Correlation between gene methylation status and clinical features
Thyroid Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1DN44JP
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between gene methylation status and clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DN44JP
Overview
Introduction

This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features. The input file "THCA-TP.meth.by_min_clin_corr.data.txt" is generated in the pipeline Methylation_Preprocess in stddata run.

Summary

Testing the association between 16830 genes and 17 clinical features across 503 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 14 clinical features related to at least one genes.

  • 30 genes correlated to 'DAYS_TO_DEATH_OR_LAST_FUP'.

    • OTUD7A ,  HMGN1 ,  CCDC113 ,  RAD54L ,  RAB6C ,  ...

  • 30 genes correlated to 'YEARS_TO_BIRTH'.

    • MGA ,  INA ,  NHLRC1 ,  RANBP17 ,  SYNGR3 ,  ...

  • 30 genes correlated to 'PATHOLOGIC_STAGE'.

    • MGA ,  INA ,  TBKBP1 ,  RANBP17 ,  TMEM204 ,  ...

  • 30 genes correlated to 'PATHOLOGY_T_STAGE'.

    • TBKBP1 ,  TMEM204 ,  GJD3 ,  PDGFB ,  FLJ42875 ,  ...

  • 30 genes correlated to 'PATHOLOGY_N_STAGE'.

    • ST6GALNAC5 ,  DAGLA ,  BMP1 ,  MET ,  SLC34A2 ,  ...

  • 30 genes correlated to 'PATHOLOGY_M_STAGE'.

    • RBM18 ,  ZNF687 ,  SLC5A5 ,  CEBPG ,  FGF20 ,  ...

  • 30 genes correlated to 'GENDER'.

    • UTP14C ,  ETF1 ,  KIF4B ,  FAM35A ,  MTFR1 ,  ...

  • 30 genes correlated to 'HISTOLOGICAL_TYPE'.

    • LEPROT ,  PON2 ,  GPR115 ,  XDH ,  SLC34A2 ,  ...

  • 30 genes correlated to 'EXTRATHYROIDAL_EXTENSION'.

    • NRCAM ,  TBKBP1 ,  PDGFB ,  SLC34A2 ,  TMEM204 ,  ...

  • 30 genes correlated to 'RESIDUAL_TUMOR'.

    • SLC25A37 ,  NECAB2 ,  EGLN2 ,  KCNK3 ,  B3GNT5 ,  ...

  • 30 genes correlated to 'NUMBER_OF_LYMPH_NODES'.

    • MET ,  TMEM173 ,  FUT2 ,  DAGLA ,  MACF1 ,  ...

  • 1 gene correlated to 'MULTIFOCALITY'.

    • SORBS1

  • 30 genes correlated to 'TUMOR_SIZE'.

    • TGFBR1 ,  NRBP1 ,  ALDOC ,  KCNQ4 ,  UNKL ,  ...

  • 30 genes correlated to 'RACE'.

    • SCAMP5 ,  CDKN2AIP ,  RAD21L1 ,  EGOT ,  CLTC ,  ...

  • No genes correlated to 'RADIATION_THERAPY', 'RADIATION_EXPOSURE', 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 genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.

Clinical feature Statistical test Significant genes Associated with                 Associated with
DAYS_TO_DEATH_OR_LAST_FUP Cox regression test N=30   N=NA   N=NA
YEARS_TO_BIRTH Spearman correlation test N=30 older N=30 younger N=0
PATHOLOGIC_STAGE Kruskal-Wallis test N=30        
PATHOLOGY_T_STAGE Spearman correlation test N=30 higher stage N=19 lower stage N=11
PATHOLOGY_N_STAGE Wilcoxon test N=30 n1 N=30 n0 N=0
PATHOLOGY_M_STAGE Wilcoxon test N=30 class1 N=30 class0 N=0
GENDER Wilcoxon test N=30 male N=30 female N=0
RADIATION_THERAPY Wilcoxon test   N=0        
HISTOLOGICAL_TYPE Kruskal-Wallis test N=30        
RADIATION_EXPOSURE Wilcoxon test   N=0        
EXTRATHYROIDAL_EXTENSION Kruskal-Wallis test N=30        
RESIDUAL_TUMOR Kruskal-Wallis test N=30        
NUMBER_OF_LYMPH_NODES Spearman correlation test N=30 higher number_of_lymph_nodes N=0 lower number_of_lymph_nodes N=30
MULTIFOCALITY Wilcoxon test N=1 unifocal N=1 multifocal N=0
TUMOR_SIZE Spearman correlation test N=30 higher tumor_size N=22 lower tumor_size N=8
RACE Kruskal-Wallis test N=30        
ETHNICITY Wilcoxon test   N=0        
Clinical variable #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

30 genes 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) 0.2-178.3 (median=31.1)
  censored N = 486
  death N = 16
     
  Significant markers N = 30
  associated with shorter survival NA
  associated with longer survival NA
List of top 10 genes differentially expressed by 'DAYS_TO_DEATH_OR_LAST_FUP'

Table S2.  Get Full Table List of top 10 genes 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
OTUD7A 1.48e-07 0.0025 0.823
HMGN1 3.34e-06 0.025 0.793
CCDC113 5.2e-06 0.025 0.729
RAD54L 6.02e-06 0.025 0.663
RAB6C 1.17e-05 0.036 0.735
ZIK1 1.51e-05 0.036 0.8
GNB5 1.63e-05 0.036 0.745
APOE 1.99e-05 0.036 0.829
IRX1 2.18e-05 0.036 0.744
DPF1 2.73e-05 0.036 0.731
Clinical variable #2: 'YEARS_TO_BIRTH'

30 genes related to 'YEARS_TO_BIRTH'.

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

YEARS_TO_BIRTH Mean (SD) 47.26 (16)
  Significant markers N = 30
  pos. correlated 30
  neg. correlated 0
List of top 10 genes differentially expressed by 'YEARS_TO_BIRTH'

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

SpearmanCorr corrP Q
MGA 0.5312 5.756e-38 9.69e-34
INA 0.4911 6.832e-32 5.75e-28
NHLRC1 0.4814 1.556e-30 8.73e-27
RANBP17 0.4703 4.798e-29 2.02e-25
SYNGR3 0.4693 6.521e-29 2.19e-25
C1ORF59 0.4658 1.882e-28 5.28e-25
OTUD7A 0.452 1.084e-26 2.61e-23
ACN9 0.4502 1.776e-26 3.74e-23
ZNF518B 0.4405 2.761e-25 5.16e-22
NTNG2 0.4294 5.455e-24 9.18e-21
Clinical variable #3: 'PATHOLOGIC_STAGE'

30 genes related to 'PATHOLOGIC_STAGE'.

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

PATHOLOGIC_STAGE Labels N
  STAGE I 284
  STAGE II 51
  STAGE III 111
  STAGE IV 2
  STAGE IVA 47
  STAGE IVC 6
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'PATHOLOGIC_STAGE'

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

kruskal_wallis_P Q
MGA 1.118e-14 1.88e-10
INA 5.913e-13 4.98e-09
TBKBP1 1.057e-12 5.93e-09
RANBP17 2.529e-12 1.06e-08
TMEM204 2.299e-11 7.74e-08
ZNF518B 6.096e-11 1.71e-07
LOC728392 1.404e-10 3.37e-07
RAB4A 3.939e-10 8.16e-07
OTUD7A 4.962e-10 8.16e-07
SPRY4 5.307e-10 8.16e-07
Clinical variable #4: 'PATHOLOGY_T_STAGE'

30 genes related to 'PATHOLOGY_T_STAGE'.

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

PATHOLOGY_T_STAGE Mean (SD) 2.14 (0.89)
  N
  T1 143
  T2 166
  T3 169
  T4 23
     
  Significant markers N = 30
  pos. correlated 19
  neg. correlated 11
List of top 10 genes differentially expressed by 'PATHOLOGY_T_STAGE'

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

SpearmanCorr corrP Q
TBKBP1 0.3124 8.436e-13 1.42e-08
TMEM204 0.2963 1.299e-11 8.16e-08
GJD3 0.2956 1.455e-11 8.16e-08
PDGFB 0.286 6.904e-11 2.91e-07
FLJ42875 0.2725 5.613e-10 1.62e-06
GGT7 0.2722 5.805e-10 1.62e-06
SCN4B 0.2712 6.755e-10 1.62e-06
PSD3 -0.2693 8.94e-10 1.88e-06
TNFRSF11B 0.265 1.698e-09 2.96e-06
PHLDA3 -0.2647 1.759e-09 2.96e-06
Clinical variable #5: 'PATHOLOGY_N_STAGE'

30 genes related to 'PATHOLOGY_N_STAGE'.

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

PATHOLOGY_N_STAGE Labels N
  N0 227
  N1 226
     
  Significant markers N = 30
  Higher in N1 30
  Higher in N0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

Table S10.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_N_STAGE'

W(pos if higher in 'N1') wilcoxontestP Q AUC
ST6GALNAC5 13267 6.182e-19 4.49e-15 0.7414
DAGLA 13289 7.125e-19 4.49e-15 0.741
BMP1 13307 8.001e-19 4.49e-15 0.7406
MET 13358 1.11e-18 4.67e-15 0.7396
SLC34A2 13466 2.213e-18 7.45e-15 0.7375
RBM12 13495 2.66e-18 7.46e-15 0.7369
PON2 13588 4.788e-18 1.15e-14 0.7351
CAPN2 13721 1.101e-17 2.27e-14 0.7325
MACF1 13737 1.217e-17 2.27e-14 0.7322
DUSP6 13787 1.659e-17 2.79e-14 0.7313
Clinical variable #6: 'PATHOLOGY_M_STAGE'

30 genes related to 'PATHOLOGY_M_STAGE'.

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

PATHOLOGY_M_STAGE Labels N
  class0 280
  class1 9
     
  Significant markers N = 30
  Higher in class1 30
  Higher in class0 0
List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

Table S12.  Get Full Table List of top 10 genes differentially expressed by 'PATHOLOGY_M_STAGE'

W(pos if higher in 'class1') wilcoxontestP Q AUC
RBM18 2245 6.624e-05 0.264 0.8909
ZNF687 313 0.0001254 0.264 0.8758
SLC5A5 2206 0.0001274 0.264 0.8754
CEBPG 2201 0.0001384 0.264 0.8734
FGF20 327 0.0001577 0.264 0.8702
PFKFB4 343 0.0002041 0.264 0.8639
GIPC2 2174 0.0002142 0.264 0.8627
CA14 367 0.0002985 0.264 0.8544
KRTCAP3 367 0.0002985 0.264 0.8544
MCOLN2 2152 0.0003032 0.264 0.854
Clinical variable #7: 'GENDER'

30 genes related to 'GENDER'.

Table S13.  Basic characteristics of clinical feature: 'GENDER'

GENDER Labels N
  FEMALE 368
  MALE 135
     
  Significant markers N = 30
  Higher in MALE 30
  Higher in FEMALE 0
List of top 10 genes differentially expressed by 'GENDER'

Table S14.  Get Full Table List of top 10 genes 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
UTP14C 49461 3.847e-65 6.48e-61 0.9956
ETF1 48980 1.083e-62 9.11e-59 0.9859
KIF4B 5105 1.715e-42 9.62e-39 0.8972
FAM35A 7901 9.35e-32 3.93e-28 0.841
MTFR1 41469 1.152e-30 3.88e-27 0.8347
WBP11P1 39987 1.005e-25 2.82e-22 0.8049
ANKRD20A4 36689 2.354e-16 5.66e-13 0.7385
FRG1B 13284 1.247e-15 2.62e-12 0.7326
DACH1 13527 4.822e-15 9.02e-12 0.7277
CCDC121 35513 1.486e-13 2.5e-10 0.7148
Clinical variable #8: 'RADIATION_THERAPY'

No gene related to 'RADIATION_THERAPY'.

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

RADIATION_THERAPY Labels N
  NO 181
  YES 306
     
  Significant markers N = 0
Clinical variable #9: 'HISTOLOGICAL_TYPE'

30 genes related to 'HISTOLOGICAL_TYPE'.

Table S16.  Basic characteristics of clinical feature: 'HISTOLOGICAL_TYPE'

HISTOLOGICAL_TYPE Labels N
  OTHER, SPECIFY 7
  THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL 358
  THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED) 102
  THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES) 36
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

Table S17.  Get Full Table List of top 10 genes differentially expressed by 'HISTOLOGICAL_TYPE'

kruskal_wallis_P Q
LEPROT 2.264e-30 3.81e-26
PON2 9.334e-30 7.85e-26
GPR115 2.549e-29 1.43e-25
XDH 6.83e-29 2.87e-25
SLC34A2 1.613e-28 5.43e-25
CAPN2 3.286e-28 9.22e-25
C3ORF26 6.866e-28 1.65e-24
MICAL2 8.037e-28 1.69e-24
CD82 1.797e-27 3.36e-24
NRP2 2.104e-27 3.54e-24
Clinical variable #10: 'RADIATION_EXPOSURE'

No gene related to 'RADIATION_EXPOSURE'.

Table S18.  Basic characteristics of clinical feature: 'RADIATION_EXPOSURE'

RADIATION_EXPOSURE Labels N
  NO 423
  YES 17
     
  Significant markers N = 0
Clinical variable #11: 'EXTRATHYROIDAL_EXTENSION'

30 genes related to 'EXTRATHYROIDAL_EXTENSION'.

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

EXTRATHYROIDAL_EXTENSION Labels N
  MINIMAL (T3) 133
  MODERATE/ADVANCED (T4A) 18
  NONE 333
  VERY ADVANCED (T4B) 1
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'EXTRATHYROIDAL_EXTENSION'

Table S20.  Get Full Table List of top 10 genes differentially expressed by 'EXTRATHYROIDAL_EXTENSION'

kruskal_wallis_P Q
NRCAM 2.784e-13 3.91e-09
TBKBP1 4.65e-13 3.91e-09
PDGFB 2.992e-12 1.68e-08
SLC34A2 1.087e-11 3.94e-08
TMEM204 1.17e-11 3.94e-08
BBC3 1.956e-11 5.12e-08
DUSP5 2.129e-11 5.12e-08
COL1A1 3.11e-11 6.54e-08
GPR115 3.829e-11 7.16e-08
MICAL2 6.187e-11 1.04e-07
Clinical variable #12: 'RESIDUAL_TUMOR'

30 genes related to 'RESIDUAL_TUMOR'.

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

RESIDUAL_TUMOR Labels N
  R0 385
  R1 52
  R2 4
  RX 30
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

Table S22.  Get Full Table List of top 10 genes differentially expressed by 'RESIDUAL_TUMOR'

kruskal_wallis_P Q
SLC25A37 6.648e-06 0.089
NECAB2 1.057e-05 0.089
EGLN2 1.951e-05 0.0893
KCNK3 2.159e-05 0.0893
B3GNT5 2.89e-05 0.0893
TRIM3 4.088e-05 0.0893
TTLL6 4.098e-05 0.0893
GALNTL2 4.243e-05 0.0893
ADAM12 7.82e-05 0.13
TBKBP1 8.335e-05 0.13
Clinical variable #13: 'NUMBER_OF_LYMPH_NODES'

30 genes related to 'NUMBER_OF_LYMPH_NODES'.

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

NUMBER_OF_LYMPH_NODES Mean (SD) 3.66 (6.2)
  Significant markers N = 30
  pos. correlated 0
  neg. correlated 30
List of top 10 genes differentially expressed by 'NUMBER_OF_LYMPH_NODES'

Table S24.  Get Full Table List of top 10 genes significantly correlated to 'NUMBER_OF_LYMPH_NODES' by Spearman correlation test

SpearmanCorr corrP Q
MET -0.4041 1.029e-16 1.73e-12
TMEM173 -0.3913 1.103e-15 6.45e-12
FUT2 -0.3911 1.149e-15 6.45e-12
DAGLA -0.3853 3.233e-15 1.36e-11
MACF1 -0.3796 8.805e-15 2.96e-11
CAPN2 -0.3767 1.454e-14 3.7e-11
TAGLN2 -0.3758 1.707e-14 3.7e-11
DUSP6 -0.3755 1.795e-14 3.7e-11
RBM12 -0.3749 1.979e-14 3.7e-11
SMURF1 -0.3724 3.037e-14 5.11e-11
Clinical variable #14: 'MULTIFOCALITY'

One gene related to 'MULTIFOCALITY'.

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

MULTIFOCALITY Labels N
  MULTIFOCAL 227
  UNIFOCAL 266
     
  Significant markers N = 1
  Higher in UNIFOCAL 1
  Higher in MULTIFOCAL 0
List of one gene differentially expressed by 'MULTIFOCALITY'

Table S26.  Get Full Table List of one gene differentially expressed by 'MULTIFOCALITY'

W(pos if higher in 'UNIFOCAL') wilcoxontestP Q AUC
SORBS1 37026 1.458e-05 0.245 0.6132
Clinical variable #15: 'TUMOR_SIZE'

30 genes related to 'TUMOR_SIZE'.

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

TUMOR_SIZE Mean (SD) 2.98 (1.6)
  Significant markers N = 30
  pos. correlated 22
  neg. correlated 8
List of top 10 genes differentially expressed by 'TUMOR_SIZE'

Table S28.  Get Full Table List of top 10 genes significantly correlated to 'TUMOR_SIZE' by Spearman correlation test

SpearmanCorr corrP Q
TGFBR1 0.2262 4.502e-06 0.0323
NRBP1 0.2239 5.658e-06 0.0323
ALDOC 0.2237 5.755e-06 0.0323
KCNQ4 0.2141 1.453e-05 0.0508
UNKL 0.2102 2.099e-05 0.0508
SLC44A4 0.2099 2.161e-05 0.0508
HOXD8 0.2064 2.97e-05 0.0508
SNCA -0.2061 3.057e-05 0.0508
RNF148 -0.2052 3.326e-05 0.0508
DCI -0.2048 3.42e-05 0.0508
Clinical variable #16: 'RACE'

30 genes related to 'RACE'.

Table S29.  Basic characteristics of clinical feature: 'RACE'

RACE Labels N
  AMERICAN INDIAN OR ALASKA NATIVE 1
  ASIAN 52
  BLACK OR AFRICAN AMERICAN 27
  WHITE 331
     
  Significant markers N = 30
List of top 10 genes differentially expressed by 'RACE'

Table S30.  Get Full Table List of top 10 genes differentially expressed by 'RACE'

kruskal_wallis_P Q
SCAMP5 3.251e-16 5.47e-12
CDKN2AIP 1.607e-11 1.35e-07
RAD21L1 9.8e-09 3.94e-05
EGOT 1.08e-08 3.94e-05
CLTC 1.17e-08 3.94e-05
MRPS10 1.547e-08 4.3e-05
PTGES 1.79e-08 4.3e-05
PSMD5 4.291e-08 9.03e-05
SLC25A1 5.476e-08 0.000102
LOC100133161 6.06e-08 0.000102
Clinical variable #17: 'ETHNICITY'

No gene related to 'ETHNICITY'.

Table S31.  Basic characteristics of clinical feature: 'ETHNICITY'

ETHNICITY Labels N
  HISPANIC OR LATINO 38
  NOT HISPANIC OR LATINO 362
     
  Significant markers N = 0
Methods & Data
Input

  • Expresson data file = THCA-TP.meth.by_min_clin_corr.data.txt

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

  • Number of patients = 503

  • Number of genes = 16830

  • Number of clinical features = 17

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)
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