Correlation between gene methylation status and clinical features

Kidney Chromophobe (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J38QKD

Overview

Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.

Summary

Testing the association between 19761 genes and 4 clinical features across 22 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

768 genes correlated to 'LYMPH.NODE.METASTASIS'.

FADS1 , MIR1908 , ZNF167 , AIFM2 , SNX32 , ...

24 genes correlated to 'NEOPLASM.DISEASESTAGE'.

HS3ST2 , FAM135B , SOX21 , SPTBN4 , ODZ3 , ...

No genes correlated to 'AGE', 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 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
LYMPH NODE METASTASIS	ANOVA test	N=768
NEOPLASM DISEASESTAGE	ANOVA test	N=24

Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	51.64 (15)
	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	10
	MALE	12

	Significant markers	N = 0

Clinical variable #3: 'LYMPH.NODE.METASTASIS'

768 genes related to 'LYMPH.NODE.METASTASIS'.

Table S3. Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'


LYMPH.NODE.METASTASIS	Labels	N
	N0	11
	N1	1
	NX	10

	Significant markers	N = 768

List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S4. Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

	ANOVA_P	Q
FADS1	1.168e-40	2.31e-36
MIR1908	1.168e-40	2.31e-36
ZNF167	1.509e-37	2.98e-33
AIFM2	1.537e-35	3.04e-31
SNX32	4.807e-35	9.5e-31
XKR6	6.997e-35	1.38e-30
C4ORF19	9.398e-33	1.86e-28
C3ORF75	6.289e-31	1.24e-26
DLGAP3	8.335e-31	1.65e-26
TTC23L	1.321e-30	2.61e-26

Figure S1. Get High-res Image As an example, this figure shows the association of FADS1 to 'LYMPH.NODE.METASTASIS'. P value = 1.17e-40 with ANOVA analysis.

Clinical variable #4: 'NEOPLASM.DISEASESTAGE'

24 genes related to 'NEOPLASM.DISEASESTAGE'.

Table S5. Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'


NEOPLASM.DISEASESTAGE	Labels	N
	STAGE I	8
	STAGE II	10
	STAGE III	2
	STAGE IV	2

	Significant markers	N = 24

List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

Table S6. Get Full Table List of top 10 genes differentially expressed by 'NEOPLASM.DISEASESTAGE'

	ANOVA_P	Q
HS3ST2	4.797e-16	9.48e-12
FAM135B	5.538e-13	1.09e-08
SOX21	5.114e-12	1.01e-07
SPTBN4	5.706e-12	1.13e-07
ODZ3	7.982e-12	1.58e-07
FLJ44606	6.038e-11	1.19e-06
IGFBP3	6.8e-11	1.34e-06
SHE	3.694e-10	7.3e-06
TDRD10	3.694e-10	7.3e-06
SEMA3E	6.015e-10	1.19e-05

Figure S2. Get High-res Image As an example, this figure shows the association of HS3ST2 to 'NEOPLASM.DISEASESTAGE'. P value = 4.8e-16 with ANOVA analysis.

Methods & Data

Input

Expresson data file = KICH-TP.meth.by_min_expr_corr.data.txt
Clinical data file = KICH-TP.clin.merged.picked.txt
Number of patients = 22
Number of genes = 19761
Number of clinical features = 4

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)

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