Correlation between miRseq expression and clinical features
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.

Summary

Testing the association between 524 miRs and 3 clinical features across 61 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, no clinical feature related to at least one miRs.

  • No miRs correlated to 'AGE', 'GENDER', 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 miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
AGE Spearman correlation test   N=0        
GENDER Wilcoxon test   N=0        
RACE Kruskal-Wallis test   N=0        
Clinical variable #1: 'AGE'

No miR 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'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 40
  MALE 21
     
  Significant markers N = 0
Clinical variable #3: 'RACE'

No miR related to 'RACE'.

Table S3.  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.miRseq_RPKM_log2.txt

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

  • Number of patients = 61

  • Number of miRs = 524

  • 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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