[1] "terrence_modification_tag"
[1] TRUE
[1] "nver"         "-nVNozzle.R1"
[1] "nfn"                                 "/xchip/tcga/Tools/Nozzle/v1.current"
[1] "Nozzle.R1"
[1] "successfully load Nozzle.R1"
[1] "ofn"        "-oTPRAD-TP"
[1] "opt"                    "-OPAGGREGRATED_CLUSTER"
[1] "tl" ""  
[1] "dx" ""  
[1] "opt"                 "AGGREGRATED_CLUSTER"
[1] "dx" ""  
[1] "cfn:"
[1] "/xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/PRAD-TP/10006169/PRAD-TP.merged_data.txt"
[1] "dfn:"
[1] "/xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/PRAD-TP/10667979/PRAD-TP.mergedcluster.txt"

nPatients in clinical file=276, in cluster file=425, common to both=274
[1]  10 274
[1] "CN_CNMF"
[1] 3
  1   2   3 
165  59  47 
  1   2   3 
165  59  47 
[1] "METHLYATION_CNMF"
[1] 3
  1   2   3 
 75  89 101 
  1   2   3 
 75  89 101 
[1] "RPPA_CNMF"
[1] 3
 1  2  3 
42 55 61 
 1  2  3 
42 55 61 
[1] "RPPA_CHIERARCHICAL"
[1] 3
 1  2  3 
48 64 46 
 1  2  3 
48 64 46 
[1] "MRNASEQ_CNMF"
[1] 3
 1  2  3 
86 85 97 
 1  2  3 
86 85 97 
[1] "MRNASEQ_CHIERARCHICAL"
[1] 3
 1  2  3 
97 82 89 
 1  2  3 
97 82 89 
[1] "MIRSEQ_CNMF"
[1] 3
 1  2  3  4  5 
50 23 58 60 81 
 1  2  3  4  5 
50 23 58 60 81 
[1] "MIRSEQ_CHIERARCHICAL"
[1] 3
  1   2   3 
 94  50 128 
  1   2   3 
 94  50 128 
[1] "MIRSEQ_MATURE_CNMF"
[1] 3
  1   2   3 
 73  99 100 
  1   2   3 
 73  99 100 
[1] "MIRSEQ_MATURE_CHIERARCHICAL"
[1] 3
 1  2  3  4  5 
44 25 55 62 86 
 1  2  3  4  5 
44 25 55 62 86 
[1] "terrence_modification_tag"
[1] TRUE
[1] "data2feature, selection=ALL"
 [1] "YEARSTOBIRTH"                          
 [2] "VITALSTATUS"                           
 [3] "DAYSTODEATH"                           
 [4] "DAYSTOLASTFOLLOWUP"                    
 [5] "PRIMARYSITEOFDESEASE"                  
 [6] "NEOPLASM.DISEASESTAGE"                 
 [7] "PATHOLOGY.T.STAGE"                     
 [8] "PATHOLOGY.N.STAGE"                     
 [9] "PATHOLOGY.M.STAGE"                     
[10] "DCCUPLOADDATE"                         
[11] "GENDER"                                
[12] "DATEOFINITIALPATHOLOGICDIAGNOSIS"      
[13] "RADIATIONTHERAPY"                      
[14] "HISTOLOGICALTYPE"                      
[15] "RADIATIONS.RADIATION.REGIMENINDICATION"
[16] "COMPLETENESS.OF.RESECTION"             
[17] "NUMBER.OF.LYMPH.NODES"                 
[18] "GLEASON_SCORE_COMBINED"                
[19] "GLEASON_SCORE_PRIMARY"                 
[20] "GLEASON_SCORE_SECONDARY"               
[21] "HIGHEST_GLEASON_SCORE"                 
[22] "GLEASON_SCORE"                         
[23] "PSA_RESULT_PREOP"                      
[24] "DAYS_TO_PREOP_PSA"                     
[25] "PSA_VALUE"                             
[26] "DAYS_TO_PSA"                           
[27] "RACE"                                  
[28] "ETHNICITY"                             
[29] "BATCHNUMBER"                           

Input Data has 29 rows and 274 columns.

[1] "Batch" "29"   
[1] "Last Follow UP"
TCGA-CH-5737 TCGA-CH-5738 TCGA-CH-5739 TCGA-CH-5740 TCGA-CH-5741 TCGA-CH-5743 
          91          212          671           31          395          425 
TCGA-CH-5744 TCGA-CH-5745 TCGA-CH-5746 TCGA-CH-5748 TCGA-CH-5750 TCGA-CH-5751 
          60           91          731           31          396         1065 
TCGA-CH-5752 TCGA-CH-5753 TCGA-CH-5754 TCGA-CH-5761 TCGA-CH-5762 TCGA-CH-5763 
         943           31           62           28         1339          365 
TCGA-CH-5764 TCGA-CH-5765 TCGA-CH-5766 TCGA-CH-5767 TCGA-CH-5768 TCGA-CH-5769 
          31          700           31          458          731           62 
TCGA-CH-5771 TCGA-CH-5772 TCGA-CH-5788 TCGA-CH-5789 TCGA-CH-5790 TCGA-CH-5791 
         396          486          821          304          974         1004 
TCGA-CH-5792 TCGA-CH-5794 TCGA-EJ-5494 TCGA-EJ-5495 TCGA-EJ-5496 TCGA-EJ-5497 
          91          882          826         1007          595          405 
TCGA-EJ-5498 TCGA-EJ-5499 TCGA-EJ-5501 TCGA-EJ-5502 TCGA-EJ-5503 TCGA-EJ-5504 
         906          533          937          800          716          880 
TCGA-EJ-5505 TCGA-EJ-5506 TCGA-EJ-5507 TCGA-EJ-5508 TCGA-EJ-5509 TCGA-EJ-5510 
         922          827          727         1184         1102         1116 
TCGA-EJ-5511 TCGA-EJ-5512 TCGA-EJ-5514 TCGA-EJ-5515 TCGA-EJ-5516 TCGA-EJ-5517 
         974         1172         1102         1240         1416         1454 
TCGA-EJ-5518 TCGA-EJ-5519 TCGA-EJ-5521 TCGA-EJ-5522 TCGA-EJ-5524 TCGA-EJ-5525 
        1590         1336         1578         1462         1310         1115 
TCGA-EJ-5526 TCGA-EJ-5527 TCGA-EJ-5530 TCGA-EJ-5531 TCGA-EJ-5532 TCGA-EJ-5542 
        1179         1209         1832          921         1456          884 
TCGA-EJ-7115 TCGA-EJ-7123 TCGA-EJ-7125 TCGA-EJ-7314 TCGA-EJ-7315 TCGA-EJ-7317 
         822         1491         1860          295          289          285 
TCGA-EJ-7321 TCGA-EJ-7327 TCGA-EJ-7328 TCGA-EJ-7330 TCGA-EJ-7331 TCGA-EJ-7781 
         233          175          170          191            9          169 
TCGA-EJ-7782 TCGA-EJ-7783 TCGA-EJ-7784 TCGA-EJ-7785 TCGA-EJ-7786 TCGA-EJ-7788 
          64          787          781          625          442           52 
TCGA-EJ-7789 TCGA-EJ-7791 TCGA-EJ-7792 TCGA-EJ-7793 TCGA-EJ-7794 TCGA-EJ-7797 
         552          596          526          114          701          369 
TCGA-EJ-8468 TCGA-EJ-8469 TCGA-EJ-8470 TCGA-EJ-8472 TCGA-EJ-8474 TCGA-EJ-A46B 
        1961         1946          821           NA          314          162 
TCGA-EJ-A46D TCGA-EJ-A46E TCGA-EJ-A46F TCGA-EJ-A46G TCGA-EJ-A46H TCGA-EJ-A46I 
         129          532          299           93          229          668 
TCGA-EJ-A65B TCGA-EJ-A65D TCGA-EJ-A65E TCGA-EJ-A65G TCGA-EJ-A65J TCGA-EJ-A65M 
         536          393          417          278           91          230 
TCGA-FC-7708 TCGA-FC-7961 TCGA-FC-A4JI TCGA-FC-A5OB TCGA-FC-A66V TCGA-FC-A6HD 
         864          469          409          273          524           54 
TCGA-FC-A8O0 TCGA-G9-6329 TCGA-G9-6332 TCGA-G9-6333 TCGA-G9-6336 TCGA-G9-6338 
          54         1266         2657         2465         2068         2028 
TCGA-G9-6339 TCGA-G9-6342 TCGA-G9-6343 TCGA-G9-6347 TCGA-G9-6348 TCGA-G9-6351 
        2110         1696          554         1718         1515         1404 
TCGA-G9-6353 TCGA-G9-6354 TCGA-G9-6356 TCGA-G9-6361 TCGA-G9-6362 TCGA-G9-6363 
        1542         1501         1434         1415         1443         1378 
TCGA-G9-6364 TCGA-G9-6365 TCGA-G9-6366 TCGA-G9-6367 TCGA-G9-6369 TCGA-G9-6370 
        1198         1363          101         1222         1215         1156 
TCGA-G9-6371 TCGA-G9-6373 TCGA-G9-6377 TCGA-G9-6378 TCGA-G9-6379 TCGA-G9-6384 
        1226          811          958         1155         1393          765 
TCGA-G9-6385 TCGA-G9-6494 TCGA-G9-6496 TCGA-G9-6498 TCGA-G9-6499 TCGA-G9-7510 
         830         1771         1726         1608         1543         1185 
TCGA-G9-7519 TCGA-G9-7521 TCGA-G9-7522 TCGA-G9-7523 TCGA-G9-7525 TCGA-H9-7775 
         849          942         1075          857          994          185 
TCGA-H9-A6BX TCGA-H9-A6BY TCGA-HC-7075 TCGA-HC-7077 TCGA-HC-7078 TCGA-HC-7079 
         570          112          601           NA          162           89 
TCGA-HC-7080 TCGA-HC-7081 TCGA-HC-7209 TCGA-HC-7210 TCGA-HC-7211 TCGA-HC-7212 
          61           72           41          149           24          870 
TCGA-HC-7213 TCGA-HC-7230 TCGA-HC-7231 TCGA-HC-7232 TCGA-HC-7233 TCGA-HC-7736 
          60           51           72           89           52           22 
TCGA-HC-7737 TCGA-HC-7738 TCGA-HC-7740 TCGA-HC-7742 TCGA-HC-7744 TCGA-HC-7745 
          64           81           53           45           51           63 
TCGA-HC-7747 TCGA-HC-7748 TCGA-HC-7749 TCGA-HC-7750 TCGA-HC-7752 TCGA-HC-7817 
          57          792           60           34           98           32 
TCGA-HC-7818 TCGA-HC-7819 TCGA-HC-7820 TCGA-HC-7821 TCGA-HC-8213 TCGA-HC-8216 
          45          543          747           56          589          682 
TCGA-HC-8256 TCGA-HC-8257 TCGA-HC-8258 TCGA-HC-8259 TCGA-HC-8260 TCGA-HC-8261 
          95          726           66           69           59          546 
TCGA-HC-8262 TCGA-HC-8264 TCGA-HC-8265 TCGA-HC-8266 TCGA-HC-A48F TCGA-HC-A4ZV 
         679           48          483           35           46           23 
TCGA-HC-A631 TCGA-HC-A632 TCGA-HC-A6AL TCGA-HC-A6AN TCGA-HC-A6AO TCGA-HC-A6AP 
          54           61           67           49          167           71 
TCGA-HC-A6AQ TCGA-HC-A6AS TCGA-HC-A6HX TCGA-HC-A6HY TCGA-HI-7168 TCGA-HI-7169 
         106           44           38          134         2391         1949 
TCGA-HI-7170 TCGA-HI-7171 TCGA-J4-8198 TCGA-J4-8200 TCGA-J4-A67K TCGA-J4-A67L 
        2522           NA          614          374          662          518 
TCGA-J4-A67M TCGA-J4-A67N TCGA-J4-A67O TCGA-J4-A67Q TCGA-J4-A67R TCGA-J4-A67S 
         488          503          491          624          454          523 
TCGA-J4-A67T TCGA-J4-A6G1 TCGA-J4-A6G3 TCGA-J4-A6M7 TCGA-J4-A83I TCGA-J4-A83J 
         183          405          528          279          279          310 
TCGA-J4-A83K TCGA-J4-A83L TCGA-J4-A83M TCGA-J4-A83N TCGA-J9-A52B TCGA-J9-A52D 
         206          316          437          617          422          212 
TCGA-J9-A52E TCGA-KK-A59V TCGA-KK-A59X TCGA-KK-A59Y TCGA-KK-A59Z TCGA-KK-A5A1 
         323         2859         1712         1359         1879         2364 
TCGA-KK-A6DY TCGA-KK-A6E3 TCGA-KK-A6E4 TCGA-KK-A6E5 TCGA-KK-A6E7 TCGA-M7-A71Y 
        3524         2056         2753         1498         2040          158 
TCGA-M7-A71Z TCGA-M7-A720 TCGA-M7-A721 TCGA-M7-A722 TCGA-M7-A723 TCGA-M7-A724 
         209          302          204          822           62          483 
TCGA-M7-A725 TCGA-QU-A6IL TCGA-QU-A6IM TCGA-QU-A6IP TCGA-VN-A88I TCGA-VN-A88K 
          96           97         1247         2620          269          776 
TCGA-VN-A88L TCGA-VN-A88M TCGA-VN-A88N TCGA-VN-A88O TCGA-VN-A88P TCGA-VN-A88Q 
         394          183          176          481          917         1001 
TCGA-VN-A88R TCGA-WW-A8ZI TCGA-XA-A8JR TCGA-Y6-A8TL 
         459          176          119          775 
Variable 1:'AGE':	nDistinctValues=32,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 2:'VITALSTATUS':	nDistinctValues=2,	numeric=TRUE,	binary=TRUE,	exclude=TRUE.
Variable 3:'DAYSTODEATH':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 4:'DAYSTOLASTFOLLOWUP':	nDistinctValues=239,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
Variable 5:'PRIMARY.SITE.OF.DISEASE':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 6:'NEOPLASM.DISEASESTAGE':	nDistinctValues=0,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 7:'PATHOLOGY.T.STAGE':	nDistinctValues=6,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 8:'PATHOLOGY.N.STAGE':	nDistinctValues=2,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 9:'PATHOLOGY.M.STAGE':	nDistinctValues=0,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 10:'DCCUPLOADDAY':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 11:'GENDER':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 12:'DAYOFINITIALPATHOLOGICDIAGNOSIS':	nDistinctValues=12,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
Variable 13:'RADIATION.THERAPY':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 14:'HISTOLOGICAL.TYPE':	nDistinctValues=2,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 15:'RADIATIONS.RADIATION.REGIMENINDICATION':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 16:'COMPLETENESS.OF.RESECTION':	nDistinctValues=4,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 17:'NUMBER.OF.LYMPH.NODES':	nDistinctValues=6,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 18:'GLEASON_SCORE_COMBINED':	nDistinctValues=6,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 19:'GLEASON_SCORE_PRIMARY':	nDistinctValues=5,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 20:'GLEASON_SCORE_SECONDARY':	nDistinctValues=4,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 21:'HIGHEST_GLEASON_SCORE':	nDistinctValues=0,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 22:'GLEASON_SCORE':	nDistinctValues=5,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 23:'PSA_RESULT_PREOP':	nDistinctValues=144,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 24:'DAYS_TO_PREOP_PSA':	nDistinctValues=144,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
Variable 25:'PSA_VALUE':	nDistinctValues=59,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 26:'DAYS_TO_PSA':	nDistinctValues=210,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
Variable 27:'RACE':	nDistinctValues=3,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 28:'ETHNICITY':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
Variable 29:'BATCH.NUMBER':	nDistinctValues=16,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
[1] "rownames(nsurv.mat)"
 [1] "AGE"                       "PATHOLOGY.T.STAGE"        
 [3] "PATHOLOGY.N.STAGE"         "HISTOLOGICAL.TYPE"        
 [5] "COMPLETENESS.OF.RESECTION" "NUMBER.OF.LYMPH.NODES"    
 [7] "GLEASON_SCORE_COMBINED"    "GLEASON_SCORE_PRIMARY"    
 [9] "GLEASON_SCORE_SECONDARY"   "GLEASON_SCORE"            
[11] "PSA_RESULT_PREOP"          "PSA_VALUE"                
[13] "RACE"                     
[1] "TUMOR.?STAGE"
[1] "TUMOR.?GRADE"
[1] "PATHOLOGY.T" "2"          
[1] "PATHOLOGY.N" "3"          
Output Data has 274 columns, 0 survival variables, and 13 non-survival variables.
AGE, nv=32, binary=FALSE, numeric=TRUE
PATHOLOGY.T.STAGE, nv=3, binary=FALSE, numeric=TRUE
[1] "grepl('PATHOLOGY.T',vnm)"
vv
 T2  T3  T4 
128 139   5 
[1] "table(vv)"
vv
 T2  T3  T4 
128 139   5 
$ClinVariableName
[1] "PATHOLOGY.T.STAGE"

$Table
vv
 T2  T3  T4 
128 139   5 

$nClasses
[1] 3

$ClinVariableType
[1] "multiclass(3)"


 T2  T3  T4 
128 139   5 
PATHOLOGY.N.STAGE, nv=2, binary=FALSE, numeric=TRUE
HISTOLOGICAL.TYPE, nv=2, binary=FALSE, numeric=FALSE
COMPLETENESS.OF.RESECTION, nv=4, binary=FALSE, numeric=FALSE
NUMBER.OF.LYMPH.NODES, nv=6, binary=FALSE, numeric=TRUE
GLEASON_SCORE_COMBINED, nv=6, binary=FALSE, numeric=TRUE
GLEASON_SCORE_PRIMARY, nv=5, binary=FALSE, numeric=TRUE
GLEASON_SCORE_SECONDARY, nv=4, binary=FALSE, numeric=TRUE
GLEASON_SCORE, nv=5, binary=FALSE, numeric=TRUE
PSA_RESULT_PREOP, nv=144, binary=FALSE, numeric=TRUE
PSA_VALUE, nv=59, binary=FALSE, numeric=TRUE
RACE, nv=3, binary=FALSE, numeric=FALSE

Clustering(1) Variable = CN_CNMF
D1V1, continuous
          vv
clus       T2 T3 T4
  subtype1 89 72  2
  subtype2 12 45  2
  subtype3 27 19  1
D1V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       89       12       27
  T3       72       45       19
  T4        2        2        1
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D1V3, binary
          cls
clus         0   1
  subtype1 123   5
  subtype2  43  14
  subtype3  35   5
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1  123    5
  subtype2   43   14
  subtype3   35    5
   clus
vv  subtype1 subtype2 subtype3
  0      123       43       35
  1        5       14        5
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D1V4, binary
          cls
clus         0   1
  subtype1   4 161
  subtype2   1  58
  subtype3   0  47
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    4  161
  subtype2    1   58
  subtype3    0   47
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        4        1        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE         161       58       47
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus        R0  R1  R2  RX
  subtype1 129  21   2   5
  subtype2  35  20   0   0
  subtype3  28  16   1   1
D1V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0      129       35       28
  R1       21       20       16
  R2        2        0        1
  RX        5        0        1
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D1V6, continuous
D1V7, continuous
D1V8, continuous
D1V9, continuous
D1V10, continuous
D1V11, continuous
D1V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     2                         3    87
  subtype2     0                         2    32
  subtype3     0                         2    27
D1V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            2        0        0
  BLACK OR AFRICAN AMERICAN        3        2        2
  WHITE                           87       32       27
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(2) Variable = METHLYATION_CNMF
D2V1, continuous
          vv
clus       T2 T3 T4
  subtype1 35 37  3
  subtype2 49 38  0
  subtype3 39 60  2
D2V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       35       49       39
  T3       37       38       60
  T4        3        0        2
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D2V3, binary
          cls
clus        0  1
  subtype1 55  8
  subtype2 68  2
  subtype3 71 14
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   55    8
  subtype2   68    2
  subtype3   71   14
   clus
vv  subtype1 subtype2 subtype3
  0       55       68       71
  1        8        2       14
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D2V4, binary
          cls
clus        0  1
  subtype1  2 73
  subtype2  1 88
  subtype3  3 98
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    2   73
  subtype2    1   88
  subtype3    3   98
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        2        1        3
  PROSTATE ADENOCARCINOMA ACINAR TYPE          73       88       98
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 49 21  0  1
  subtype2 66 16  0  2
  subtype3 73 19  2  3
D2V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       49       66       73
  R1       21       16       19
  R2        0        0        2
  RX        1        2        3
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D2V6, continuous
D2V7, continuous
D2V8, continuous
D2V9, continuous
D2V10, continuous
D2V11, continuous
D2V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         3    40
  subtype2     1                         1    48
  subtype3     1                         3    59
D2V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        1        1
  BLACK OR AFRICAN AMERICAN        3        1        3
  WHITE                           40       48       59
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(3) Variable = RPPA_CNMF
D3V1, continuous
          vv
clus       T2 T3 T4
  subtype1 18 23  0
  subtype2 27 27  1
  subtype3 13 44  4
D3V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       18       27       13
  T3       23       27       44
  T4        0        1        4
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D3V3, binary
          cls
clus        0  1
  subtype1 38  4
  subtype2 43  2
  subtype3 42 11
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   38    4
  subtype2   43    2
  subtype3   42   11
   clus
vv  subtype1 subtype2 subtype3
  0       38       43       42
  1        4        2       11
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D3V4, binary
          cls
clus        0  1
  subtype1  0 42
  subtype2  1 54
  subtype3  1 60
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0   42
  subtype2    1   54
  subtype3    1   60
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        0        1        1
  PROSTATE ADENOCARCINOMA ACINAR TYPE          42       54       60
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 36  4  0  1
  subtype2 40  9  0  2
  subtype3 35 20  1  1
D3V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       36       40       35
  R1        4        9       20
  R2        0        0        1
  RX        1        2        1
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D3V6, continuous
D3V7, continuous
D3V8, continuous
D3V9, continuous
D3V10, continuous
D3V11, continuous
D3V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    39
  subtype2     2                         1    42
  subtype3     0                         5    40
D3V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        2        0
  BLACK OR AFRICAN AMERICAN        0        1        5
  WHITE                           39       42       40
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(4) Variable = RPPA_CHIERARCHICAL
D4V1, continuous
          vv
clus       T2 T3 T4
  subtype1 22 25  0
  subtype2 15 45  4
  subtype3 21 24  1
D4V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       22       15       21
  T3       25       45       24
  T4        0        4        1
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D4V3, binary
          cls
clus        0  1
  subtype1 43  4
  subtype2 43 12
  subtype3 37  1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   43    4
  subtype2   43   12
  subtype3   37    1
   clus
vv  subtype1 subtype2 subtype3
  0       43       43       37
  1        4       12        1
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D4V4, binary
          cls
clus        0  1
  subtype1  0 48
  subtype2  2 62
  subtype3  0 46
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0   48
  subtype2    2   62
  subtype3    0   46
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        0        2        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE          48       62       46
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 43  4  0  0
  subtype2 35 20  1  3
  subtype3 33  9  0  1
D4V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       43       35       33
  R1        4       20        9
  R2        0        1        0
  RX        0        3        1
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D4V6, continuous
D4V7, continuous
D4V8, continuous
D4V9, continuous
D4V10, continuous
D4V11, continuous
D4V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    45
  subtype2     0                         5    40
  subtype3     2                         1    36
D4V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        0        2
  BLACK OR AFRICAN AMERICAN        0        5        1
  WHITE                           45       40       36
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(5) Variable = MRNASEQ_CNMF
D5V1, continuous
          vv
clus       T2 T3 T4
  subtype1 47 36  3
  subtype2 45 37  1
  subtype3 35 61  1
D5V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       47       45       35
  T3       36       37       61
  T4        3        1        1
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V3, binary
          cls
clus        0  1
  subtype1 62  8
  subtype2 69  2
  subtype3 67 14
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   62    8
  subtype2   69    2
  subtype3   67   14
   clus
vv  subtype1 subtype2 subtype3
  0       62       69       67
  1        8        2       14
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V4, binary
          cls
clus        0  1
  subtype1  3 83
  subtype2  0 85
  subtype3  2 95
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3   83
  subtype2    0   85
  subtype3    2   95
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        3        0        2
  PROSTATE ADENOCARCINOMA ACINAR TYPE          83       85       95
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 57 24  1  1
  subtype2 63 14  0  2
  subtype3 70 18  2  3
D5V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       57       63       70
  R1       24       14       18
  R2        1        0        2
  RX        1        2        3
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V6, continuous
D5V7, continuous
D5V8, continuous
D5V9, continuous
D5V10, continuous
D5V11, continuous
D5V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         3    38
  subtype2     2                         1    52
  subtype3     0                         3    57
D5V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        2        0
  BLACK OR AFRICAN AMERICAN        3        1        3
  WHITE                           38       52       57
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(6) Variable = MRNASEQ_CHIERARCHICAL
D6V1, continuous
          vv
clus       T2 T3 T4
  subtype1 56 39  2
  subtype2 40 40  0
  subtype3 31 55  3
D6V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       56       40       31
  T3       39       40       55
  T4        2        0        3
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V3, binary
          cls
clus        0  1
  subtype1 71  8
  subtype2 67  4
  subtype3 60 12
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   71    8
  subtype2   67    4
  subtype3   60   12
   clus
vv  subtype1 subtype2 subtype3
  0       71       67       60
  1        8        4       12
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V4, binary
          cls
clus        0  1
  subtype1  2 95
  subtype2  0 82
  subtype3  3 86
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    2   95
  subtype2    0   82
  subtype3    3   86
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        2        0        3
  PROSTATE ADENOCARCINOMA ACINAR TYPE          95       82       86
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 67 24  1  1
  subtype2 64 11  0  2
  subtype3 59 21  2  3
D6V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       67       64       59
  R1       24       11       21
  R2        1        0        2
  RX        1        2        3
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V6, continuous
D6V7, continuous
D6V8, continuous
D6V9, continuous
D6V10, continuous
D6V11, continuous
D6V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         5    37
  subtype2     2                         0    63
  subtype3     0                         2    47
D6V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        2        0
  BLACK OR AFRICAN AMERICAN        5        0        2
  WHITE                           37       63       47
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(7) Variable = MIRSEQ_CNMF
D7V1, continuous
          vv
clus       T2 T3 T4
  subtype1 23 25  0
  subtype2 16  6  1
  subtype3 22 36  0
  subtype4 23 34  3
  subtype5 43 38  0
D7V2, multiclass
    clus
vv   subtype1 subtype2 subtype3 subtype4 subtype5
  T2       23       16       22       23       43
  T3       25        6       36       34       38
  T4        0        1        0        3        0
[1] 3 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D7V3, binary
          cls
clus        0  1
  subtype1 39  2
  subtype2 17  1
  subtype3 42  6
  subtype4 46 10
  subtype5 59  4
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   39    2
  subtype2   17    1
  subtype3   42    6
  subtype4   46   10
  subtype5   59    4
   clus
vv  subtype1 subtype2 subtype3 subtype4 subtype5
  0       39       17       42       46       59
  1        2        1        6       10        4
[1] 2 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D7V4, binary
          cls
clus        0  1
  subtype1  0 50
  subtype2  0 23
  subtype3  6 52
  subtype4  0 60
  subtype5  0 81
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0   50
  subtype2    0   23
  subtype3    6   52
  subtype4    0   60
  subtype5    0   81
                                        clus
vv                                       subtype1 subtype2 subtype3 subtype4
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        0        0        6        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE          50       23       52       60
                                        clus
vv                                       subtype5
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE          81
[1] 2 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 35  9  0  2
  subtype2 15  6  1  0
  subtype3 35 19  0  2
  subtype4 45 15  0  0
  subtype5 63  8  2  2
D7V5, multiclass
    clus
vv   subtype1 subtype2 subtype3 subtype4 subtype5
  R0       35       15       35       45       63
  R1        9        6       19       15        8
  R2        0        1        0        0        2
  RX        2        0        2        0        2
[1] 4 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D7V6, continuous
D7V7, continuous
D7V8, continuous
D7V9, continuous
D7V10, continuous
D7V11, continuous
D7V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    25
  subtype2     0                         1     8
  subtype3     0                         0     2
  subtype4     1                         1    50
  subtype5     1                         5    61
D7V13, multiclass
[1] "Remove cluster labels:" "subtype3"              
clus
subtype1 subtype2 subtype3 subtype4 subtype5 
      25        9        2       52       67 
  [1] "subtype4" "subtype1" "subtype5" "subtype4" "subtype4" "subtype1"
  [7] "subtype4" "subtype4" "subtype1" "subtype4" "subtype4" "subtype4"
 [13] "subtype4" "subtype4" "subtype4" "subtype1" "subtype1" "subtype1"
 [19] "subtype4" "subtype4" "subtype4" "subtype4" "subtype4" "subtype4"
 [25] "subtype2" "subtype4" "subtype1" "subtype4" "subtype4" "subtype4"
 [31] "subtype4" "subtype1" "subtype4" "subtype1" "subtype1" "subtype4"
 [37] "subtype4" "subtype1" "subtype4" "subtype4" "subtype1" "subtype4"
 [43] "subtype1" "subtype4" "subtype4" "subtype4" "subtype1" "subtype2"
 [49] "subtype1" "subtype1" "subtype2" "subtype4" "subtype4" "subtype4"
 [55] "subtype1" "subtype1" "subtype4" "subtype4" "subtype1" "subtype4"
 [61] "subtype1" "subtype4" "subtype4" "subtype5" "subtype5" "subtype5"
 [67] "subtype5" "subtype5" "subtype5" "subtype5" "subtype5" "subtype5"
 [73] "subtype5" "subtype5" "subtype5" "subtype5" "subtype5" "subtype5"
 [79] "subtype5" "subtype5" "subtype5" "subtype5" "subtype2" "subtype2"
 [85] "subtype5" "subtype5" "subtype5" "subtype5" "subtype1" "subtype4"
 [91] "subtype5" "subtype1" "subtype5" "subtype5" "subtype5" "subtype4"
 [97] "subtype5" "subtype1" "subtype4" "subtype5" "subtype4" "subtype4"
[103] "subtype4" "subtype2" "subtype5" "subtype4" "subtype5" "subtype1"
[109] "subtype4" "subtype4" "subtype5" "subtype2" "subtype4" "subtype4"
[115] "subtype4" "subtype4" "subtype5" "subtype5" "subtype5" "subtype5"
[121] "subtype5" "subtype5" "subtype5" "subtype5" "subtype5" "subtype5"
[127] "subtype5" "subtype5" "subtype5" "subtype5" "subtype5" "subtype5"
[133] "subtype5" "subtype5" "subtype5" "subtype5" "subtype2" "subtype5"
[139] "subtype5" "subtype5" "subtype1" "subtype5" "subtype5" "subtype5"
[145] "subtype5" "subtype5" "subtype5" "subtype5" "subtype5" "subtype5"
[151] "subtype2" "subtype5" "subtype5"
                           clus
vv                          subtype1 subtype2 subtype4 subtype5
  ASIAN                            0        0        1        1
  BLACK OR AFRICAN AMERICAN        0        1        1        5
  WHITE                           25        8       50       61
[1] 3 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(8) Variable = MIRSEQ_CHIERARCHICAL
D8V1, continuous
          vv
clus       T2 T3 T4
  subtype1 44 48  0
  subtype2 31 15  4
  subtype3 52 76  0
D8V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       44       31       52
  T3       48       15       76
  T4        0        4        0
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V3, binary
          cls
clus        0  1
  subtype1 77  5
  subtype2 37  0
  subtype3 89 18
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   77    5
  subtype2   37    0
  subtype3   89   18
   clus
vv  subtype1 subtype2 subtype3
  0       77       37       89
  1        5        0       18
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V4, binary
          cls
clus         0   1
  subtype1   3  91
  subtype2   0  50
  subtype3   3 125
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3   91
  subtype2    0   50
  subtype3    3  125
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        3        0        3
  PROSTATE ADENOCARCINOMA ACINAR TYPE          91       50      125
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 70 16  0  2
  subtype2 34 12  1  1
  subtype3 89 29  2  3
D8V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       70       34       89
  R1       16       12       29
  R2        0        1        2
  RX        2        1        3
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V6, continuous
D8V7, continuous
D8V8, continuous
D8V9, continuous
D8V10, continuous
D8V11, continuous
D8V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     1                         1    50
  subtype2     0                         2    28
  subtype3     1                         4    68
D8V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            1        0        1
  BLACK OR AFRICAN AMERICAN        1        2        4
  WHITE                           50       28       68
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(9) Variable = MIRSEQ_MATURE_CNMF
D9V1, continuous
          vv
clus       T2 T3 T4
  subtype1 31 42  0
  subtype2 51 47  1
  subtype3 45 50  3
D9V2, multiclass
    clus
vv   subtype1 subtype2 subtype3
  T2       31       51       45
  T3       42       47       50
  T4        0        1        3
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D9V3, binary
          cls
clus        0  1
  subtype1 50 10
  subtype2 72  8
  subtype3 81  5
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   50   10
  subtype2   72    8
  subtype3   81    5
   clus
vv  subtype1 subtype2 subtype3
  0       50       72       81
  1       10        8        5
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D9V4, binary
          cls
clus        0  1
  subtype1  4 69
  subtype2  0 99
  subtype3  2 98
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    4   69
  subtype2    0   99
  subtype3    2   98
                                        clus
vv                                       subtype1 subtype2 subtype3
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        4        0        2
  PROSTATE ADENOCARCINOMA ACINAR TYPE          69       99       98
[1] 2 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 48 21  0  2
  subtype2 73 15  2  2
  subtype3 72 21  1  2
D9V5, multiclass
    clus
vv   subtype1 subtype2 subtype3
  R0       48       73       72
  R1       21       15       21
  R2        0        2        1
  RX        2        2        2
[1] 4 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D9V6, continuous
D9V7, continuous
D9V8, continuous
D9V9, continuous
D9V10, continuous
D9V11, continuous
D9V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    19
  subtype2     1                         5    69
  subtype3     1                         2    58
D9V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        1        1
  BLACK OR AFRICAN AMERICAN        0        5        2
  WHITE                           19       69       58
[1] 3 3
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"

Clustering(10) Variable = MIRSEQ_MATURE_CHIERARCHICAL
D10V1, continuous
          vv
clus       T2 T3 T4
  subtype1 25 18  0
  subtype2 14  7  4
  subtype3 20 35  0
  subtype4 22 39  0
  subtype5 46 40  0
D10V2, multiclass
    clus
vv   subtype1 subtype2 subtype3 subtype4 subtype5
  T2       25       14       20       22       46
  T3       18        7       35       39       40
  T4        0        4        0        0        0
[1] 3 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V3, binary
          cls
clus        0  1
  subtype1 33  0
  subtype2 19  1
  subtype3 40  7
  subtype4 50  9
  subtype5 61  6
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   33    0
  subtype2   19    1
  subtype3   40    7
  subtype4   50    9
  subtype5   61    6
   clus
vv  subtype1 subtype2 subtype3 subtype4 subtype5
  0       33       19       40       50       61
  1        0        1        7        9        6
[1] 2 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V4, binary
          cls
clus        0  1
  subtype1  3 41
  subtype2  0 25
  subtype3  3 52
  subtype4  0 62
  subtype5  0 86
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3   41
  subtype2    0   25
  subtype3    3   52
  subtype4    0   62
  subtype5    0   86
                                        clus
vv                                       subtype1 subtype2 subtype3 subtype4
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        3        0        3        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE          41       25       52       62
                                        clus
vv                                       subtype5
  PROSTATE ADENOCARCINOMA  OTHER SUBTYPE        0
  PROSTATE ADENOCARCINOMA ACINAR TYPE          86
[1] 2 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
          vv
clus       R0 R1 R2 RX
  subtype1 29 10  0  1
  subtype2 16  7  1  0
  subtype3 37 15  0  2
  subtype4 48 13  0  1
  subtype5 63 12  2  2
D10V5, multiclass
    clus
vv   subtype1 subtype2 subtype3 subtype4 subtype5
  R0       29       16       37       48       63
  R1       10        7       15       13       12
  R2        0        1        0        0        2
  RX        1        0        2        1        2
[1] 4 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V6, continuous
D10V7, continuous
D10V8, continuous
D10V9, continuous
D10V10, continuous
D10V11, continuous
D10V12, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0     4
  subtype2     0                         2    11
  subtype3     0                         1     9
  subtype4     1                         0    61
  subtype5     1                         4    61
D10V13, multiclass
                           clus
vv                          subtype1 subtype2 subtype3 subtype4 subtype5
  ASIAN                            0        0        0        1        1
  BLACK OR AFRICAN AMERICAN        0        2        1        0        4
  WHITE                            4       11        9       61       61
[1] 3 5
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
