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 469 genes and 5 clinical features across 25 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

  • 1 gene correlated to 'LYMPH.NODE.METASTASIS'.

    • HSA-MIR-1537

  • No genes correlated to 'AGE', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.

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
AGE Spearman correlation test   N=0        
GENDER t test   N=0        
DISTANT METASTASIS ANOVA test   N=0        
LYMPH NODE METASTASIS ANOVA test N=1        
NEOPLASM DISEASESTAGE ANOVA test   N=0        
Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 52.32 (15)
  Significant markers N = 0
Clinical variable #2: 'GENDER'

No gene related to 'GENDER'.

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

GENDER Labels N
  FEMALE 11
  MALE 14
     
  Significant markers N = 0
Clinical variable #3: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S3.  Basic characteristics of clinical feature: 'DISTANT.METASTASIS'

DISTANT.METASTASIS Labels N
  M0 2
  M1 2
  MX 3
     
  Significant markers N = 0
Clinical variable #4: 'LYMPH.NODE.METASTASIS'

One gene related to 'LYMPH.NODE.METASTASIS'.

Table S4.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 13
  N1 2
  NX 10
     
  Significant markers N = 1
List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S5.  Get Full Table List of one gene differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
HSA-MIR-1537 0.0001046 0.049

Figure S1.  Get High-res Image As an example, this figure shows the association of HSA-MIR-1537 to 'LYMPH.NODE.METASTASIS'. P value = 0.000105 with ANOVA analysis.

Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S6.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 8
  STAGE II 12
  STAGE III 2
  STAGE IV 3
     
  Significant markers N = 0
Methods & Data
Input

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

  • Clinical data file = KICH-TP.clin.merged.picked.txt

  • Number of patients = 25

  • Number of genes = 469

  • Number of clinical features = 5

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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