Correlation between miRseq expression and clinical features
Testicular Germ Cell Tumors (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1TH8KMT
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 657 miRs and 4 clinical features across 13 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one miRs.

  • 1 miR correlated to 'PATHOLOGY.T.STAGE'.

    • HSA-MIR-3199-2

  • No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.N.STAGE'.

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        
NEOPLASM DISEASESTAGE Kruskal-Wallis test   N=0        
PATHOLOGY T STAGE Spearman correlation test N=1 higher stage N=0 lower stage N=1
PATHOLOGY N STAGE Wilcoxon test   N=0        
Clinical variable #1: 'AGE'

No miR related to 'AGE'.

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

AGE Mean (SD) 30.92 (7.4)
  Significant markers N = 0
Clinical variable #2: 'NEOPLASM.DISEASESTAGE'

No miR related to 'NEOPLASM.DISEASESTAGE'.

Table S2.  Basic characteristics of clinical feature: 'NEOPLASM.DISEASESTAGE'

NEOPLASM.DISEASESTAGE Labels N
  STAGE I 2
  STAGE IB 1
  STAGE IIA 1
  STAGE IIC 1
  STAGE IIIC 1
  STAGE IS 7
     
  Significant markers N = 0
Clinical variable #3: 'PATHOLOGY.T.STAGE'

One miR related to 'PATHOLOGY.T.STAGE'.

Table S3.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 1.69 (0.63)
  N
  1 5
  2 7
  3 1
     
  Significant markers N = 1
  pos. correlated 0
  neg. correlated 1
List of one miR differentially expressed by 'PATHOLOGY.T.STAGE'

Table S4.  Get Full Table List of one miR significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-3199-2 -0.8559 0.000382 0.25
Clinical variable #4: 'PATHOLOGY.N.STAGE'

No miR related to 'PATHOLOGY.N.STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 5
  class1 2
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = TGCT-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 13

  • Number of miRs = 657

  • Number of clinical features = 4

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[2] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[3] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[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)