Correlation between miRseq expression and clinical features
Lung Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between miRseq expression and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1J38S0M
Overview
Introduction

This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features. The input file " LUAD-TP.miRseq_RPKM_log2.txt " is generated in the pipeline miRseq_Preprocess in the stddata run.

Summary

Testing the association between 531 miRs and 15 clinical features across 513 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 12 clinical features related to at least one miRs.

  • 8 miRs correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • HSA-MIR-1245 ,  HSA-MIR-548V ,  HSA-MIR-1910 ,  HSA-MIR-101-1 ,  HSA-MIR-29C ,  ...

  • 30 miRs correlated to 'YEARS_TO_BIRTH'.

    • HSA-MIR-9-2 ,  HSA-MIR-9-1 ,  HSA-MIR-130B ,  HSA-MIR-590 ,  HSA-MIR-629 ,  ...

  • 30 miRs correlated to 'PATHOLOGIC_STAGE'.

    • HSA-MIR-30C-2 ,  HSA-MIR-101-2 ,  HSA-MIR-150 ,  HSA-MIR-664 ,  HSA-MIR-451 ,  ...

  • 30 miRs correlated to 'PATHOLOGY_T_STAGE'.

    • HSA-MIR-30C-2 ,  HSA-MIR-451 ,  HSA-MIR-101-2 ,  HSA-MIR-150 ,  HSA-MIR-146A ,  ...

  • 30 miRs correlated to 'PATHOLOGY_N_STAGE'.

    • HSA-MIR-200A ,  HSA-MIR-200B ,  HSA-MIR-660 ,  HSA-MIR-548B ,  HSA-MIR-181C ,  ...

  • 30 miRs correlated to 'GENDER'.

    • HSA-MIR-105-2 ,  HSA-MIR-361 ,  HSA-MIR-30E ,  HSA-MIR-105-1 ,  HSA-MIR-99A ,  ...

  • 1 miR correlated to 'KARNOFSKY_PERFORMANCE_SCORE'.

    • HSA-MIR-660

  • 30 miRs correlated to 'HISTOLOGICAL_TYPE'.

    • HSA-MIR-18A ,  HSA-MIR-656 ,  HSA-MIR-130B ,  HSA-MIR-338 ,  HSA-MIR-200A ,  ...

  • 30 miRs correlated to 'NUMBER_PACK_YEARS_SMOKED'.

    • HSA-MIR-339 ,  HSA-MIR-345 ,  HSA-MIR-210 ,  HSA-MIR-590 ,  HSA-MIR-296 ,  ...

  • 28 miRs correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

    • HSA-MIR-629 ,  HSA-MIR-508 ,  HSA-MIR-550A-1 ,  HSA-MIR-514-1 ,  HSA-MIR-128-1 ,  ...

  • 17 miRs correlated to 'RESIDUAL_TUMOR'.

    • HSA-MIR-34A ,  HSA-MIR-34B ,  HSA-MIR-181B-2 ,  HSA-MIR-664 ,  HSA-MIR-151 ,  ...

  • 30 miRs correlated to 'RACE'.

    • HSA-MIR-1304 ,  HSA-MIR-548J ,  HSA-MIR-3680 ,  HSA-MIR-339 ,  HSA-MIR-186 ,  ...

  • No miRs correlated to 'PATHOLOGY_M_STAGE', 'RADIATION_THERAPY', and 'ETHNICITY'.

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
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=8   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=7 younger N=23
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=4 lower stage N=26
PATHOLOGY_N_STAGE Spearman correlation test N=30 higher stage N=13 lower stage N=17
PATHOLOGY_M_STAGE Wilcoxon test   N=0        
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
KARNOFSKY_PERFORMANCE_SCORE Spearman correlation test N=1 higher score N=1 lower score N=0
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
NUMBER_PACK_YEARS_SMOKED Spearman correlation test N=30 higher number_pack_years_smoked N=30 lower number_pack_years_smoked N=0
YEAR_OF_TOBACCO_SMOKING_ONSET Spearman correlation test N=28 higher year_of_tobacco_smoking_onset N=13 lower year_of_tobacco_smoking_onset N=15
RESIDUAL_TUMOR Kruskal-Wallis test N=17        
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

8 miRs related to 'DAYS_TO_DEATH_OR_LAST_FUP'.

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

DAYS_TO_DEATH_OR_LAST_FUP Duration (Months) 0-238.3 (median=21.6)
  censored N = 326
  death N = 186
     
  Significant markers N = 8
  associated with shorter survival NA
  associated with longer survival NA
List of 8 miRs differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of 8 miRs significantly associated with 'Time to Death' by Cox regression test. For the survival curves, it compared quantile intervals at c(0, 0.25, 0.50, 0.75, 1) and did not try survival analysis if there is only one interval.

logrank_P Q C_index
HSA-MIR-1245 0.000392 0.13 0.563
HSA-MIR-548V 5e-04 0.13 0.431
HSA-MIR-1910 0.00104 0.15 0.618
HSA-MIR-101-1 0.0011 0.15 0.408
HSA-MIR-29C 0.00208 0.22 0.414
HSA-MIR-148A 0.0032 0.26 0.43
HSA-MIR-99A 0.00339 0.26 0.407
HSA-MIR-3653 0.0041 0.27 0.429
Clinical variable #2: 'YEARS_TO_BIRTH'

30 miRs related to 'YEARS_TO_BIRTH'.

Table S3.  Basic characteristics of clinical feature: 'YEARS_TO_BIRTH'

YEARS_TO_BIRTH Mean (SD) 65.29 (9.9)
  Significant markers N = 30
  pos. correlated 7
  neg. correlated 23
List of top 10 miRs differentially expressed by 'YEARS_TO_BIRTH'

Table S4.  Get Full Table List of top 10 miRs significantly correlated to 'YEARS_TO_BIRTH' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-9-2 -0.1808 6.451e-05 0.015
HSA-MIR-9-1 -0.1782 8.261e-05 0.015
HSA-MIR-130B -0.1779 8.449e-05 0.015
HSA-MIR-590 -0.1709 0.0001609 0.0179
HSA-MIR-629 -0.1703 0.000169 0.0179
HSA-MIR-424 -0.1597 0.0004246 0.0343
HSA-MIR-18A -0.1569 0.000537 0.0343
HSA-MIR-30A 0.1565 0.0005549 0.0343
HSA-MIR-96 -0.156 0.0005812 0.0343
HSA-MIR-345 -0.1515 0.0008379 0.0394
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 miRs related to 'PATHOLOGIC_STAGE'.

Table S5.  Basic characteristics of clinical feature: 'PATHOLOGIC_STAGE'

PATHOLOGIC_STAGE Labels N
  STAGE I 5
  STAGE IA 133
  STAGE IB 139
  STAGE II 1
  STAGE IIA 50
  STAGE IIB 70
  STAGE IIIA 73
  STAGE IIIB 11
  STAGE IV 24
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

Table S6.  Get Full Table List of top 10 miRs differentially expressed by 'PATHOLOGIC_STAGE'

kruskal_wallis_P Q
HSA-MIR-30C-2 1.874e-05 0.00514
HSA-MIR-101-2 1.936e-05 0.00514
HSA-MIR-150 4.133e-05 0.00732
HSA-MIR-664 0.0001297 0.0151
HSA-MIR-451 0.0001523 0.0151
HSA-MIR-660 0.0001705 0.0151
HSA-MIR-146A 0.0002144 0.0163
HSA-MIR-548B 0.0004759 0.0316
HSA-MIR-133A-1 0.0008588 0.0489
HSA-MIR-342 0.001005 0.0489
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 miRs related to 'PATHOLOGY_T_STAGE'.

Table S7.  Basic characteristics of clinical feature: 'PATHOLOGY_T_STAGE'

PATHOLOGY_T_STAGE Mean (SD) 1.83 (0.74)
  N
  T1 170
  T2 274
  T3 47
  T4 19
     
  Significant markers N = 30
  pos. correlated 4
  neg. correlated 26
List of top 10 miRs differentially expressed by 'PATHOLOGY_T_STAGE'

Table S8.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_T_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-30C-2 -0.2313 1.269e-07 3.63e-05
HSA-MIR-451 -0.2301 1.492e-07 3.63e-05
HSA-MIR-101-2 -0.2276 2.052e-07 3.63e-05
HSA-MIR-150 -0.2113 1.477e-06 0.000196
HSA-MIR-146A -0.2087 1.999e-06 0.000212
HSA-MIR-374B -0.2053 2.939e-06 0.00026
HSA-MIR-342 -0.2013 4.616e-06 0.00035
HSA-MIR-30B -0.1972 7.234e-06 0.000413
HSA-MIR-486 -0.1972 7.247e-06 0.000413
HSA-MIR-218-2 -0.1965 7.771e-06 0.000413
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 miRs related to 'PATHOLOGY_N_STAGE'.

Table S9.  Basic characteristics of clinical feature: 'PATHOLOGY_N_STAGE'

PATHOLOGY_N_STAGE Mean (SD) 0.5 (0.76)
  N
  N0 330
  N1 95
  N2 74
  N3 2
     
  Significant markers N = 30
  pos. correlated 13
  neg. correlated 17
List of top 10 miRs differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 miRs significantly correlated to 'PATHOLOGY_N_STAGE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-200A -0.1809 4.67e-05 0.00977
HSA-MIR-200B -0.1797 5.226e-05 0.00977
HSA-MIR-660 -0.1791 5.522e-05 0.00977
HSA-MIR-548B -0.178 0.0001059 0.0141
HSA-MIR-181C -0.1635 0.0002371 0.0252
HSA-MIR-429 -0.1551 0.0004959 0.041
HSA-MIR-29C -0.1532 0.0005795 0.041
HSA-MIR-944 0.158 0.0006184 0.041
HSA-MIR-30B -0.1487 0.0008414 0.0496
HSA-MIR-497 -0.1457 0.001075 0.0571
Clinical variable #6: 'PATHOLOGY_M_STAGE'

No miR related to 'PATHOLOGY_M_STAGE'.

Table S11.  Basic characteristics of clinical feature: 'PATHOLOGY_M_STAGE'

PATHOLOGY_M_STAGE Labels N
  class0 346
  class1 23
     
  Significant markers N = 0
Clinical variable #7: 'GENDER'

30 miRs related to 'GENDER'.

Table S12.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 274
  MALE 239
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 miRs differentially expressed by 'GENDER'

Table S13.  Get Full Table List of top 10 miRs differentially expressed by 'GENDER'. 0 significant gene(s) located in sex chromosomes is(are) filtered out.

W(pos if higher in 'MALE') wilcoxontestP Q AUC
HSA-MIR-105-2 16263 1.467e-06 0.000779 0.6574
HSA-MIR-361 25884 4.22e-05 0.00855 0.6047
HSA-MIR-30E 25983 5.437e-05 0.00855 0.6032
HSA-MIR-105-1 16931 7.951e-05 0.00855 0.6258
HSA-MIR-99A 26139 8.052e-05 0.00855 0.6008
HSA-MIR-651 25340 0.0001219 0.0108 0.5991
HSA-MIR-1298 4577 0.0002748 0.0208 0.6646
HSA-MIR-1293 13552 0.0003859 0.0244 0.6194
HSA-MIR-1911 11395 0.0004142 0.0244 0.6247
HSA-MIR-3934 26774 0.0006015 0.0319 0.5879
Clinical variable #8: 'RADIATION_THERAPY'

No miR related to 'RADIATION_THERAPY'.

Table S14.  Basic characteristics of clinical feature: 'RADIATION_THERAPY'

RADIATION_THERAPY Labels N
  NO 407
  YES 60
     
  Significant markers N = 0
Clinical variable #9: 'KARNOFSKY_PERFORMANCE_SCORE'

One miR related to 'KARNOFSKY_PERFORMANCE_SCORE'.

Table S15.  Basic characteristics of clinical feature: 'KARNOFSKY_PERFORMANCE_SCORE'

KARNOFSKY_PERFORMANCE_SCORE Mean (SD) 78.5 (29)
  Significant markers N = 1
  pos. correlated 1
  neg. correlated 0
List of one miR differentially expressed by 'KARNOFSKY_PERFORMANCE_SCORE'

Table S16.  Get Full Table List of one miR significantly correlated to 'KARNOFSKY_PERFORMANCE_SCORE' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-660 0.3118 0.0002582 0.137
Clinical variable #10: 'HISTOLOGICAL_TYPE'

30 miRs related to 'HISTOLOGICAL_TYPE'.

Table S17.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  LUNG ACINAR ADENOCARCINOMA 18
  LUNG ADENOCARCINOMA MIXED SUBTYPE 106
  LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) 320
  LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS 5
  LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS 19
  LUNG CLEAR CELL ADENOCARCINOMA 2
  LUNG MICROPAPILLARY ADENOCARCINOMA 2
  LUNG MUCINOUS ADENOCARCINOMA 2
  LUNG PAPILLARY ADENOCARCINOMA 23
  LUNG SIGNET RING ADENOCARCINOMA 1
  LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA 5
  MUCINOUS (COLLOID) CARCINOMA 10
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

Table S18.  Get Full Table List of top 10 miRs differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
HSA-MIR-18A 6.534e-06 0.0024
HSA-MIR-656 1.199e-05 0.0024
HSA-MIR-130B 1.358e-05 0.0024
HSA-MIR-338 3.399e-05 0.00451
HSA-MIR-200A 4.432e-05 0.00464
HSA-MIR-200B 5.237e-05 0.00464
HSA-MIR-92A-1 6.744e-05 0.00512
HSA-MIR-197 9.618e-05 0.00638
HSA-LET-7B 0.0001414 0.00834
HSA-MIR-1976 0.0001977 0.0105
Clinical variable #11: 'NUMBER_PACK_YEARS_SMOKED'

30 miRs related to 'NUMBER_PACK_YEARS_SMOKED'.

Table S19.  Basic characteristics of clinical feature: 'NUMBER_PACK_YEARS_SMOKED'

NUMBER_PACK_YEARS_SMOKED Mean (SD) 42 (27)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 miRs differentially expressed by 'NUMBER_PACK_YEARS_SMOKED'

Table S20.  Get Full Table List of top 10 miRs significantly correlated to 'NUMBER_PACK_YEARS_SMOKED' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-339 0.2375 7.095e-06 0.00292
HSA-MIR-345 0.2326 1.098e-05 0.00292
HSA-MIR-210 0.2226 2.636e-05 0.00467
HSA-MIR-590 0.2157 4.718e-05 0.00558
HSA-MIR-296 0.2147 5.256e-05 0.00558
HSA-MIR-31 0.2077 0.0001285 0.0114
HSA-MIR-136 0.1894 0.0003676 0.0218
HSA-MIR-16-2 0.1882 0.0003997 0.0218
HSA-MIR-301A 0.1881 0.0004039 0.0218
HSA-MIR-1295 0.2125 0.0004162 0.0218
Clinical variable #12: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

28 miRs related to 'YEAR_OF_TOBACCO_SMOKING_ONSET'.

Table S21.  Basic characteristics of clinical feature: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

YEAR_OF_TOBACCO_SMOKING_ONSET Mean (SD) 1964.77 (12)
  Significant markers N = 28
  pos. correlated 13
  neg. correlated 15
List of top 10 miRs differentially expressed by 'YEAR_OF_TOBACCO_SMOKING_ONSET'

Table S22.  Get Full Table List of top 10 miRs significantly correlated to 'YEAR_OF_TOBACCO_SMOKING_ONSET' by Spearman correlation test

SpearmanCorr corrP Q
HSA-MIR-629 0.2158 0.0003208 0.135
HSA-MIR-508 -0.2043 0.0006997 0.135
HSA-MIR-550A-1 0.2022 0.0007624 0.135
HSA-MIR-514-1 -0.1928 0.002488 0.218
HSA-MIR-128-1 0.1793 0.002901 0.218
HSA-MIR-128-2 0.1759 0.003492 0.218
HSA-MIR-514-3 -0.1881 0.003722 0.218
HSA-MIR-421 0.1709 0.004702 0.218
HSA-MIR-3676 0.1698 0.005142 0.218
HSA-MIR-589 0.1664 0.005755 0.218
Clinical variable #13: 'RESIDUAL_TUMOR'

17 miRs related to 'RESIDUAL_TUMOR'.

Table S23.  Basic characteristics of clinical feature: 'RESIDUAL_TUMOR'

RESIDUAL_TUMOR Labels N
  R0 341
  R1 13
  R2 4
  RX 26
     
  Significant markers N = 17
List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

Table S24.  Get Full Table List of top 10 miRs differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
HSA-MIR-34A 7.202e-05 0.0382
HSA-MIR-34B 0.0001573 0.0418
HSA-MIR-181B-2 0.0003391 0.0488
HSA-MIR-664 0.0004166 0.0488
HSA-MIR-151 0.0004591 0.0488
HSA-MIR-30C-2 0.0007214 0.0638
HSA-MIR-642A 0.001834 0.123
HSA-MIR-34C 0.001852 0.123
HSA-MIR-30B 0.003109 0.183
HSA-MIR-145 0.00344 0.183
Clinical variable #14: 'RACE'

30 miRs related to 'RACE'.

Table S25.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  ASIAN 8
  BLACK OR AFRICAN AMERICAN 52
  WHITE 387
     
  Significant markers N = 30
List of top 10 miRs differentially expressed by 'RACE'

Table S26.  Get Full Table List of top 10 miRs differentially expressed by 'RACE'

kruskal_wallis_P Q
HSA-MIR-1304 3.106e-06 0.00165
HSA-MIR-548J 0.0004543 0.0858
HSA-MIR-3680 0.0004846 0.0858
HSA-MIR-339 0.001909 0.162
HSA-MIR-186 0.00191 0.162
HSA-MIR-3667 0.002088 0.162
HSA-MIR-589 0.002132 0.162
HSA-MIR-3614 0.002655 0.176
HSA-MIR-190 0.00332 0.196
HSA-MIR-34A 0.003863 0.199
Clinical variable #15: 'ETHNICITY'

No miR related to 'ETHNICITY'.

Table S27.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 7
  NOT HISPANIC OR LATINO 381
     
  Significant markers N = 0
Methods & Data
Input
  • Expresson data file = LUAD-TP.miRseq_RPKM_log2.txt

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

  • Number of patients = 513

  • Number of miRs = 531

  • Number of clinical features = 15

Selected clinical features
  • Further details on clinical features selected for this analysis, please find a documentation on selected CDEs (Clinical Data Elements). The first column of the file is a formula to convert values and the second column is a clinical parameter name.

  • Survival time data

    • Survival time data is a combined value of days_to_death and days_to_last_followup. For each patient, it creates a combined value 'days_to_death_or_last_fup' using conversion process below.

      • if 'vital_status'==1(dead), 'days_to_last_followup' is always NA. Thus, uses 'days_to_death' value for 'days_to_death_or_fup'

      • if 'vital_status'==0(alive),

        • if 'days_to_death'==NA & 'days_to_last_followup'!=NA, uses 'days_to_last_followup' value for 'days_to_death_or_fup'

        • if 'days_to_death'!=NA, excludes this case in survival analysis and report the case.

      • if 'vital_status'==NA,excludes this case in survival analysis and report the case.

    • cf. In certain diesase types such as SKCM, days_to_death parameter is replaced with time_from_specimen_dx or time_from_specimen_procurement_to_death .

  • This analysis excluded clinical variables that has only NA values.

Survival analysis

For survival clinical features, logrank test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values comparing quantile intervals using the 'coxph' function in R. Kaplan-Meier survival curves were plotted using quantile intervals at c(0, 0.25, 0.50, 0.75, 1). If there is only one interval group, it will not try survival analysis.

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

Wilcoxon rank sum test (Mann-Whitney U test)

For two groups (mutant or wild-type) of continuous type of clinical data, wilcoxon rank sum test (Mann and Whitney, 1947) was applied to compare their mean difference using 'wilcox.test(continuous.clinical ~ as.factor(group), exact=FALSE)' function in R. This test is equivalent to the Mann-Whitney test.

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] Andersen and Gill, Cox's regression model for counting processes, a large sample study, Annals of Statistics 10(4):1100-1120 (1982)
[2] Spearman, C, The proof and measurement of association between two things, Amer. J. Psychol 15:72-101 (1904)
[3] Mann and Whitney, On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other, Annals of Mathematical Statistics 18 (1), 50-60 (1947)
[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)