Correlation between mutation rate and clinical features

Overview

Introduction

This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.

Summary

Testing the association between 2 variables and 6 clinical features across 197 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.

2 variables correlated to 'AGE'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

2 variables correlated to 'AGE_mutation.rate'.

MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT

No variables correlated to 'Time to Death', 'GENDER', 'RACE', and 'ETHNICITY'.

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 variables that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature	Statistical test	Significant variables	Associated with		Associated with
Time to Death	Cox regression test	N=0
AGE	Spearman correlation test	N=2	older	N=2	younger	N=0
AGE	Linear Regression Analysis	N=2
GENDER	Wilcoxon test	N=0
RACE	Kruskal-Wallis test	N=0
ETHNICITY	Wilcoxon test	N=0

Clinical variable #1: 'Time to Death'

No variable related to 'Time to Death'.

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


Time to Death	Duration (Months)	0.9-94.1 (median=12)
	censored	N = 65
	death	N = 108

	Significant variables	N = 0

Clinical variable #2: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	55.05 (16)
	Significant variables	N = 2
	pos. correlated	2
	neg. correlated	0

List of 2 variables associated with 'AGE'

Table S3. Get Full Table List of 2 variables significantly correlated to 'AGE' by Spearman correlation test

	SpearmanCorr	corrP	Q
MUTATIONRATE_NONSYNONYMOUS	0.2798	6.852e-05	0.000137
MUTATIONRATE_SILENT	0.1921	0.006844	0.00684

Clinical variable #3: 'AGE'

2 variables related to 'AGE'.

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


AGE	Mean (SD)	55.05 (16)
	Significant variables	N = 2

List of 2 variables associated with 'AGE'

Table S5. Get Full Table List of 2 variables significantly correlated to 'AGE' by Linear regression analysis [lm (mutation rate ~ age)]. Compared to a correlation analysis testing for interdependence of the variables, a regression model attempts to describe the dependence of a variable on one (or more) explanatory variables assuming that there is a one-way causal effect from the explanatory variable(s) to the response variable. If 'Residuals vs Fitted' plot (a standard residual plot) shows a random pattern indicating a good fit for a linear model, it explains linear regression relationship between Mutation rate and age factor. Adj.R-squared (= Explained variation / Total variation) indicates regression model's explanatory power.

	Adj.R.squared	F	P	Residual.std.err	DF	coef(intercept)	coef.p(intercept)
MUTATIONRATE_NONSYNONYMOUS	0.0551	12.4	0.000526	1.84e-07	195	2.86e-09 ( 1.47e-07 )	0.000526 ( 0.00184 )
MUTATIONRATE_SILENT	0.0259	6.22	0.0135	6.93e-08	195	7.63e-10 ( 3.99e-08 )	0.0135 ( 0.0241 )

Clinical variable #4: 'GENDER'

No variable related to 'GENDER'.

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


GENDER	Labels	N
	FEMALE	91
	MALE	106

	Significant variables	N = 0

Clinical variable #5: 'RACE'

No variable related to 'RACE'.

Table S7. Basic characteristics of clinical feature: 'RACE'


RACE	Labels	N
	ASIAN	2
	BLACK OR AFRICAN AMERICAN	15
	WHITE	178

	Significant variables	N = 0

Clinical variable #6: 'ETHNICITY'

No variable related to 'ETHNICITY'.

Table S8. Basic characteristics of clinical feature: 'ETHNICITY'


ETHNICITY	Labels	N
	HISPANIC OR LATINO	3
	NOT HISPANIC OR LATINO	191

	Significant variables	N = 0

Methods & Data

Input

Expresson data file = LAML-TB.patients.counts_and_rates.txt
Clinical data file = LAML-TB.merged_data.txt
Number of patients = 197
Number of variables = 2
Number of clinical features = 6

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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