Correlation between RPPA expression and clinical features

Overview

Introduction

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

Summary

Testing the association between 171 genes and 9 clinical features across 130 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

2 genes correlated to 'HISTOLOGICAL.TYPE'.

RAF1|C-RAF-R-V , BCL2L1|BCL-X-R-C

No genes correlated to 'Time to Death', 'AGE', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', 'TUMOR.STAGE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

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=0
HISTOLOGICAL TYPE	t test	N=2	rectal mucinous adenocarcinoma	N=1	rectal adenocarcinoma	N=1
PATHOLOGY T	Spearman correlation test	N=0
PATHOLOGY N	Spearman correlation test	N=0
PATHOLOGICSPREAD(M)	ANOVA test	N=0
TUMOR STAGE	Spearman correlation test	N=0
RADIATIONS RADIATION REGIMENINDICATION	t test	N=0

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)	0.2-121.1 (median=6.2)
	censored	N = 91
	death	N = 11

	Significant markers	N = 0

Clinical variable #2: 'AGE'

No gene related to 'AGE'.

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


AGE	Mean (SD)	65.55 (12)
	Significant markers	N = 0

Clinical variable #3: 'GENDER'

No gene related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	60
	MALE	70

	Significant markers	N = 0

Clinical variable #4: 'HISTOLOGICAL.TYPE'

2 genes related to 'HISTOLOGICAL.TYPE'.

Table S4. Basic characteristics of clinical feature: 'HISTOLOGICAL.TYPE'


HISTOLOGICAL.TYPE	Labels	N
	RECTAL ADENOCARCINOMA	117
	RECTAL MUCINOUS ADENOCARCINOMA	10

	Significant markers	N = 2
	Higher in RECTAL MUCINOUS ADENOCARCINOMA	1
	Higher in RECTAL ADENOCARCINOMA	1

List of 2 genes differentially expressed by 'HISTOLOGICAL.TYPE'

Table S5. Get Full Table List of 2 genes differentially expressed by 'HISTOLOGICAL.TYPE'

	T(pos if higher in 'RECTAL MUCINOUS ADENOCARCINOMA')	ttestP	Q	AUC
RAF1\|C-RAF-R-V	5.18	0.0001255	0.0215	0.8385
BCL2L1\|BCL-X-R-C	-4.9	0.0002677	0.0455	0.8068

Figure S1. Get High-res Image As an example, this figure shows the association of RAF1|C-RAF-R-V to 'HISTOLOGICAL.TYPE'. P value = 0.000126 with T-test analysis.

Clinical variable #5: 'PATHOLOGY.T'

No gene related to 'PATHOLOGY.T'.

Table S6. Basic characteristics of clinical feature: 'PATHOLOGY.T'


PATHOLOGY.T	Mean (SD)	2.82 (0.62)
		N
	T1	5
	T2	23
	T3	91
	T4	10

	Significant markers	N = 0

Clinical variable #6: 'PATHOLOGY.N'

No gene related to 'PATHOLOGY.N'.

Table S7. Basic characteristics of clinical feature: 'PATHOLOGY.N'


PATHOLOGY.N	Mean (SD)	0.7 (0.79)
		N
	N0	64
	N1	37
	N2	26

	Significant markers	N = 0

Clinical variable #7: 'PATHOLOGICSPREAD(M)'

No gene related to 'PATHOLOGICSPREAD(M)'.

Table S8. Basic characteristics of clinical feature: 'PATHOLOGICSPREAD(M)'


PATHOLOGICSPREAD(M)	Labels	N
	M0	98
	M1	18
	M1A	2
	MX	10

	Significant markers	N = 0

Clinical variable #8: 'TUMOR.STAGE'

No gene related to 'TUMOR.STAGE'.

Table S9. Basic characteristics of clinical feature: 'TUMOR.STAGE'


TUMOR.STAGE	Mean (SD)	2.48 (0.96)
		N
	Stage 1	21
	Stage 2	40
	Stage 3	42
	Stage 4	19

	Significant markers	N = 0

Clinical variable #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.

Table S10. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'


RADIATIONS.RADIATION.REGIMENINDICATION	Labels	N
	NO	5
	YES	125

	Significant markers	N = 0

Methods & Data

Input

Expresson data file = READ-TP.rppa.txt
Clinical data file = READ-TP.clin.merged.picked.txt
Number of patients = 130
Number of genes = 171
Number of clinical features = 9

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.

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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