Correlation between mRNAseq expression and clinical features
Kidney Chromophobe (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between mRNAseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C18K7739
Overview
Introduction

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

Summary

Testing the association between 17843 genes and 5 clinical features across 25 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.

  • 4 genes correlated to 'GENDER'.

    • DDX3Y|8653 ,  XIST|7503 ,  RPS4Y1|6192 ,  ZFY|7544

  • 15 genes correlated to 'LYMPH.NODE.METASTASIS'.

    • WDR20|91833 ,  GRIP1|23426 ,  C14ORF159|80017 ,  GLRX5|51218 ,  C14ORF109|26175 ,  ...

  • No genes correlated to 'AGE', '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=4 male N=3 female N=1
DISTANT METASTASIS ANOVA test   N=0        
LYMPH NODE METASTASIS ANOVA test N=15        
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'

4 genes related to 'GENDER'.

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

GENDER Labels N
  FEMALE 11
  MALE 14
     
  Significant markers N = 4
  Higher in MALE 3
  Higher in FEMALE 1
List of 4 genes differentially expressed by 'GENDER'

Table S3.  Get Full Table List of 4 genes differentially expressed by 'GENDER'

T(pos if higher in 'MALE') ttestP Q AUC
DDX3Y|8653 17.12 3.268e-11 5.78e-07 1
XIST|7503 -13.36 3.034e-10 5.37e-06 1
RPS4Y1|6192 14.54 5.06e-09 8.95e-05 1
ZFY|7544 9.56 5.237e-08 0.000927 1

Figure S1.  Get High-res Image As an example, this figure shows the association of DDX3Y|8653 to 'GENDER'. P value = 3.27e-11 with T-test analysis.

Clinical variable #3: 'DISTANT.METASTASIS'

No gene related to 'DISTANT.METASTASIS'.

Table S4.  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'

15 genes related to 'LYMPH.NODE.METASTASIS'.

Table S5.  Basic characteristics of clinical feature: 'LYMPH.NODE.METASTASIS'

LYMPH.NODE.METASTASIS Labels N
  N0 13
  N1 2
  NX 10
     
  Significant markers N = 15
List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

Table S6.  Get Full Table List of top 10 genes differentially expressed by 'LYMPH.NODE.METASTASIS'

ANOVA_P Q
WDR20|91833 2.75e-10 4.91e-06
GRIP1|23426 3.23e-08 0.000576
C14ORF159|80017 5.028e-08 0.000897
GLRX5|51218 1.081e-07 0.00193
C14ORF109|26175 1.425e-07 0.00254
ASAM|79827 2.374e-07 0.00423
UBR7|55148 2.808e-07 0.00501
KIAA1543|57662 7.539e-07 0.0134
ZNF22|7570 7.944e-07 0.0142
TWIST2|117581 8.644e-07 0.0154

Figure S2.  Get High-res Image As an example, this figure shows the association of WDR20|91833 to 'LYMPH.NODE.METASTASIS'. P value = 2.75e-10 with ANOVA analysis.

Clinical variable #5: 'NEOPLASM.DISEASESTAGE'

No gene related to 'NEOPLASM.DISEASESTAGE'.

Table S7.  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.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 25

  • Number of genes = 17843

  • 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.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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)