Correlation between RPPA expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 189 genes and 5 clinical features across 137 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.

  • 3 genes correlated to 'PATHOLOGY.T.STAGE'.

    • EIF4G1|EIF4G-R-C ,  DVL3|DVL3-R-V ,  EEF2K|EEF2K-R-V

  • No genes correlated to 'AGE', 'PATHOLOGY.N.STAGE', 'COMPLETENESS.OF.RESECTION', and 'NUMBER.OF.LYMPH.NODES'.

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        
PATHOLOGY T STAGE Spearman correlation test N=3 higher stage N=3 lower stage N=0
PATHOLOGY N STAGE t test   N=0        
COMPLETENESS OF RESECTION ANOVA test   N=0        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

No gene related to 'AGE'.

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

AGE Mean (SD) 60.23 (7.1)
  Significant markers N = 0
Clinical variable #2: 'PATHOLOGY.T.STAGE'

3 genes related to 'PATHOLOGY.T.STAGE'.

Table S2.  Basic characteristics of clinical feature: 'PATHOLOGY.T.STAGE'

PATHOLOGY.T.STAGE Mean (SD) 2.65 (0.55)
  N
  2 53
  3 79
  4 5
     
  Significant markers N = 3
  pos. correlated 3
  neg. correlated 0
List of 3 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S3.  Get Full Table List of 3 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
EIF4G1|EIF4G-R-C 0.3464 3.38e-05 0.00639
DVL3|DVL3-R-V 0.3391 5.053e-05 0.0095
EEF2K|EEF2K-R-V 0.3255 0.0001041 0.0195

Figure S1.  Get High-res Image As an example, this figure shows the association of EIF4G1|EIF4G-R-C to 'PATHOLOGY.T.STAGE'. P value = 3.38e-05 with Spearman correlation analysis.

Clinical variable #3: 'PATHOLOGY.N.STAGE'

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

Table S4.  Basic characteristics of clinical feature: 'PATHOLOGY.N.STAGE'

PATHOLOGY.N.STAGE Labels N
  class0 109
  class1 13
     
  Significant markers N = 0
Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

No gene related to 'COMPLETENESS.OF.RESECTION'.

Table S5.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 100
  R1 27
  RX 3
     
  Significant markers N = 0
Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

No gene related to 'NUMBER.OF.LYMPH.NODES'.

Table S6.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 0.2 (0.76)
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PRAD-TP.rppa.txt

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

  • Number of patients = 137

  • Number of genes = 189

  • 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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