Correlation between miRseq expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 518 miRs and 5 clinical features across 10 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one miRs.

  • 2 miRs correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-196A-1 ,  HSA-MIR-196B

  • No miRs correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', and 'GENDER'.

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 Q value < 0.05.

Clinical feature Statistical test Significant miRs Associated with                 Associated with
Time to Death Cox regression test   N=0        
AGE Spearman correlation test   N=0        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=2 higher stage N=2 lower stage N=0
GENDER t test   N=0        
Clinical variable #1: 'Time to Death'

No miR related to 'Time to Death'.

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

Time to Death Duration (Months) 10.2-121.2 (median=26.3)
  censored N = 6
  death N = 4
     
  Significant markers N = 0
Clinical variable #2: 'AGE'

No miR related to 'AGE'.

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

AGE Mean (SD) 44.2 (16)
  Significant markers N = 0
Clinical variable #3: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S3.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 3
  STAGE II 2
  STAGE IV 4
     
  Significant markers N = 0
Clinical variable #4: 'PATHOLOGY.T.STAGE'

2 miRs related to 'PATHOLOGY.T.STAGE'.

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.33 (1.3)
  N
  1 3
  2 3
  4 3
     
  Significant markers N = 2
  pos. correlated 2
  neg. correlated 0
List of 2 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S5.  Get Full Table List of 2 miRs significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-196A-1 0.9487 9.585e-05 0.0481
HSA-MIR-196B 0.9487 9.585e-05 0.0481

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-196A-1 to 'PATHOLOGY.T.STAGE'. P value = 9.58e-05 with Spearman correlation analysis.

Clinical variable #5: 'GENDER'

No miR related to 'GENDER'.

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

GENDER Labels N
  FEMALE 5
  MALE 5
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = ACC-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 10

  • Number of miRs = 518

  • Number of clinical features = 5

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

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

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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[4] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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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