[1] "ofn"        "-oTKICH-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/KICH-TP/19775213/KICH-TP.merged_data.txt"
[1] "dfn:"
[1] "/xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KICH-TP/20147307/KICH-TP.mergedcluster.txt"

nPatients in clinical file=111, in cluster file=66, common to both=66
[1] 10 66
[1] "CN_CNMF"
[1] 3
 1  2  3 
 5 47 14 
 1  2  3 
 5 47 14 
[1] "METHLYATION_CNMF"
[1] 3
 1  2  3 
18 35 13 
 1  2  3 
18 35 13 
[1] "RPPA_CNMF"
[1] 3
 1  2  3 
26 17 20 
 1  2  3 
26 17 20 
[1] "RPPA_CHIERARCHICAL"
[1] 3
 1  2  3 
29 20 14 
 1  2  3 
29 20 14 
[1] "MRNASEQ_CNMF"
[1] 3
 1  2  3  4 
19 22 15 10 
 1  2  3  4 
19 22 15 10 
[1] "MRNASEQ_CHIERARCHICAL"
[1] 3
 1  2  3  4  5  6  7 
19 16  7  6 10  4  4 
 1  2  3  4  5  6  7 
19 16  7  6 10  4  4 
[1] "MIRSEQ_CNMF"
[1] 3
 1  2  3 
20 24 22 
 1  2  3 
20 24 22 
[1] "MIRSEQ_CHIERARCHICAL"
[1] 3
 1  2  3  4  5  6  7 
18 11 12  6  5  5  9 
 1  2  3  4  5  6  7 
18 11 12  6  5  5  9 
[1] "MIRSEQ_MATURE_CNMF"
[1] 3
 1  2 
19 29 
 1  2 
19 29 
[1] "MIRSEQ_MATURE_CHIERARCHICAL"
[1] 3
 1  2  3  4  5  6  7  8 
10  6  5  8  4  6  5  4 
 1  2  3  4  5  6  7  8 
10  6  5  8  4  6  5  4 
[1] "data2feature, selection=ALL"
 [1] "YEARS_TO_BIRTH"                      
 [2] "VITAL_STATUS"                        
 [3] "DAYS_TO_DEATH"                       
 [4] "DAYS_TO_LAST_FOLLOWUP"               
 [5] "TUMOR_TISSUE_SITE"                   
 [6] "PATHOLOGIC_STAGE"                    
 [7] "PATHOLOGY_T_STAGE"                   
 [8] "PATHOLOGY_N_STAGE"                   
 [9] "PATHOLOGY_M_STAGE"                   
[10] "GENDER"                              
[11] "DATE_OF_INITIAL_PATHOLOGIC_DIAGNOSIS"
[12] "RADIATION_THERAPY"                   
[13] "KARNOFSKY_PERFORMANCE_SCORE"         
[14] "HISTOLOGICAL_TYPE"                   
[15] "NUMBER_PACK_YEARS_SMOKED"            
[16] "YEAR_OF_TOBACCO_SMOKING_ONSET"       
[17] "RACE"                                
[18] "ETHNICITY"                           

Input Data has 18 rows and 66 columns.

[1] "Last Follow UP"
Variable 1:'YEARS_TO_BIRTH':	nDistinctValues=39,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 2:'VITAL_STATUS':	nDistinctValues=2,	numeric=TRUE,	binary=TRUE,	exclude=FALSE.
Variable 3:'DAYS_TO_DEATH':	nDistinctValues=9,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
[1] "exclude grep('DAYS_?TO', vnms) to deal with survival parameters seperately"
Variable 4:'DAYS_TO_LAST_FOLLOWUP':	nDistinctValues=54,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
[1] "exclude grep('FOLLOWUP', vnms) to deal with survival parameters seperately"
Variable 5:'TUMOR_TISSUE_SITE':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
[1] "TUMOR_TISSUE_SITE is excluded in the analysis because there is no more than two cases of (unique non-NA values)"
Variable 6:'PATHOLOGIC_STAGE':	nDistinctValues=4,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 7:'PATHOLOGY_T_STAGE':	nDistinctValues=9,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 8:'PATHOLOGY_N_STAGE':	nDistinctValues=4,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 9:'PATHOLOGY_M_STAGE':	nDistinctValues=3,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 10:'GENDER':	nDistinctValues=2,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 11:'DATE_OF_INITIAL_PATHOLOGIC_DIAGNOSIS':	nDistinctValues=12,	numeric=TRUE,	binary=FALSE,	exclude=TRUE.
[1] "exclude grep('DATE', vnms) to deal with survival parameters seperately"
Variable 12:'RADIATION_THERAPY':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
[1] "RADIATION_THERAPY is excluded in the analysis because there is no more than two cases of (unique non-NA values)"
Variable 13:'KARNOFSKY_PERFORMANCE_SCORE':	nDistinctValues=2,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 14:'HISTOLOGICAL_TYPE':	nDistinctValues=1,	numeric=FALSE,	binary=FALSE,	exclude=TRUE.
[1] "HISTOLOGICAL_TYPE is excluded in the analysis because there is no more than two cases of (unique non-NA values)"
Variable 15:'NUMBER_PACK_YEARS_SMOKED':	nDistinctValues=10,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 16:'YEAR_OF_TOBACCO_SMOKING_ONSET':	nDistinctValues=8,	numeric=TRUE,	binary=FALSE,	exclude=FALSE.
Variable 17:'RACE':	nDistinctValues=3,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
Variable 18:'ETHNICITY':	nDistinctValues=2,	numeric=FALSE,	binary=FALSE,	exclude=FALSE.
[1] "## **** detect survival parameters (defined in index such as ind_OS, ind_MFS, ind_RFS, ind_RFS, ind_BCR and ind_d2ssd) *** ##"
[1] "detected survival parameters using [ind_OS, overall_survival]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [ind_OS, curated_overall_survival]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [ind_TCGAOS]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survivial parameters using [ind_MFS]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [ind_RFS]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [ind_BCR]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [ind_Progression]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using [index_additional_survival_time]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "detected survival parameters using condition: [is.null(surv.mat)&&(selection=='SURV')]"
[1] "survival parameters accumulated so far"
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "************ conversion from categorical data to rank data ********** "
[1] "PATHOLOGY_T_STAGE is converted to numeric rank data using modified categoies"
[1] "PATHOLOGY_N_STAGE is converted to numeric rank data using modified categoies"
[1] "PATHOLOGY_M_STAGE is converted to rank data using modified categoies"
[1] "****** SUMMARY ***** "
Output Data has 66 columns, 1 survival variables, and 11 non-survival variables.
[1] "* survival variables: "
[1] "DAYS_TO_DEATH_OR_LAST_FUP" "VITAL_STATUS"             
[1] "* non-survival variables: "
 [1] "YEARS_TO_BIRTH"                "PATHOLOGIC_STAGE"             
 [3] "PATHOLOGY_T_STAGE"             "PATHOLOGY_N_STAGE"            
 [5] "PATHOLOGY_M_STAGE"             "GENDER"                       
 [7] "KARNOFSKY_PERFORMANCE_SCORE"   "NUMBER_PACK_YEARS_SMOKED"     
 [9] "YEAR_OF_TOBACCO_SMOKING_ONSET" "RACE"                         
[11] "ETHNICITY"                    
YEARS_TO_BIRTH, nv=39, binary=FALSE, numeric=TRUE
PATHOLOGIC_STAGE, nv=4, binary=FALSE, numeric=FALSE
PATHOLOGY_T_STAGE, nv=4, binary=FALSE, numeric=TRUE
[1] "grepl('PATHOLOGY.T',vnm)"
vv
T1 T2 T3 T4 
21 25 18  2 
[1] "table(vv)"
vv
   T1    T2 T3+T4 
   21    25    20 
$ClinVariableName
[1] "PATHOLOGY_T_STAGE"

$Table
vv
T1 T2 T3 T4 
21 25 18  2 

$nClasses
[1] 3

$ClinVariableType
[1] "multiclass(3)"


   T1    T2 T3+T4 
   21    25    20 
PATHOLOGY_N_STAGE, nv=3, binary=FALSE, numeric=TRUE
[1] "grepl('PATHOLOGY.N',vnm)"
vv
N0 N1 N2 
40  3  2 
[1] "table(vv)"
vv
   N0 N1+N2 
   40     5 
$ClinVariableName
[1] "PATHOLOGY_N_STAGE"

$Table
vv
N0 N1 N2 
40  3  2 

$ClinVariableType
[1] "binary"

$Class0_nSamples
[1] 40

$Class1_nSamples
[1] 5

$Class0_label
[1] "N0"

$Class1_label
[1] "N1+N2"


   N0 N1+N2 
   40     5 
PATHOLOGY_M_STAGE, nv=2, binary=FALSE, numeric=TRUE
GENDER, nv=2, binary=FALSE, numeric=FALSE
KARNOFSKY_PERFORMANCE_SCORE, nv=2, binary=FALSE, numeric=TRUE
NUMBER_PACK_YEARS_SMOKED, nv=10, binary=FALSE, numeric=TRUE
YEAR_OF_TOBACCO_SMOKING_ONSET, nv=8, binary=FALSE, numeric=TRUE
RACE, nv=3, binary=FALSE, numeric=FALSE
ETHNICITY, nv=2, binary=FALSE, numeric=FALSE

Clustering(1) Variable = CN_CNMF
D1V1, survival
          sevent
clus2       0  1
  subtype1  4  1
  subtype2 38  8
  subtype3 14  0
subtype1 subtype2 subtype3 
       5       46       14 
subtype1 subtype2 subtype3 
       1        8        0 
$subtype1
TCGA-KL-8323 TCGA-KL-8329 TCGA-KM-8440 TCGA-KN-8434 TCGA-KO-8407 
       38.07        90.48        44.91        59.08        92.71 

$subtype2
TCGA-KL-8324 TCGA-KL-8325 TCGA-KL-8326 TCGA-KL-8328 TCGA-KL-8330 TCGA-KL-8331 
      141.73        23.84       109.22       102.81       108.62        98.96 
TCGA-KL-8332 TCGA-KL-8333 TCGA-KL-8334 TCGA-KL-8335 TCGA-KL-8336 TCGA-KL-8338 
       84.89        98.33        96.95        92.55        30.25        86.56 
TCGA-KL-8339 TCGA-KL-8340 TCGA-KL-8341 TCGA-KL-8342 TCGA-KL-8343 TCGA-KL-8344 
       28.11        66.54        24.79        73.91        71.41        28.80 
TCGA-KL-8345 TCGA-KL-8346 TCGA-KM-8438 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8442 
       54.74        62.37       151.96        23.28        28.80        47.24 
TCGA-KM-8443 TCGA-KM-8477 TCGA-KM-8639 TCGA-KN-8418 TCGA-KN-8419 TCGA-KN-8421 
       49.97        51.06        31.33       129.37        23.47        87.91 
TCGA-KN-8422 TCGA-KN-8423 TCGA-KN-8424 TCGA-KN-8426 TCGA-KN-8427 TCGA-KN-8428 
       25.48        25.91        65.19         3.55         0.99        52.27 
TCGA-KN-8429 TCGA-KN-8431 TCGA-KN-8437 TCGA-KO-8403 TCGA-KO-8404 TCGA-KO-8405 
       87.12        19.33       100.87       105.47        10.68       100.47 
TCGA-KO-8406 TCGA-KO-8408 TCGA-KO-8409 TCGA-KO-8410 
      103.40        16.67       104.55       103.10 

$subtype3
TCGA-KL-8327 TCGA-KL-8337 TCGA-KM-8476 TCGA-KN-8425 TCGA-KN-8432 TCGA-KN-8433 
      137.06        44.84       123.12       104.52         2.50        11.21 
TCGA-KN-8435 TCGA-KN-8436 TCGA-KO-8411 TCGA-KO-8413 TCGA-KO-8414 TCGA-KO-8415 
      115.00        42.35       130.13        99.88       105.60        96.62 
TCGA-KO-8416 TCGA-KO-8417 
       90.48        92.55 

subtype1 subtype2 subtype3 
   38.07     0.99     2.50 
subtype1 subtype2 subtype3 
   92.71   151.96   137.06 
subtype1 subtype2 subtype3 
  59.080   65.865   98.250 
[1] "38.1 - 92.7 (59.1)" "1.0 - 152.0 (65.9)" "2.5 - 137.1 (98.2)"
D1V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       1        2         2        0
  subtype2      13       18        10        6
  subtype3       7        5         2        0
D1V3, multiclass
           clus
vv          subtype1 subtype2 subtype3
  STAGE I          1       13        7
  STAGE II         2       18        5
  STAGE III        2       10        2
  STAGE IV         0        6        0
[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"
          vv
clus       T1 T2 T3+T4
  subtype1  1  2     2
  subtype2 13 18    16
  subtype3  7  5     2
D1V4, multiclass
       clus
vv      subtype1 subtype2 subtype3
  T1           1       13        7
  T2           2       18        5
  T3+T4        2       16        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"
          vv
clus       N0 N1+N2
  subtype1  2     0
  subtype2 30     5
  subtype3  8     0
D1V5, multiclass
[1] "Remove cluster labels:" "subtype1"              
clus
subtype1 subtype2 subtype3 
       2       35        8 
 [1] "subtype2" "subtype3" "subtype2" "subtype2" "subtype2" "subtype2"
 [7] "subtype2" "subtype2" "subtype2" "subtype2" "subtype3" "subtype2"
[13] "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2"
[19] "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2"
[25] "subtype2" "subtype2" "subtype2" "subtype2" "subtype3" "subtype2"
[31] "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2"
[37] "subtype2" "subtype2" "subtype3" "subtype3" "subtype3" "subtype3"
[43] "subtype3"
       clus
vv      subtype2 subtype3
  N0          30        8
  N1+N2        5        0
[1] 2 2
[1] FALSE
D1V6, binary
          cls
clus        0  1
  subtype1  3  0
  subtype2 23  2
  subtype3  8  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3    0
  subtype2   23    2
  subtype3    8    0
   clus
vv  subtype1 subtype2 subtype3
  0        3       23        8
  1        0        2        0
[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"
D1V7, binary
          cls
clus        0  1
  subtype1  3  2
  subtype2 18 29
  subtype3  6  8
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3    2
  subtype2   18   29
  subtype3    6    8
        clus
vv       subtype1 subtype2 subtype3
  FEMALE        3       18        6
  MALE          2       29        8
[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"
D1V8, binary
          cls
clus       0 1
  subtype1 2 0
  subtype2 5 3
  subtype3 3 0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    2    0
  subtype2    5    3
  subtype3    3    0
     clus
vv    subtype1 subtype2 subtype3
  100        2        5        3
  90         0        3        0
[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"
D1V9, continuous
[1] "Remove cluster labels:" "subtype3"              
clus
subtype2 subtype3 
       9        2 
[1] "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2"
[8] "subtype2" "subtype2"
D1V10, continuous
[1] "Remove cluster labels:" "subtype3"              
clus
subtype2 subtype3 
       7        1 
[1] "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2" "subtype2"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     1                         1     3
  subtype2     1                         2    42
  subtype3     0                         1    13
D1V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            1        1        0
  BLACK OR AFRICAN AMERICAN        1        2        1
  WHITE                            3       42       13
[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"
D1V12, binary
          cls
clus        0  1
  subtype1  0  3
  subtype2  2 20
  subtype3  2  9
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0    3
  subtype2    2   20
  subtype3    2    9
                        clus
vv                       subtype1 subtype2 subtype3
  HISPANIC OR LATINO            0        2        2
  NOT HISPANIC OR LATINO        3       20        9
[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"

Clustering(2) Variable = METHLYATION_CNMF
D2V1, survival
          sevent
clus2       0  1
  subtype1 16  2
  subtype2 29  5
  subtype3 11  2
subtype1 subtype2 subtype3 
      18       34       13 
subtype1 subtype2 subtype3 
       2        5        2 
$subtype1
TCGA-KL-8323 TCGA-KL-8325 TCGA-KL-8327 TCGA-KL-8342 TCGA-KL-8345 TCGA-KM-8443 
       38.07        23.84       137.06        73.91        54.74        49.97 
TCGA-KM-8476 TCGA-KN-8419 TCGA-KN-8425 TCGA-KN-8434 TCGA-KO-8403 TCGA-KO-8406 
      123.12        23.47       104.52        59.08       105.47       103.40 
TCGA-KO-8407 TCGA-KO-8409 TCGA-KO-8411 TCGA-KO-8415 TCGA-KO-8416 TCGA-KO-8417 
       92.71       104.55       130.13        96.62        90.48        92.55 

$subtype2
TCGA-KL-8324 TCGA-KL-8326 TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8330 TCGA-KL-8331 
      141.73       109.22       102.81        90.48       108.62        98.96 
TCGA-KL-8332 TCGA-KL-8334 TCGA-KL-8335 TCGA-KL-8336 TCGA-KL-8337 TCGA-KL-8338 
       84.89        96.95        92.55        30.25        44.84        86.56 
TCGA-KL-8339 TCGA-KL-8340 TCGA-KL-8341 TCGA-KL-8344 TCGA-KL-8346 TCGA-KM-8438 
       28.11        66.54        24.79        28.80        62.37       151.96 
TCGA-KM-8440 TCGA-KM-8442 TCGA-KN-8418 TCGA-KN-8421 TCGA-KN-8422 TCGA-KN-8423 
       44.91        47.24       129.37        87.91        25.48        25.91 
TCGA-KN-8424 TCGA-KN-8426 TCGA-KN-8428 TCGA-KN-8429 TCGA-KN-8431 TCGA-KN-8432 
       65.19         3.55        52.27        87.12        19.33         2.50 
TCGA-KN-8435 TCGA-KN-8436 TCGA-KO-8408 TCGA-KO-8414 
      115.00        42.35        16.67       105.60 

$subtype3
TCGA-KL-8333 TCGA-KL-8343 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8477 TCGA-KM-8639 
       98.33        71.41        23.28        28.80        51.06        31.33 
TCGA-KN-8427 TCGA-KN-8433 TCGA-KN-8437 TCGA-KO-8404 TCGA-KO-8405 TCGA-KO-8410 
        0.99        11.21       100.87        10.68       100.47       103.10 
TCGA-KO-8413 
       99.88 

subtype1 subtype2 subtype3 
   23.47     2.50     0.99 
subtype1 subtype2 subtype3 
  137.06   151.96   103.10 
subtype1 subtype2 subtype3 
  92.630   65.865   51.060 
[1] "23.5 - 137.1 (92.6)" "2.5 - 152.0 (65.9)"  "1.0 - 103.1 (51.1)" 
D2V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       8        7         3        0
  subtype2       6       17         8        4
  subtype3       7        1         3        2
D2V3, multiclass
           clus
vv          subtype1 subtype2 subtype3
  STAGE I          8        6        7
  STAGE II         7       17        1
  STAGE III        3        8        3
  STAGE IV         0        4        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"
          vv
clus       T1 T2 T3+T4
  subtype1  8  7     3
  subtype2  6 17    12
  subtype3  7  1     5
D2V4, multiclass
       clus
vv      subtype1 subtype2 subtype3
  T1           8        6        7
  T2           7       17        1
  T3+T4        3       12        5
[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"
          vv
clus       N0 N1+N2
  subtype1 12     0
  subtype2 23     4
  subtype3  5     1
D2V5, multiclass
       clus
vv      subtype1 subtype2 subtype3
  N0          12       23        5
  N1+N2        0        4        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"
D2V6, binary
          cls
clus        0  1
  subtype1 12  0
  subtype2 17  2
  subtype3  5  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   12    0
  subtype2   17    2
  subtype3    5    0
   clus
vv  subtype1 subtype2 subtype3
  0       12       17        5
  1        0        2        0
[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"
D2V7, binary
          cls
clus        0  1
  subtype1  8 10
  subtype2 15 20
  subtype3  4  9
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    8   10
  subtype2   15   20
  subtype3    4    9
        clus
vv       subtype1 subtype2 subtype3
  FEMALE        8       15        4
  MALE         10       20        9
[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"
D2V8, binary
          cls
clus       0 1
  subtype1 4 0
  subtype2 3 1
  subtype3 3 2
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    4    0
  subtype2    3    1
  subtype3    3    2
     clus
vv    subtype1 subtype2 subtype3
  100        4        3        3
  90         0        1        2
[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"
D2V9, continuous
[1] "Remove cluster labels:" "subtype3"              
clus
subtype1 subtype2 subtype3 
       3        6        2 
[1] "subtype2" "subtype2" "subtype2" "subtype2" "subtype1" "subtype2" "subtype2"
[8] "subtype1" "subtype1"
D2V10, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype3"              
clus
subtype1 subtype2 subtype3 
       2        4        2 
[1] "subtype2" "subtype2" "subtype2" "subtype2"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         1    17
  subtype2     2                         3    28
  subtype3     0                         0    13
D2V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        2        0
  BLACK OR AFRICAN AMERICAN        1        3        0
  WHITE                           17       28       13
[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"
D2V12, binary
          cls
clus        0  1
  subtype1  1 11
  subtype2  1 12
  subtype3  2  9
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1   11
  subtype2    1   12
  subtype3    2    9
                        clus
vv                       subtype1 subtype2 subtype3
  HISPANIC OR LATINO            1        1        2
  NOT HISPANIC OR LATINO       11       12        9
[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"

Clustering(3) Variable = RPPA_CNMF
D3V1, survival
          sevent
clus2       0  1
  subtype1 22  4
  subtype2 14  3
  subtype3 18  1
subtype1 subtype2 subtype3 
      26       17       19 
subtype1 subtype2 subtype3 
       4        3        1 
$subtype1
TCGA-KL-8324 TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8331 TCGA-KL-8332 TCGA-KL-8334 
      141.73       102.81        90.48        98.96        84.89        96.95 
TCGA-KL-8335 TCGA-KL-8337 TCGA-KL-8338 TCGA-KL-8340 TCGA-KL-8341 TCGA-KL-8342 
       92.55        44.84        86.56        66.54        24.79        73.91 
TCGA-KL-8346 TCGA-KM-8438 TCGA-KM-8440 TCGA-KM-8443 TCGA-KN-8425 TCGA-KN-8426 
       62.37       151.96        44.91        49.97       104.52         3.55 
TCGA-KN-8428 TCGA-KN-8429 TCGA-KN-8433 TCGA-KN-8434 TCGA-KO-8404 TCGA-KO-8408 
       52.27        87.12        11.21        59.08        10.68        16.67 
TCGA-KO-8414 TCGA-KO-8415 
      105.60        96.62 

$subtype2
TCGA-KL-8325 TCGA-KL-8333 TCGA-KL-8336 TCGA-KL-8343 TCGA-KL-8345 TCGA-KM-8442 
       23.84        98.33        30.25        71.41        54.74        47.24 
TCGA-KN-8418 TCGA-KN-8423 TCGA-KN-8424 TCGA-KN-8427 TCGA-KN-8432 TCGA-KO-8403 
      129.37        25.91        65.19         0.99         2.50       105.47 
TCGA-KO-8405 TCGA-KO-8406 TCGA-KO-8409 TCGA-KO-8410 TCGA-KO-8413 
      100.47       103.40       104.55       103.10        99.88 

$subtype3
TCGA-KL-8326 TCGA-KL-8327 TCGA-KL-8330 TCGA-KL-8339 TCGA-KL-8344 TCGA-KM-8439 
      109.22       137.06       108.62        28.11        28.80        23.28 
TCGA-KM-8441 TCGA-KM-8476 TCGA-KM-8639 TCGA-KN-8419 TCGA-KN-8421 TCGA-KN-8431 
       28.80       123.12        31.33        23.47        87.91        19.33 
TCGA-KN-8435 TCGA-KN-8436 TCGA-KN-8437 TCGA-KO-8407 TCGA-KO-8411 TCGA-KO-8416 
      115.00        42.35       100.87        92.71       130.13        90.48 
TCGA-KO-8417 
       92.55 

subtype1 subtype2 subtype3 
    3.55     0.99    19.33 
subtype1 subtype2 subtype3 
  151.96   129.37   137.06 
subtype1 subtype2 subtype3 
   79.40    71.41    90.48 
[1] "3.5 - 152.0 (79.4)"  "1.0 - 129.4 (71.4)"  "19.3 - 137.1 (90.5)"
D3V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       4       12         7        3
  subtype2       6        6         3        2
  subtype3       9        7         3        1
D3V3, multiclass
           clus
vv          subtype1 subtype2 subtype3
  STAGE I          4        6        9
  STAGE II        12        6        7
  STAGE III        7        3        3
  STAGE IV         3        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"
          vv
clus       T1 T2 T3+T4
  subtype1  4 12    10
  subtype2  6  6     5
  subtype3  9  7     4
D3V4, multiclass
       clus
vv      subtype1 subtype2 subtype3
  T1           4        6        9
  T2          12        6        7
  T3+T4       10        5        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"
          vv
clus       N0 N1+N2
  subtype1 18     2
  subtype2 11     2
  subtype3 10     1
D3V5, multiclass
       clus
vv      subtype1 subtype2 subtype3
  N0          18       11       10
  N1+N2        2        2        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"
D3V6, binary
          cls
clus        0  1
  subtype1 15  1
  subtype2 10  0
  subtype3  8  1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   15    1
  subtype2   10    0
  subtype3    8    1
   clus
vv  subtype1 subtype2 subtype3
  0       15       10        8
  1        1        0        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"
D3V7, binary
          cls
clus        0  1
  subtype1 11 15
  subtype2  7 10
  subtype3  7 13
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   11   15
  subtype2    7   10
  subtype3    7   13
        clus
vv       subtype1 subtype2 subtype3
  FEMALE       11        7        7
  MALE         15       10       13
[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"
D3V8, binary
          cls
clus       0 1
  subtype1 5 0
  subtype2 1 1
  subtype3 4 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    5    0
  subtype2    1    1
  subtype3    4    1
     clus
vv    subtype1 subtype2 subtype3
  100        5        1        4
  90         0        1        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"
D3V9, continuous
[1] "Remove cluster labels:" "subtype2"              
clus
subtype1 subtype2 subtype3 
       6        2        3 
[1] "subtype1" "subtype3" "subtype1" "subtype1" "subtype3" "subtype1" "subtype1"
[8] "subtype1" "subtype3"
D3V10, continuous
[1] "Remove cluster labels:" "subtype2"               "subtype3"              
clus
subtype1 subtype2 subtype3 
       4        2        2 
[1] "subtype1" "subtype1" "subtype1" "subtype1"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     2                         2    22
  subtype2     0                         0    16
  subtype3     0                         2    17
D3V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            2        0        0
  BLACK OR AFRICAN AMERICAN        2        0        2
  WHITE                           22       16       17
[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"
D3V12, binary
          cls
clus        0  1
  subtype1  1  9
  subtype2  3  7
  subtype3  0 15
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1    9
  subtype2    3    7
  subtype3    0   15
                        clus
vv                       subtype1 subtype2 subtype3
  HISPANIC OR LATINO            1        3        0
  NOT HISPANIC OR LATINO        9        7       15
[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"

Clustering(4) Variable = RPPA_CHIERARCHICAL
D4V1, survival
          sevent
clus2       0  1
  subtype1 24  5
  subtype2 18  1
  subtype3 12  2
subtype1 subtype2 subtype3 
      29       19       14 
subtype1 subtype2 subtype3 
       5        1        2 
$subtype1
TCGA-KL-8324 TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8331 TCGA-KL-8332 TCGA-KL-8334 
      141.73       102.81        90.48        98.96        84.89        96.95 
TCGA-KL-8335 TCGA-KL-8337 TCGA-KL-8338 TCGA-KL-8339 TCGA-KL-8340 TCGA-KL-8341 
       92.55        44.84        86.56        28.11        66.54        24.79 
TCGA-KL-8342 TCGA-KL-8346 TCGA-KM-8438 TCGA-KM-8440 TCGA-KM-8443 TCGA-KN-8418 
       73.91        62.37       151.96        44.91        49.97       129.37 
TCGA-KN-8421 TCGA-KN-8426 TCGA-KN-8427 TCGA-KN-8428 TCGA-KN-8429 TCGA-KN-8433 
       87.91         3.55         0.99        52.27        87.12        11.21 
TCGA-KN-8434 TCGA-KO-8404 TCGA-KO-8408 TCGA-KO-8414 TCGA-KO-8415 
       59.08        10.68        16.67       105.60        96.62 

$subtype2
TCGA-KL-8325 TCGA-KL-8326 TCGA-KL-8327 TCGA-KL-8330 TCGA-KL-8344 TCGA-KL-8345 
       23.84       109.22       137.06       108.62        28.80        54.74 
TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8476 TCGA-KM-8639 TCGA-KN-8419 TCGA-KN-8431 
       23.28        28.80       123.12        31.33        23.47        19.33 
TCGA-KN-8435 TCGA-KN-8436 TCGA-KN-8437 TCGA-KO-8407 TCGA-KO-8411 TCGA-KO-8416 
      115.00        42.35       100.87        92.71       130.13        90.48 
TCGA-KO-8417 
       92.55 

$subtype3
TCGA-KL-8333 TCGA-KL-8336 TCGA-KL-8343 TCGA-KM-8442 TCGA-KN-8423 TCGA-KN-8424 
       98.33        30.25        71.41        47.24        25.91        65.19 
TCGA-KN-8425 TCGA-KN-8432 TCGA-KO-8403 TCGA-KO-8405 TCGA-KO-8406 TCGA-KO-8409 
      104.52         2.50       105.47       100.47       103.40       104.55 
TCGA-KO-8410 TCGA-KO-8413 
      103.10        99.88 

subtype1 subtype2 subtype3 
    0.99    19.33     2.50 
subtype1 subtype2 subtype3 
  151.96   137.06   105.47 
subtype1 subtype2 subtype3 
  73.910   90.480   99.105 
[1] "1.0 - 152.0 (73.9)"  "19.3 - 137.1 (90.5)" "2.5 - 105.5 (99.1)" 
D4V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       3       14         7        5
  subtype2       9        7         4        0
  subtype3       7        4         2        1
D4V3, multiclass
           clus
vv          subtype1 subtype2 subtype3
  STAGE I          3        9        7
  STAGE II        14        7        4
  STAGE III        7        4        2
  STAGE IV         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"
          vv
clus       T1 T2 T3+T4
  subtype1  3 14    12
  subtype2  9  7     4
  subtype3  7  4     3
D4V4, multiclass
       clus
vv      subtype1 subtype2 subtype3
  T1           3        9        7
  T2          14        7        4
  T3+T4       12        4        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"
          vv
clus       N0 N1+N2
  subtype1 19     4
  subtype2 10     0
  subtype3 10     1
D4V5, multiclass
       clus
vv      subtype1 subtype2 subtype3
  N0          19       10       10
  N1+N2        4        0        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"
D4V6, binary
          cls
clus        0  1
  subtype1 15  2
  subtype2 10  0
  subtype3  8  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   15    2
  subtype2   10    0
  subtype3    8    0
   clus
vv  subtype1 subtype2 subtype3
  0       15       10        8
  1        2        0        0
[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"
D4V7, binary
          cls
clus        0  1
  subtype1 13 16
  subtype2  7 13
  subtype3  5  9
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   13   16
  subtype2    7   13
  subtype3    5    9
        clus
vv       subtype1 subtype2 subtype3
  FEMALE       13        7        5
  MALE         16       13        9
[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"
D4V8, binary
          cls
clus       0 1
  subtype1 5 0
  subtype2 4 1
  subtype3 1 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    5    0
  subtype2    4    1
  subtype3    1    1
     clus
vv    subtype1 subtype2 subtype3
  100        5        4        1
  90         0        1        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"
D4V9, continuous
[1] "Remove cluster labels:" "subtype3"              
clus
subtype1 subtype2 subtype3 
       6        4        1 
 [1] "subtype1" "subtype2" "subtype1" "subtype1" "subtype2" "subtype2"
 [7] "subtype1" "subtype1" "subtype1" "subtype2"
D4V10, continuous
[1] "Remove cluster labels:" "subtype3"              
clus
subtype1 subtype2 subtype3 
       4        3        1 
[1] "subtype2" "subtype1" "subtype1" "subtype2" "subtype2" "subtype1" "subtype1"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     2                         2    25
  subtype2     0                         2    17
  subtype3     0                         0    13
D4V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            2        0        0
  BLACK OR AFRICAN AMERICAN        2        2        0
  WHITE                           25       17       13
[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"
D4V12, binary
          cls
clus        0  1
  subtype1  1 10
  subtype2  0 14
  subtype3  3  7
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1   10
  subtype2    0   14
  subtype3    3    7
                        clus
vv                       subtype1 subtype2 subtype3
  HISPANIC OR LATINO            1        0        3
  NOT HISPANIC OR LATINO       10       14        7
[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"

Clustering(5) Variable = MRNASEQ_CNMF
D5V1, survival
          sevent
clus2       0  1
  subtype1 17  2
  subtype2 20  1
  subtype3 11  4
  subtype4  8  2
subtype1 subtype2 subtype3 subtype4 
      19       21       15       10 
subtype1 subtype2 subtype3 subtype4 
       2        1        4        2 
$subtype1
TCGA-KL-8323 TCGA-KL-8327 TCGA-KL-8331 TCGA-KL-8334 TCGA-KL-8338 TCGA-KL-8341 
       38.07       137.06        98.96        96.95        86.56        24.79 
TCGA-KL-8342 TCGA-KL-8345 TCGA-KM-8443 TCGA-KM-8476 TCGA-KN-8424 TCGA-KN-8434 
       73.91        54.74        49.97       123.12        65.19        59.08 
TCGA-KN-8435 TCGA-KO-8403 TCGA-KO-8406 TCGA-KO-8407 TCGA-KO-8411 TCGA-KO-8416 
      115.00       105.47       103.40        92.71       130.13        90.48 
TCGA-KO-8417 
       92.55 

$subtype2
TCGA-KL-8324 TCGA-KL-8325 TCGA-KL-8330 TCGA-KL-8332 TCGA-KL-8346 TCGA-KM-8438 
      141.73        23.84       108.62        84.89        62.37       151.96 
TCGA-KM-8442 TCGA-KN-8419 TCGA-KN-8422 TCGA-KN-8423 TCGA-KN-8425 TCGA-KN-8426 
       47.24        23.47        25.48        25.91       104.52         3.55 
TCGA-KN-8431 TCGA-KN-8432 TCGA-KN-8433 TCGA-KO-8405 TCGA-KO-8409 TCGA-KO-8410 
       19.33         2.50        11.21       100.47       104.55       103.10 
TCGA-KO-8413 TCGA-KO-8414 TCGA-KO-8415 
       99.88       105.60        96.62 

$subtype3
TCGA-KL-8326 TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8335 TCGA-KL-8336 TCGA-KL-8337 
      109.22       102.81        90.48        92.55        30.25        44.84 
TCGA-KL-8339 TCGA-KL-8340 TCGA-KM-8440 TCGA-KN-8418 TCGA-KN-8421 TCGA-KN-8428 
       28.11        66.54        44.91       129.37        87.91        52.27 
TCGA-KN-8429 TCGA-KN-8436 TCGA-KO-8408 
       87.12        42.35        16.67 

$subtype4
TCGA-KL-8333 TCGA-KL-8343 TCGA-KL-8344 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8477 
       98.33        71.41        28.80        23.28        28.80        51.06 
TCGA-KM-8639 TCGA-KN-8427 TCGA-KN-8437 TCGA-KO-8404 
       31.33         0.99       100.87        10.68 

subtype1 subtype2 subtype3 subtype4 
   24.79     2.50    16.67     0.99 
subtype1 subtype2 subtype3 subtype4 
  137.06   151.96   129.37   100.87 
subtype1 subtype2 subtype3 subtype4 
  92.550   84.890   66.540   30.065 
[1] "24.8 - 137.1 (92.5)" "2.5 - 152.0 (84.9)"  "16.7 - 129.4 (66.5)"
[4] "1.0 - 100.9 (30.1)" 
D5V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       7        6         5        1
  subtype2       8       11         2        1
  subtype3       1        7         5        2
  subtype4       5        1         2        2
D5V3, multiclass
           clus
vv          subtype1 subtype2 subtype3 subtype4
  STAGE I          7        8        1        5
  STAGE II         6       11        7        1
  STAGE III        5        2        5        2
  STAGE IV         1        1        2        2
[1] 4 4
[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       T1 T2 T3+T4
  subtype1  7  6     6
  subtype2  8 11     3
  subtype3  1  7     7
  subtype4  5  1     4
D5V4, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4
  T1           7        8        1        5
  T2           6       11        7        1
  T3+T4        6        3        7        4
[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"
          vv
clus       N0 N1+N2
  subtype1 12     1
  subtype2 15     0
  subtype3 10     3
  subtype4  3     1
D5V5, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4
  N0          12       15       10        3
  N1+N2        1        0        3        1
[1] 2 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V6, binary
          cls
clus        0  1
  subtype1 13  0
  subtype2 11  1
  subtype3  7  1
  subtype4  3  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   13    0
  subtype2   11    1
  subtype3    7    1
  subtype4    3    0
   clus
vv  subtype1 subtype2 subtype3 subtype4
  0       13       11        7        3
  1        0        1        1        0
[1] 2 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V7, binary
          cls
clus        0  1
  subtype1  9 10
  subtype2 11 11
  subtype3  5 10
  subtype4  2  8
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    9   10
  subtype2   11   11
  subtype3    5   10
  subtype4    2    8
        clus
vv       subtype1 subtype2 subtype3 subtype4
  FEMALE        9       11        5        2
  MALE         10       11       10        8
[1] 2 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V8, binary
          cls
clus       0 1
  subtype1 4 0
  subtype2 2 1
  subtype3 2 0
  subtype4 2 2
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    4    0
  subtype2    2    1
  subtype3    2    0
  subtype4    2    2
     clus
vv    subtype1 subtype2 subtype3 subtype4
  100        4        2        2        2
  90         0        1        0        2
[1] 2 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D5V9, continuous
[1] "Remove cluster labels:" "subtype4"              
clus
subtype1 subtype2 subtype3 subtype4 
       3        4        3        1 
 [1] "subtype2" "subtype2" "subtype3" "subtype1" "subtype1" "subtype3"
 [7] "subtype2" "subtype3" "subtype2" "subtype1"
D5V10, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype3"              
[4] "subtype4"              
clus
subtype1 subtype2 subtype3 subtype4 
       2        3        2        1 
[1] "subtype2" "subtype2" "subtype2"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     1                         0    18
  subtype2     0                         2    18
  subtype3     1                         2    12
  subtype4     0                         0    10
D5V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3 subtype4
  ASIAN                            1        0        1        0
  BLACK OR AFRICAN AMERICAN        0        2        2        0
  WHITE                           18       18       12       10
[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"
D5V12, binary
          cls
clus        0  1
  subtype1  0 10
  subtype2  4 10
  subtype3  0  5
  subtype4  0  7
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0   10
  subtype2    4   10
  subtype3    0    5
  subtype4    0    7
                        clus
vv                       subtype1 subtype2 subtype3 subtype4
  HISPANIC OR LATINO            0        4        0        0
  NOT HISPANIC OR LATINO       10       10        5        7
[1] 2 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(6) Variable = MRNASEQ_CHIERARCHICAL
D6V1, survival
          sevent
clus2       0  1
  subtype1 17  2
  subtype2 15  0
  subtype3  5  2
  subtype4  5  1
  subtype5  6  4
  subtype6  4  0
  subtype7  4  0
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
      19       15        7        6       10        4        4 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
       2        0        2        1        4        0        0 
$subtype1
TCGA-KL-8323 TCGA-KL-8327 TCGA-KL-8331 TCGA-KL-8334 TCGA-KL-8340 TCGA-KL-8341 
       38.07       137.06        98.96        96.95        66.54        24.79 
TCGA-KL-8342 TCGA-KL-8345 TCGA-KM-8443 TCGA-KM-8476 TCGA-KN-8424 TCGA-KN-8434 
       73.91        54.74        49.97       123.12        65.19        59.08 
TCGA-KN-8435 TCGA-KO-8403 TCGA-KO-8406 TCGA-KO-8407 TCGA-KO-8411 TCGA-KO-8416 
      115.00       105.47       103.40        92.71       130.13        90.48 
TCGA-KO-8417 
       92.55 

$subtype2
TCGA-KL-8324 TCGA-KL-8330 TCGA-KM-8438 TCGA-KN-8422 TCGA-KN-8423 TCGA-KN-8425 
      141.73       108.62       151.96        25.48        25.91       104.52 
TCGA-KN-8426 TCGA-KN-8431 TCGA-KN-8432 TCGA-KN-8433 TCGA-KO-8409 TCGA-KO-8410 
        3.55        19.33         2.50        11.21       104.55       103.10 
TCGA-KO-8413 TCGA-KO-8414 TCGA-KO-8415 
       99.88       105.60        96.62 

$subtype3
TCGA-KL-8325 TCGA-KL-8332 TCGA-KL-8346 TCGA-KM-8442 TCGA-KN-8427 TCGA-KO-8404 
       23.84        84.89        62.37        47.24         0.99        10.68 
TCGA-KO-8405 
      100.47 

$subtype4
TCGA-KL-8326 TCGA-KL-8333 TCGA-KL-8343 TCGA-KL-8344 TCGA-KM-8477 TCGA-KN-8419 
      109.22        98.33        71.41        28.80        51.06        23.47 

$subtype5
TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8335 TCGA-KL-8336 TCGA-KL-8339 TCGA-KM-8440 
      102.81        90.48        92.55        30.25        28.11        44.91 
TCGA-KN-8418 TCGA-KN-8428 TCGA-KN-8429 TCGA-KO-8408 
      129.37        52.27        87.12        16.67 

$subtype6
TCGA-KL-8337 TCGA-KL-8338 TCGA-KN-8421 TCGA-KN-8436 
       44.84        86.56        87.91        42.35 

$subtype7
TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8639 TCGA-KN-8437 
       23.28        28.80        31.33       100.87 

subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
   24.79     2.50     0.99    23.47    16.67    42.35    23.28 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
  137.06   151.96   100.47   109.22   129.37    87.91   100.87 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
  92.550   99.880   47.240   61.235   69.695   65.700   30.065 
[1] "24.8 - 137.1 (92.5)" "2.5 - 152.0 (99.9)"  "1.0 - 100.5 (47.2)" 
[4] "23.5 - 109.2 (61.2)" "16.7 - 129.4 (69.7)" "42.4 - 87.9 (65.7)" 
[7] "23.3 - 100.9 (30.1)"
D6V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       7        7         4        1
  subtype2       7        7         1        1
  subtype3       1        3         1        2
  subtype4       1        2         3        0
  subtype5       1        3         4        2
  subtype6       0        3         1        0
  subtype7       4        0         0        0
D6V3, multiclass
           clus
vv          subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  STAGE I          7        7        1        1        1        0        4
  STAGE II         7        7        3        2        3        3        0
  STAGE III        4        1        1        3        4        1        0
  STAGE IV         1        1        2        0        2        0        0
[1] 4 7
[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       T1 T2 T3+T4
  subtype1  7  7     5
  subtype2  7  7     2
  subtype3  1  3     3
  subtype4  1  2     3
  subtype5  1  3     6
  subtype6  0  3     1
  subtype7  4  0     0
D6V4, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  T1           7        7        1        1        1        0        4
  T2           7        7        3        2        3        3        0
  T3+T4        5        2        3        3        6        1        0
[1] 3 7
[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       N0 N1+N2
  subtype1 12     1
  subtype2 11     0
  subtype3  3     1
  subtype4  5     0
  subtype5  6     3
  subtype6  3     0
  subtype7  0     0
D6V5, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6
  N0          12       11        3        5        6        3
  N1+N2        1        0        1        0        3        0
[1] 2 6
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V6, binary
          cls
clus        0  1
  subtype1 13  0
  subtype2  7  1
  subtype3  4  0
  subtype4  4  0
  subtype5  4  1
  subtype6  2  0
  subtype7  0  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   13    0
  subtype2    7    1
  subtype3    4    0
  subtype4    4    0
  subtype5    4    1
  subtype6    2    0
  subtype7    0    0
   clus
vv  subtype1 subtype2 subtype3 subtype4 subtype5 subtype6
  0       13        7        4        4        4        2
  1        0        1        0        0        1        0
[1] 2 6
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V7, binary
          cls
clus        0  1
  subtype1  9 10
  subtype2 10  6
  subtype3  1  6
  subtype4  0  6
  subtype5  4  6
  subtype6  1  3
  subtype7  2  2
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    9   10
  subtype2   10    6
  subtype3    1    6
  subtype4    0    6
  subtype5    4    6
  subtype6    1    3
  subtype7    2    2
        clus
vv       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  FEMALE        9       10        1        0        4        1        2
  MALE         10        6        6        6        6        3        2
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V8, binary
          cls
clus       0 1
  subtype1 4 0
  subtype2 2 0
  subtype3 0 1
  subtype4 0 1
  subtype5 2 0
  subtype6 0 0
  subtype7 2 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    4    0
  subtype2    2    0
  subtype3    0    1
  subtype4    0    1
  subtype5    2    0
  subtype6    0    0
  subtype7    2    1
     clus
vv    subtype1 subtype2 subtype3 subtype4 subtype5 subtype7
  100        4        2        0        0        2        2
  90         0        0        1        1        0        1
[1] 2 6
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V9, continuous
[1] "Remove cluster labels:" "subtype7"              
clus
subtype1 subtype2 subtype5 subtype7 
       3        4        3        1 
 [1] "subtype2" "subtype2" "subtype5" "subtype1" "subtype1" "subtype5"
 [7] "subtype2" "subtype5" "subtype2" "subtype1"
D6V10, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype5"              
[4] "subtype7"              
clus
subtype1 subtype2 subtype5 subtype7 
       2        3        2        1 
[1] "subtype2" "subtype2" "subtype2"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     1                         0    18
  subtype2     0                         1    14
  subtype3     0                         0     6
  subtype4     0                         1     5
  subtype5     1                         1     8
  subtype6     0                         1     3
  subtype7     0                         0     4
D6V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3 subtype4 subtype5
  ASIAN                            1        0        0        0        1
  BLACK OR AFRICAN AMERICAN        0        1        0        1        1
  WHITE                           18       14        6        5        8
                           clus
vv                          subtype6 subtype7
  ASIAN                            0        0
  BLACK OR AFRICAN AMERICAN        1        0
  WHITE                            3        4
[1] 3 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D6V12, binary
          cls
clus        0  1
  subtype1  0 10
  subtype2  2  9
  subtype3  2  1
  subtype4  0  3
  subtype5  0  3
  subtype6  0  2
  subtype7  0  4
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0   10
  subtype2    2    9
  subtype3    2    1
  subtype4    0    3
  subtype5    0    3
  subtype6    0    2
  subtype7    0    4
                        clus
vv                       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6
  HISPANIC OR LATINO            0        2        2        0        0        0
  NOT HISPANIC OR LATINO       10        9        1        3        3        2
                        clus
vv                       subtype7
  HISPANIC OR LATINO            0
  NOT HISPANIC OR LATINO        4
[1] 2 7
[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, survival
          sevent
clus2       0  1
  subtype1 14  5
  subtype2 22  2
  subtype3 20  2
subtype1 subtype2 subtype3 
      19       24       22 
subtype1 subtype2 subtype3 
       5        2        2 
$subtype1
TCGA-KL-8323 TCGA-KL-8333 TCGA-KL-8334 TCGA-KL-8335 TCGA-KL-8338 TCGA-KL-8339 
       38.07        98.33        96.95        92.55        86.56        28.11 
TCGA-KL-8343 TCGA-KL-8344 TCGA-KM-8438 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8477 
       71.41        28.80       151.96        23.28        28.80        51.06 
TCGA-KM-8639 TCGA-KN-8427 TCGA-KN-8428 TCGA-KN-8432 TCGA-KN-8437 TCGA-KO-8404 
       31.33         0.99        52.27         2.50       100.87        10.68 
TCGA-KO-8405 
      100.47 

$subtype2
TCGA-KL-8324 TCGA-KL-8326 TCGA-KL-8327 TCGA-KL-8328 TCGA-KL-8329 TCGA-KL-8331 
      141.73       109.22       137.06       102.81        90.48        98.96 
TCGA-KL-8336 TCGA-KL-8341 TCGA-KL-8345 TCGA-KM-8476 TCGA-KN-8419 TCGA-KN-8421 
       30.25        24.79        54.74       123.12        23.47        87.91 
TCGA-KN-8424 TCGA-KN-8426 TCGA-KN-8434 TCGA-KN-8435 TCGA-KN-8436 TCGA-KO-8403 
       65.19         3.55        59.08       115.00        42.35       105.47 
TCGA-KO-8406 TCGA-KO-8407 TCGA-KO-8410 TCGA-KO-8411 TCGA-KO-8415 TCGA-KO-8416 
      103.40        92.71       103.10       130.13        96.62        90.48 

$subtype3
TCGA-KL-8325 TCGA-KL-8330 TCGA-KL-8332 TCGA-KL-8337 TCGA-KL-8340 TCGA-KL-8342 
       23.84       108.62        84.89        44.84        66.54        73.91 
TCGA-KL-8346 TCGA-KM-8440 TCGA-KM-8442 TCGA-KM-8443 TCGA-KN-8418 TCGA-KN-8422 
       62.37        44.91        47.24        49.97       129.37        25.48 
TCGA-KN-8423 TCGA-KN-8425 TCGA-KN-8429 TCGA-KN-8431 TCGA-KN-8433 TCGA-KO-8408 
       25.91       104.52        87.12        19.33        11.21        16.67 
TCGA-KO-8409 TCGA-KO-8413 TCGA-KO-8414 TCGA-KO-8417 
      104.55        99.88       105.60        92.55 

subtype1 subtype2 subtype3 
    0.99     3.55    11.21 
subtype1 subtype2 subtype3 
  151.96   141.73   129.37 
subtype1 subtype2 subtype3 
  51.060   94.665   64.455 
[1] "1.0 - 152.0 (51.1)"  "3.5 - 141.7 (94.7)"  "11.2 - 129.4 (64.5)"
D7V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       6        4         7        3
  subtype2       9        9         3        3
  subtype3       6       12         4        0
D7V3, multiclass
           clus
vv          subtype1 subtype2 subtype3
  STAGE I          6        9        6
  STAGE II         4        9       12
  STAGE III        7        3        4
  STAGE IV         3        3        0
[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"
          vv
clus       T1 T2 T3+T4
  subtype1  6  4    10
  subtype2  9  9     6
  subtype3  6 12     4
D7V4, multiclass
       clus
vv      subtype1 subtype2 subtype3
  T1           6        9        6
  T2           4        9       12
  T3+T4       10        6        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"
          vv
clus       N0 N1+N2
  subtype1 10     2
  subtype2 14     2
  subtype3 16     1
D7V5, multiclass
       clus
vv      subtype1 subtype2 subtype3
  N0          10       14       16
  N1+N2        2        2        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"
D7V6, binary
          cls
clus        0  1
  subtype1  8  1
  subtype2 15  1
  subtype3 11  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    8    1
  subtype2   15    1
  subtype3   11    0
   clus
vv  subtype1 subtype2 subtype3
  0        8       15       11
  1        1        1        0
[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"
D7V7, binary
          cls
clus        0  1
  subtype1  6 14
  subtype2 11 13
  subtype3 10 12
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    6   14
  subtype2   11   13
  subtype3   10   12
        clus
vv       subtype1 subtype2 subtype3
  FEMALE        6       11       10
  MALE         14       13       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"
D7V8, binary
          cls
clus       0 1
  subtype1 3 2
  subtype2 3 0
  subtype3 4 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3    2
  subtype2    3    0
  subtype3    4    1
     clus
vv    subtype1 subtype2 subtype3
  100        3        3        4
  90         2        0        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"
D7V9, continuous
D7V10, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype2"              
clus
subtype1 subtype2 subtype3 
       2        2        4 
[1] "subtype3" "subtype3" "subtype3" "subtype3"
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    20
  subtype2     2                         1    21
  subtype3     0                         3    17
D7V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3
  ASIAN                            0        2        0
  BLACK OR AFRICAN AMERICAN        0        1        3
  WHITE                           20       21       17
[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"
D7V12, binary
          cls
clus        0  1
  subtype1  1 11
  subtype2  1 12
  subtype3  2  9
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1   11
  subtype2    1   12
  subtype3    2    9
                        clus
vv                       subtype1 subtype2 subtype3
  HISPANIC OR LATINO            1        1        2
  NOT HISPANIC OR LATINO       11       12        9
[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"

Clustering(8) Variable = MIRSEQ_CHIERARCHICAL
D8V1, survival
          sevent
clus2       0  1
  subtype1 16  2
  subtype2 10  1
  subtype3 11  1
  subtype4  6  0
  subtype5  4  0
  subtype6  3  2
  subtype7  6  3
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
      18       11       12        6        4        5        9 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
       2        1        1        0        0        2        3 
$subtype1
TCGA-KL-8323 TCGA-KL-8329 TCGA-KL-8331 TCGA-KL-8334 TCGA-KL-8336 TCGA-KL-8338 
       38.07        90.48        98.96        96.95        30.25        86.56 
TCGA-KL-8342 TCGA-KL-8345 TCGA-KM-8476 TCGA-KN-8424 TCGA-KN-8434 TCGA-KN-8435 
       73.91        54.74       123.12        65.19        59.08       115.00 
TCGA-KO-8403 TCGA-KO-8406 TCGA-KO-8407 TCGA-KO-8411 TCGA-KO-8415 TCGA-KO-8416 
      105.47       103.40        92.71       130.13        96.62        90.48 

$subtype2
TCGA-KL-8324 TCGA-KL-8327 TCGA-KL-8328 TCGA-KL-8335 TCGA-KL-8341 TCGA-KN-8421 
      141.73       137.06       102.81        92.55        24.79        87.91 
TCGA-KN-8423 TCGA-KN-8426 TCGA-KN-8432 TCGA-KN-8436 TCGA-KO-8410 
       25.91         3.55         2.50        42.35       103.10 

$subtype3
TCGA-KL-8325 TCGA-KL-8330 TCGA-KL-8332 TCGA-KL-8337 TCGA-KL-8340 TCGA-KL-8346 
       23.84       108.62        84.89        44.84        66.54        62.37 
TCGA-KM-8440 TCGA-KM-8443 TCGA-KN-8429 TCGA-KO-8409 TCGA-KO-8413 TCGA-KO-8414 
       44.91        49.97        87.12       104.55        99.88       105.60 

$subtype4
TCGA-KL-8326 TCGA-KM-8477 TCGA-KN-8419 TCGA-KN-8422 TCGA-KN-8425 TCGA-KN-8431 
      109.22        51.06        23.47        25.48       104.52        19.33 

$subtype5
TCGA-KL-8333 TCGA-KM-8442 TCGA-KN-8433 TCGA-KO-8405 
       98.33        47.24        11.21       100.47 

$subtype6
TCGA-KL-8339 TCGA-KM-8438 TCGA-KN-8418 TCGA-KO-8408 TCGA-KO-8417 
       28.11       151.96       129.37        16.67        92.55 

$subtype7
TCGA-KL-8343 TCGA-KL-8344 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8639 TCGA-KN-8427 
       71.41        28.80        23.28        28.80        31.33         0.99 
TCGA-KN-8428 TCGA-KN-8437 TCGA-KO-8404 
       52.27       100.87        10.68 

subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
   30.25     2.50    23.84    19.33    11.21    16.67     0.99 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
  130.13   141.73   108.62   109.22   100.47   151.96   100.87 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 
  91.595   87.910   75.715   38.270   72.785   92.550   28.800 
[1] "30.2 - 130.1 (91.6)" "2.5 - 141.7 (87.9)"  "23.8 - 108.6 (75.7)"
[4] "19.3 - 109.2 (38.3)" "11.2 - 100.5 (72.8)" "16.7 - 152.0 (92.5)"
[7] "1.0 - 100.9 (28.8)" 
D8V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       7        5         5        1
  subtype2       3        5         1        2
  subtype3       2        8         2        0
  subtype4       3        2         1        0
  subtype5       1        2         2        0
  subtype6       1        2         1        1
  subtype7       4        1         2        2
D8V3, multiclass
           clus
vv          subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  STAGE I          7        3        2        3        1        1        4
  STAGE II         5        5        8        2        2        2        1
  STAGE III        5        1        2        1        2        1        2
  STAGE IV         1        2        0        0        0        1        2
[1] 4 7
[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       T1 T2 T3+T4
  subtype1  7  5     6
  subtype2  3  5     3
  subtype3  2  8     2
  subtype4  3  2     1
  subtype5  1  2     2
  subtype6  1  2     2
  subtype7  4  1     4
D8V4, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  T1           7        3        2        3        1        1        4
  T2           5        5        8        2        2        2        1
  T3+T4        6        3        2        1        2        2        4
[1] 3 7
[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       N0 N1+N2
  subtype1 11     1
  subtype2  6     1
  subtype3 10     0
  subtype4  4     0
  subtype5  3     0
  subtype6  3     2
  subtype7  3     1
D8V5, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  N0          11        6       10        4        3        3        3
  N1+N2        1        1        0        0        0        2        1
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V6, binary
          cls
clus        0  1
  subtype1 13  0
  subtype2  6  1
  subtype3  9  0
  subtype4  1  0
  subtype5  2  0
  subtype6  1  1
  subtype7  2  0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   13    0
  subtype2    6    1
  subtype3    9    0
  subtype4    1    0
  subtype5    2    0
  subtype6    1    1
  subtype7    2    0
   clus
vv  subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  0       13        6        9        1        2        1        2
  1        0        1        0        0        0        1        0
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V7, binary
          cls
clus        0  1
  subtype1 10  8
  subtype2  5  6
  subtype3  4  8
  subtype4  2  4
  subtype5  1  4
  subtype6  3  2
  subtype7  2  7
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1   10    8
  subtype2    5    6
  subtype3    4    8
  subtype4    2    4
  subtype5    1    4
  subtype6    3    2
  subtype7    2    7
        clus
vv       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  FEMALE       10        5        4        2        1        3        2
  MALE          8        6        8        4        4        2        7
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V8, binary
          cls
clus       0 1
  subtype1 3 0
  subtype2 0 0
  subtype3 4 0
  subtype4 0 1
  subtype5 0 1
  subtype6 1 0
  subtype7 2 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    3    0
  subtype2    0    0
  subtype3    4    0
  subtype4    0    1
  subtype5    0    1
  subtype6    1    0
  subtype7    2    1
     clus
vv    subtype1 subtype3 subtype4 subtype5 subtype6 subtype7
  100        3        4        0        0        1        2
  90         0        0        1        1        0        1
[1] 2 6
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V9, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype3"              
[4] "subtype5"               "subtype6"               "subtype7"              
clus
subtype1 subtype2 subtype3 subtype5 subtype6 subtype7 
       2        3        2        1        1        2 
[1] "subtype2" "subtype2" "subtype2"
D8V10, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype2"              
[4] "subtype3"               "subtype5"               "subtype6"              
[7] "subtype7"              
clus
subtype1 subtype2 subtype3 subtype5 subtype6 subtype7 
       1        2        2        1        1        1 
character(0)
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     1                         0    17
  subtype2     1                         0    10
  subtype3     0                         3     9
  subtype4     0                         1     4
  subtype5     0                         0     4
  subtype6     0                         0     5
  subtype7     0                         0     9
D8V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3 subtype4 subtype5
  ASIAN                            1        1        0        0        0
  BLACK OR AFRICAN AMERICAN        0        0        3        1        0
  WHITE                           17       10        9        4        4
                           clus
vv                          subtype6 subtype7
  ASIAN                            0        0
  BLACK OR AFRICAN AMERICAN        0        0
  WHITE                            5        9
[1] 3 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D8V12, binary
          cls
clus       0 1
  subtype1 1 8
  subtype2 0 5
  subtype3 1 5
  subtype4 0 2
  subtype5 2 2
  subtype6 0 3
  subtype7 0 7
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1    8
  subtype2    0    5
  subtype3    1    5
  subtype4    0    2
  subtype5    2    2
  subtype6    0    3
  subtype7    0    7
                        clus
vv                       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6
  HISPANIC OR LATINO            1        0        1        0        2        0
  NOT HISPANIC OR LATINO        8        5        5        2        2        3
                        clus
vv                       subtype7
  HISPANIC OR LATINO            0
  NOT HISPANIC OR LATINO        7
[1] 2 7
[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, survival
          sevent
clus2       0  1
  subtype1 12  6
  subtype2 27  2
subtype1 subtype2 
      18       29 
subtype1 subtype2 
       6        2 
$subtype1
TCGA-KL-8323 TCGA-KL-8333 TCGA-KL-8334 TCGA-KL-8335 TCGA-KL-8336 TCGA-KL-8338 
       38.07        98.33        96.95        92.55        30.25        86.56 
TCGA-KL-8343 TCGA-KL-8344 TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8639 TCGA-KN-8427 
       71.41        28.80        23.28        28.80        31.33         0.99 
TCGA-KN-8428 TCGA-KN-8433 TCGA-KN-8437 TCGA-KO-8404 TCGA-KO-8405 TCGA-KO-8408 
       52.27        11.21       100.87        10.68       100.47        16.67 

$subtype2
TCGA-KL-8324 TCGA-KL-8327 TCGA-KL-8328 TCGA-KL-8331 TCGA-KL-8332 TCGA-KL-8339 
      141.73       137.06       102.81        98.96        84.89        28.11 
TCGA-KL-8340 TCGA-KL-8341 TCGA-KL-8342 TCGA-KL-8345 TCGA-KM-8438 TCGA-KM-8442 
       66.54        24.79        73.91        54.74       151.96        47.24 
TCGA-KN-8418 TCGA-KN-8419 TCGA-KN-8422 TCGA-KN-8423 TCGA-KN-8424 TCGA-KN-8426 
      129.37        23.47        25.48        25.91        65.19         3.55 
TCGA-KN-8429 TCGA-KN-8431 TCGA-KN-8432 TCGA-KN-8434 TCGA-KN-8436 TCGA-KO-8409 
       87.12        19.33         2.50        59.08        42.35       104.55 
TCGA-KO-8410 TCGA-KO-8413 TCGA-KO-8415 TCGA-KO-8416 TCGA-KO-8417 
      103.10        99.88        96.62        90.48        92.55 

subtype1 subtype2 
    0.99     2.50 
subtype1 subtype2 
  100.87   151.96 
subtype1 subtype2 
   34.70    73.91 
[1] "1.0 - 100.9 (34.7)" "2.5 - 152.0 (73.9)"
[1] "hr=" "5.9"
D9V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       5        2         9        3
  subtype2       9       14         3        3
D9V3, multiclass
           clus
vv          subtype1 subtype2
  STAGE I          5        9
  STAGE II         2       14
  STAGE III        9        3
  STAGE IV         3        3
[1] 4 2
[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       T1 T2 T3+T4
  subtype1  5  2    12
  subtype2  9 14     6
D9V4, multiclass
       clus
vv      subtype1 subtype2
  T1           5        9
  T2           2       14
  T3+T4       12        6
[1] 3 2
[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       N0 N1+N2
  subtype1  9     3
  subtype2 20     2
D9V5, multiclass
       clus
vv      subtype1 subtype2
  N0           9       20
  N1+N2        3        2
[1] 2 2
[1] FALSE
D9V6, binary
          cls
clus        0  1
  subtype1  9  0
  subtype2 15  2
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    9    0
  subtype2   15    2
   clus
vv  subtype1 subtype2
  0        9       15
  1        0        2
[1] 2 2
[1] FALSE
D9V7, binary
          cls
clus        0  1
  subtype1  6 13
  subtype2 15 14
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    6   13
  subtype2   15   14
        clus
vv       subtype1 subtype2
  FEMALE        6       15
  MALE         13       14
[1] 2 2
[1] FALSE
D9V8, binary
          cls
clus       0 1
  subtype1 2 1
  subtype2 4 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    2    1
  subtype2    4    1
     clus
vv    subtype1 subtype2
  100        2        4
  90         1        1
[1] 2 2
[1] FALSE
D9V9, continuous
D9V10, continuous
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    19
  subtype2     1                         1    25
D9V11, multiclass
                           clus
vv                          subtype1 subtype2
  ASIAN                            0        1
  BLACK OR AFRICAN AMERICAN        0        1
  WHITE                           19       25
[1] 3 2
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D9V12, binary
          cls
clus        0  1
  subtype1  1 10
  subtype2  3 10
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1   10
  subtype2    3   10
                        clus
vv                       subtype1 subtype2
  HISPANIC OR LATINO            1        3
  NOT HISPANIC OR LATINO       10       10
[1] 2 2
[1] FALSE

Clustering(10) Variable = MIRSEQ_MATURE_CHIERARCHICAL
D10V1, survival
          sevent
clus2      0 1
  subtype1 8 2
  subtype2 6 0
  subtype3 4 1
  subtype4 6 1
  subtype5 4 0
  subtype6 4 2
  subtype7 3 2
  subtype8 4 0
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8 
      10        6        5        7        4        6        5        4 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8 
       2        0        1        1        0        2        2        0 
$subtype1
TCGA-KL-8323 TCGA-KL-8331 TCGA-KL-8334 TCGA-KL-8336 TCGA-KL-8338 TCGA-KN-8424 
       38.07        98.96        96.95        30.25        86.56        65.19 
TCGA-KN-8426 TCGA-KO-8410 TCGA-KO-8415 TCGA-KO-8416 
        3.55       103.10        96.62        90.48 

$subtype2
TCGA-KL-8324 TCGA-KN-8419 TCGA-KN-8422 TCGA-KN-8423 TCGA-KO-8409 TCGA-KO-8413 
      141.73        23.47        25.48        25.91       104.55        99.88 

$subtype3
TCGA-KL-8327 TCGA-KL-8341 TCGA-KL-8342 TCGA-KL-8345 TCGA-KN-8436 
      137.06        24.79        73.91        54.74        42.35 

$subtype4
TCGA-KL-8328 TCGA-KL-8339 TCGA-KM-8438 TCGA-KM-8442 TCGA-KN-8418 TCGA-KN-8432 
      102.81        28.11       151.96        47.24       129.37         2.50 
TCGA-KO-8417 
       92.55 

$subtype5
TCGA-KL-8332 TCGA-KL-8340 TCGA-KN-8429 TCGA-KN-8434 
       84.89        66.54        87.12        59.08 

$subtype6
TCGA-KL-8333 TCGA-KL-8335 TCGA-KN-8427 TCGA-KN-8428 TCGA-KO-8404 TCGA-KO-8405 
       98.33        92.55         0.99        52.27        10.68       100.47 

$subtype7
TCGA-KL-8343 TCGA-KL-8344 TCGA-KN-8431 TCGA-KN-8433 TCGA-KO-8408 
       71.41        28.80        19.33        11.21        16.67 

$subtype8
TCGA-KM-8439 TCGA-KM-8441 TCGA-KM-8639 TCGA-KN-8437 
       23.28        28.80        31.33       100.87 

subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8 
    3.55    23.47    24.79     2.50    59.08     0.99    11.21    23.28 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8 
  103.10   141.73   137.06   151.96    87.12   100.47    71.41   100.87 
subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8 
  88.520   62.895   54.740   92.550   75.715   72.410   19.330   30.065 
[1] "3.5 - 103.1 (88.5)"  "23.5 - 141.7 (62.9)" "24.8 - 137.1 (54.7)"
[4] "2.5 - 152.0 (92.5)"  "59.1 - 87.1 (75.7)"  "1.0 - 100.5 (72.4)" 
[7] "11.2 - 71.4 (19.3)"  "23.3 - 100.9 (30.1)"
D10V2, continuous
          vv
clus       STAGE I STAGE II STAGE III STAGE IV
  subtype1       3        1         4        2
  subtype2       3        3         0        0
  subtype3       1        2         1        1
  subtype4       2        5         0        1
  subtype5       1        2         1        0
  subtype6       0        2         2        2
  subtype7       0        1         4        0
  subtype8       4        0         0        0
D10V3, multiclass
           clus
vv          subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  STAGE I          3        3        1        2        1        0        0
  STAGE II         1        3        2        5        2        2        1
  STAGE III        4        0        1        0        1        2        4
  STAGE IV         2        0        1        1        0        2        0
           clus
vv          subtype8
  STAGE I          4
  STAGE II         0
  STAGE III        0
  STAGE IV         0
[1] 4 8
[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       T1 T2 T3+T4
  subtype1  3  1     6
  subtype2  3  3     0
  subtype3  1  2     2
  subtype4  2  5     1
  subtype5  1  2     1
  subtype6  0  2     4
  subtype7  0  1     4
  subtype8  4  0     0
D10V4, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7 subtype8
  T1           3        3        1        2        1        0        0        4
  T2           1        3        2        5        2        2        1        0
  T3+T4        6        0        2        1        1        4        4        0
[1] 3 8
[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       N0 N1+N2
  subtype1  7     1
  subtype2  5     0
  subtype3  3     1
  subtype4  5     1
  subtype5  3     0
  subtype6  4     1
  subtype7  2     1
  subtype8  0     0
D10V5, multiclass
       clus
vv      subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  N0           7        5        3        5        3        4        2
  N1+N2        1        0        1        1        0        1        1
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V6, binary
          cls
clus       0 1
  subtype1 8 1
  subtype2 3 0
  subtype3 4 0
  subtype4 2 1
  subtype5 2 0
  subtype6 3 0
  subtype7 2 0
  subtype8 0 0
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    8    1
  subtype2    3    0
  subtype3    4    0
  subtype4    2    1
  subtype5    2    0
  subtype6    3    0
  subtype7    2    0
  subtype8    0    0
   clus
vv  subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  0        8        3        4        2        2        3        2
  1        1        0        0        1        0        0        0
[1] 2 7
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V7, binary
          cls
clus       0 1
  subtype1 7 3
  subtype2 2 4
  subtype3 2 3
  subtype4 4 4
  subtype5 2 2
  subtype6 0 6
  subtype7 2 3
  subtype8 2 2
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    7    3
  subtype2    2    4
  subtype3    2    3
  subtype4    4    4
  subtype5    2    2
  subtype6    0    6
  subtype7    2    3
  subtype8    2    2
        clus
vv       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6 subtype7
  FEMALE        7        2        2        4        2        0        2
  MALE          3        4        3        4        2        6        3
        clus
vv       subtype8
  FEMALE        2
  MALE          2
[1] 2 8
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V8, binary
          cls
clus       0 1
  subtype1 0 0
  subtype2 1 0
  subtype3 0 0
  subtype4 1 1
  subtype5 2 0
  subtype6 0 0
  subtype7 0 0
  subtype8 2 1
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    0    0
  subtype2    1    0
  subtype3    0    0
  subtype4    1    1
  subtype5    2    0
  subtype6    0    0
  subtype7    0    0
  subtype8    2    1
     clus
vv    subtype2 subtype4 subtype5 subtype8
  100        1        1        2        2
  90         0        1        0        1
[1] 2 4
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V9, continuous
[1] "Remove cluster labels:" "subtype1"               "subtype2"              
[4] "subtype3"               "subtype6"               "subtype7"              
[7] "subtype8"              
clus
subtype1 subtype2 subtype3 subtype6 subtype7 subtype8 
       1        2        2        2        2        1 
character(0)
D10V10, continuous
[1] "Remove cluster labels:" "subtype2"               "subtype3"              
[4] "subtype6"               "subtype7"               "subtype8"              
clus
subtype2 subtype3 subtype6 subtype7 subtype8 
       1        2        1        2        1 
character(0)
          vv
clus       ASIAN BLACK OR AFRICAN AMERICAN WHITE
  subtype1     0                         0    10
  subtype2     0                         1     5
  subtype3     1                         0     4
  subtype4     0                         0     7
  subtype5     0                         0     4
  subtype6     0                         0     6
  subtype7     0                         0     4
  subtype8     0                         0     4
D10V11, multiclass
                           clus
vv                          subtype1 subtype2 subtype3 subtype4 subtype5
  ASIAN                            0        0        1        0        0
  BLACK OR AFRICAN AMERICAN        0        1        0        0        0
  WHITE                           10        5        4        7        4
                           clus
vv                          subtype6 subtype7 subtype8
  ASIAN                            0        0        0
  BLACK OR AFRICAN AMERICAN        0        0        0
  WHITE                            6        4        4
[1] 3 8
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
D10V12, binary
          cls
clus       0 1
  subtype1 1 2
  subtype2 1 3
  subtype3 0 1
  subtype4 1 4
  subtype5 0 1
  subtype6 1 2
  subtype7 0 3
  subtype8 0 4
[1] "tbl2"
          cls
clus       [,1] [,2]
  subtype1    1    2
  subtype2    1    3
  subtype3    0    1
  subtype4    1    4
  subtype5    0    1
  subtype6    1    2
  subtype7    0    3
  subtype8    0    4
                        clus
vv                       subtype1 subtype2 subtype3 subtype4 subtype5 subtype6
  HISPANIC OR LATINO            1        1        0        1        0        1
  NOT HISPANIC OR LATINO        2        3        1        4        1        2
                        clus
vv                       subtype7 subtype8
  HISPANIC OR LATINO            0        0
  NOT HISPANIC OR LATINO        3        4
[1] 2 8
[1] TRUE
[1] "dimension of contingency table is larger than 2 by 2 --- so will do fisher test with simulate.p.value=TRUE"
