Correlation between RPPA expression and clinical features

Pheochromocytoma and Paraganglioma (Primary solid tumor)

02 April 2015 | analyses__2015_04_02

Maintainer Information

Citation Information

Maintained by Juok Cho (Broad Institute)

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between RPPA expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16Q1WBS

Overview

Introduction

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

Summary

Testing the association between 192 genes and 3 clinical features across 79 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.

1 gene correlated to 'YEARS_TO_BIRTH'.

EEF2K|EEF2K-R-V

2 genes correlated to 'GENDER'.

RPS6KA1|P90RSK_PT359_S363-R-C , BIRC2 |CIAP-R-V

1 gene correlated to 'RACE'.

GSK3A GSK3B|GSK3-ALPHA-BETA-M-V

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
YEARS_TO_BIRTH	Spearman correlation test	N=1	older	N=0	younger	N=1
GENDER	Wilcoxon test	N=2	male	N=2	female	N=0
RACE	Kruskal-Wallis test	N=1

Clinical variable #1: 'YEARS_TO_BIRTH'

One gene related to 'YEARS_TO_BIRTH'.

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


YEARS_TO_BIRTH	Mean (SD)	47.77 (15)
	Significant markers	N = 1
	pos. correlated	0
	neg. correlated	1

List of one gene differentially expressed by 'YEARS_TO_BIRTH'

Table S2. Get Full Table List of one gene significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

	SpearmanCorr	corrP	Q
EEF2K\|EEF2K-R-V	-0.381	0.0005318	0.102

Clinical variable #2: 'GENDER'

2 genes related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	40
	MALE	39

	Significant markers	N = 2
	Higher in MALE	2
	Higher in FEMALE	0

List of 2 genes differentially expressed by 'GENDER'

Table S4. Get Full Table List of 2 genes differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

	W(pos if higher in 'MALE')	wilcoxontestP	Q	AUC
RPS6KA1\|P90RSK_PT359_S363-R-C	416	0.0003647	0.07	0.7333
BIRC2 \|CIAP-R-V	1097	0.001912	0.184	0.7032

Clinical variable #3: 'RACE'

One gene related to 'RACE'.

Table S5. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	4
	BLACK OR AFRICAN AMERICAN	9
	WHITE	65

	Significant markers	N = 1

List of one gene differentially expressed by 'RACE'

Table S6. Get Full Table List of one gene differentially expressed by 'RACE'

	kruskal_wallis_P	Q
GSK3A GSK3B\|GSK3-ALPHA-BETA-M-V	0.0001786	0.0343

Methods & Data

Input

Expresson data file = PCPG-TP.rppa.txt
Clinical data file = PCPG-TP.merged_data.txt
Number of patients = 79
Number of genes = 192
Number of clinical features = 3

Selected clinical features

For clinical features selected for this analysis and their value conozzle.versions, please find a documentation on selected CDEs .
Survival time data

Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

if 'vital_status'==0(alive),

if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

if 'vital_status'==NA,excludes this case in survival analysis and report the case.

cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

This analysis excluded clinical variables that has only NA values.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)

[2] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)

[3] 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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