0.9981

Figure S2. Get High-res Image As an example, this figure shows the association of ZFY|7544 to 'GENDER'. P value = 3.92e-51 with T-test analysis.

Methods & Data

Input

Expresson data file = SKCM-TM.uncv2.mRNAseq_RSEM_normalized_log2.txt
Clinical data file = SKCM.clin.merged.picked.txt
Number of patients = 124
Number of genes = 18101
Number of clinical features = 3

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

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] 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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