Correlation between mRNAseq expression and clinical features
Overview
Introduction

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

Summary

Testing the association between 18239 genes and 5 clinical features across 161 samples, statistically thresholded by Q value < 0.05, 4 clinical features related to at least one genes.

  • 1 gene correlated to 'AGE'.

    • ADAP2|55803

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

    • KIAA0319L|79932 ,  GMDS|2762 ,  ABAT|18 ,  CHRNA2|1135 ,  EPHX2|2053 ,  ...

  • 4 genes correlated to 'PATHOLOGY.N.STAGE'.

    • CRHR2|1395 ,  NKX6-2|84504 ,  SEC24A|10802 ,  PRTN3|5657

  • 3 genes correlated to 'COMPLETENESS.OF.RESECTION'.

    • GLP2R|9340 ,  MRPS6|64968 ,  CNGB3|54714

  • No genes correlated to '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=1 older N=1 younger N=0
PATHOLOGY T STAGE Spearman correlation test N=9 higher stage N=2 lower stage N=7
PATHOLOGY N STAGE t test N=4 class1 N=1 class0 N=3
COMPLETENESS OF RESECTION ANOVA test N=3        
NUMBER OF LYMPH NODES Spearman correlation test   N=0        
Clinical variable #1: 'AGE'

One gene related to 'AGE'.

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

AGE Mean (SD) 60.34 (6.9)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one gene significantly correlated to 'AGE' by Spearman correlation test

Table S2.  Get Full Table List of one gene significantly correlated to 'AGE' by Spearman correlation test

SpearmanCorr corrP Q
ADAP2|55803 0.3633 2.343e-06 0.0427

Figure S1.  Get High-res Image As an example, this figure shows the association of ADAP2|55803 to 'AGE'. P value = 2.34e-06 with Spearman correlation analysis. The straight line presents the best linear regression.

Clinical variable #2: 'PATHOLOGY.T.STAGE'

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

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

PATHOLOGY.T.STAGE Mean (SD) 2.6 (0.55)
  N
  2 69
  3 86
  4 5
     
  Significant markers N = 9
  pos. correlated 2
  neg. correlated 7
List of 9 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

Table S4.  Get Full Table List of 9 genes significantly correlated to 'PATHOLOGY.T.STAGE' by Spearman correlation test

SpearmanCorr corrP Q
KIAA0319L|79932 -0.3928 2.787e-07 0.00508
GMDS|2762 -0.3908 3.224e-07 0.00588
ABAT|18 -0.3878 4.042e-07 0.00737
CHRNA2|1135 -0.3861 4.612e-07 0.00841
EPHX2|2053 -0.3835 5.55e-07 0.0101
TSPAN1|10103 -0.3718 1.291e-06 0.0235
ELAVL4|1996 0.3912 1.619e-06 0.0295
KCNK6|9424 -0.3648 2.104e-06 0.0384
FAM171B|165215 0.3629 2.403e-06 0.0438

Figure S2.  Get High-res Image As an example, this figure shows the association of KIAA0319L|79932 to 'PATHOLOGY.T.STAGE'. P value = 2.79e-07 with Spearman correlation analysis.

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

4 genes related to 'PATHOLOGY.N.STAGE'.

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

PATHOLOGY.N.STAGE Labels N
  class0 127
  class1 15
     
  Significant markers N = 4
  Higher in class1 1
  Higher in class0 3
List of 4 genes differentially expressed by 'PATHOLOGY.N.STAGE'

Table S6.  Get Full Table List of 4 genes differentially expressed by 'PATHOLOGY.N.STAGE'

T(pos if higher in 'class1') ttestP Q AUC
CRHR2|1395 -8.74 8.893e-08 0.00162 0.9421
NKX6-2|84504 -6.07 3.342e-07 0.00609 0.8651
SEC24A|10802 5.72 1.949e-06 0.0355 0.7717
PRTN3|5657 -6.32 1.989e-06 0.0362 0.783

Figure S3.  Get High-res Image As an example, this figure shows the association of CRHR2|1395 to 'PATHOLOGY.N.STAGE'. P value = 8.89e-08 with T-test analysis.

Clinical variable #4: 'COMPLETENESS.OF.RESECTION'

3 genes related to 'COMPLETENESS.OF.RESECTION'.

Table S7.  Basic characteristics of clinical feature: 'COMPLETENESS.OF.RESECTION'

COMPLETENESS.OF.RESECTION Labels N
  R0 120
  R1 31
  RX 3
     
  Significant markers N = 3
List of 3 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

Table S8.  Get Full Table List of 3 genes differentially expressed by 'COMPLETENESS.OF.RESECTION'

ANOVA_P Q
GLP2R|9340 6.285e-10 1.15e-05
MRPS6|64968 9.64e-07 0.0176
CNGB3|54714 1.7e-06 0.031

Figure S4.  Get High-res Image As an example, this figure shows the association of GLP2R|9340 to 'COMPLETENESS.OF.RESECTION'. P value = 6.28e-10 with ANOVA analysis.

Clinical variable #5: 'NUMBER.OF.LYMPH.NODES'

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

Table S9.  Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'

NUMBER.OF.LYMPH.NODES Mean (SD) 0.2 (0.73)
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = PRAD-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt

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

  • Number of patients = 161

  • Number of genes = 18239

  • 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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