Correlation between gene methylation status and clinical features
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 19848 genes and 3 clinical features across 61 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes.

  • 3 genes correlated to 'GENDER'.

    • DKFZP434L187 ,  CHTF8 ,  HAS3

  • No genes correlated to 'AGE', and 'RACE'.

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
AGE Spearman correlation test   N=0        
GENDER Wilcoxon test N=3 male N=3 female N=0
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 49.31 (14)
  Significant markers N = 0
Clinical variable #2: 'GENDER'

3 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 40
  MALE 21
     
  Significant markers N = 3
  Higher in MALE 3
  Higher in FEMALE 0
List of 3 genes differentially expressed by 'GENDER'

Table S3.  Get Full Table List of 3 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
DKFZP434L187 767 1.443e-07 0.00286 0.9131
CHTF8 718 6.306e-06 0.125 0.8548
HAS3 718 6.306e-06 0.125 0.8548
Clinical variable #3: 'RACE'

No gene related to 'RACE'.

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

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 3
  BLACK OR AFRICAN AMERICAN 7
  WHITE 48
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PCPG-TP.meth.by_min_clin_corr.data.txt

  • Clinical data file = PCPG-TP.merged_data.txt

  • Number of patients = 61

  • Number of genes = 19848

  • Number of clinical features = 3

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] 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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