Correlation between aggregated molecular cancer subtypes and selected clinical features
Colorectal Adenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1QV3KC9
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 13 clinical features across 617 patients, 30 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE',  'PATHOLOGY.N.STAGE',  'HISTOLOGICAL.TYPE', and 'NUMBER.OF.LYMPH.NODES'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGY.M.STAGE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.M.STAGE' and 'NUMBER.OF.LYMPH.NODES'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'AGE',  'PRIMARY.SITE.OF.DISEASE',  'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE',  'PATHOLOGY.M.STAGE',  'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.M.STAGE' and 'COMPLETENESS.OF.RESECTION'.

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 30 significant findings detected.

Clinical
Features
Statistical
Tests
mRNA
CNMF
subtypes
mRNA
cHierClus
subtypes
Copy
Number
Ratio
CNMF
subtypes
METHLYATION
CNMF
RPPA
CNMF
subtypes
RPPA
cHierClus
subtypes
RNAseq
CNMF
subtypes
RNAseq
cHierClus
subtypes
MIRSEQ
CNMF
MIRSEQ
CHIERARCHICAL
MIRseq
Mature
CNMF
subtypes
MIRseq
Mature
cHierClus
subtypes
Time to Death logrank test 0.2
(1.00)
0.223
(1.00)
0.681
(1.00)
0.945
(1.00)
0.433
(1.00)
0.236
(1.00)
0.065
(1.00)
0.594
(1.00)
0.0499
(1.00)
0.678
(1.00)
0.588
(1.00)
0.557
(1.00)
AGE Kruskal-Wallis (anova) 0.434
(1.00)
0.0483
(1.00)
0.324
(1.00)
0.00707
(0.842)
0.955
(1.00)
0.65
(1.00)
0.00232
(0.292)
0.000335
(0.0432)
0.00875
(1.00)
0.00428
(0.526)
0.201
(1.00)
0.516
(1.00)
PRIMARY SITE OF DISEASE Fisher's exact test 0.0242
(1.00)
0.00285
(0.353)
3e-05
(0.0042)
0.00031
(0.0403)
0.828
(1.00)
0.77
(1.00)
0.0304
(1.00)
0.00016
(0.0214)
0.04
(1.00)
0.262
(1.00)
1e-05
(0.00156)
1e-05
(0.00156)
NEOPLASM DISEASESTAGE Fisher's exact test 0.00542
(0.656)
0.0505
(1.00)
0.00451
(0.55)
0.799
(1.00)
0.0984
(1.00)
0.0296
(1.00)
0.00252
(0.315)
1e-05
(0.00156)
0.00012
(0.0162)
0.0103
(1.00)
0.196
(1.00)
0.77
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.0561
(1.00)
0.062
(1.00)
0.606
(1.00)
0.755
(1.00)
0.335
(1.00)
0.0432
(1.00)
0.0444
(1.00)
0.293
(1.00)
0.106
(1.00)
0.919
(1.00)
0.143
(1.00)
0.441
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.0157
(1.00)
0.459
(1.00)
7e-05
(0.00966)
0.323
(1.00)
0.0713
(1.00)
0.0167
(1.00)
0.134
(1.00)
0.0106
(1.00)
0.014
(1.00)
0.123
(1.00)
0.183
(1.00)
0.788
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.00915
(1.00)
0.0579
(1.00)
0.031
(1.00)
0.884
(1.00)
0.00105
(0.134)
0.0001
(0.0136)
1e-05
(0.00156)
1e-05
(0.00156)
1e-05
(0.00156)
1e-05
(0.00156)
0.127
(1.00)
0.491
(1.00)
GENDER Fisher's exact test 0.061
(1.00)
0.19
(1.00)
0.322
(1.00)
0.0789
(1.00)
0.717
(1.00)
0.325
(1.00)
0.492
(1.00)
0.151
(1.00)
0.54
(1.00)
0.517
(1.00)
0.857
(1.00)
0.326
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 1e-05
(0.00156)
1e-05
(0.00156)
1e-05
(0.00156)
0.00022
(0.0293)
0.18
(1.00)
0.207
(1.00)
3e-05
(0.0042)
1e-05
(0.00156)
0.00024
(0.0317)
0.341
(1.00)
1e-05
(0.00156)
1e-05
(0.00156)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.396
(1.00)
0.654
(1.00)
0.84
(1.00)
0.0623
(1.00)
0.731
(1.00)
0.0564
(1.00)
0.428
(1.00)
0.0277
(1.00)
0.0952
(1.00)
0.398
(1.00)
0.881
(1.00)
0.144
(1.00)
COMPLETENESS OF RESECTION Fisher's exact test 0.0186
(1.00)
0.0105
(1.00)
0.0175
(1.00)
0.219
(1.00)
0.0172
(1.00)
0.0148
(1.00)
1e-05
(0.00156)
1e-05
(0.00156)
1e-05
(0.00156)
7e-05
(0.00966)
0.533
(1.00)
0.355
(1.00)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.0127
(1.00)
0.174
(1.00)
0.00127
(0.162)
0.681
(1.00)
0.0256
(1.00)
0.000274
(0.0359)
0.0693
(1.00)
0.00627
(0.753)
0.0488
(1.00)
0.127
(1.00)
0.0318
(1.00)
0.348
(1.00)
RACE Fisher's exact test 0.134
(1.00)
0.168
(1.00)
0.797
(1.00)
0.756
(1.00)
0.0465
(1.00)
0.76
(1.00)
0.699
(1.00)
0.425
(1.00)
0.659
(1.00)
0.792
(1.00)
0.218
(1.00)
0.793
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1.  Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 46 62 72 42
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.2 (logrank test), Q value = 1

Table S2.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 203 36 0.9 - 54.0 (21.0)
subtype1 40 8 0.9 - 46.6 (22.7)
subtype2 58 15 1.0 - 52.0 (20.0)
subtype3 66 8 0.9 - 50.0 (20.5)
subtype4 39 5 1.0 - 54.0 (21.0)

Figure S1.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.434 (Kruskal-Wallis (anova)), Q value = 1

Table S3.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 222 69.5 (11.4)
subtype1 46 70.8 (11.8)
subtype2 62 68.5 (8.9)
subtype3 72 69.5 (12.4)
subtype4 42 69.6 (12.6)

Figure S2.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0242 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 152 68
subtype1 39 7
subtype2 37 25
subtype3 45 25
subtype4 31 11

Figure S3.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00542 (Fisher's exact test), Q value = 0.66

Table S5.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 47 15 65 5 10 3 22 20 33 1
subtype1 8 3 15 3 3 1 4 2 6 0
subtype2 9 2 14 0 2 2 5 11 17 0
subtype3 19 4 23 0 4 0 7 7 8 0
subtype4 11 6 13 2 1 0 6 0 2 1

Figure S4.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0561 (Fisher's exact test), Q value = 1

Table S6.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 9 46 148 19
subtype1 1 9 30 6
subtype2 1 12 41 8
subtype3 5 16 51 0
subtype4 2 9 26 5

Figure S5.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0157 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 136 43 43
subtype1 30 6 10
subtype2 28 17 17
subtype3 46 12 14
subtype4 32 8 2

Figure S6.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00915 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A
ALL 186 33 1
subtype1 39 6 0
subtype2 45 17 0
subtype3 63 8 0
subtype4 39 2 1

Figure S7.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.061 (Fisher's exact test), Q value = 1

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 106 116
subtype1 28 18
subtype2 33 29
subtype3 30 42
subtype4 15 27

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S10.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 129 22 58 7
subtype1 28 11 4 2
subtype2 36 1 25 0
subtype3 44 1 21 2
subtype4 21 9 8 3

Figure S9.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.396 (Fisher's exact test), Q value = 1

Table S11.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 221
subtype1 1 45
subtype2 0 62
subtype3 0 72
subtype4 0 42

Figure S10.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0186 (Fisher's exact test), Q value = 1

Table S12.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 185 2 29
subtype1 38 0 4
subtype2 45 2 15
subtype3 62 0 8
subtype4 40 0 2

Figure S11.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'mRNA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0127 (Kruskal-Wallis (anova)), Q value = 1

Table S13.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 221 2.2 (4.7)
subtype1 46 3.3 (7.0)
subtype2 62 2.9 (5.4)
subtype3 72 1.6 (2.7)
subtype4 41 0.8 (2.2)

Figure S12.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.134 (Fisher's exact test), Q value = 1

Table S14.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15
subtype1 0 7
subtype2 2 3
subtype3 0 4
subtype4 0 1

Figure S13.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S15.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 52 41 77 52
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.223 (logrank test), Q value = 1

Table S16.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 203 36 0.9 - 54.0 (21.0)
subtype1 46 9 0.9 - 53.0 (22.5)
subtype2 39 11 1.0 - 48.0 (17.8)
subtype3 71 11 1.0 - 54.0 (20.0)
subtype4 47 5 0.9 - 50.0 (21.0)

Figure S14.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0483 (Kruskal-Wallis (anova)), Q value = 1

Table S17.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 222 69.5 (11.4)
subtype1 52 72.9 (11.6)
subtype2 41 67.7 (9.6)
subtype3 77 68.1 (11.7)
subtype4 52 69.5 (11.6)

Figure S15.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.00285 (Fisher's exact test), Q value = 0.35

Table S18.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 152 68
subtype1 46 6
subtype2 24 17
subtype3 48 28
subtype4 34 17

Figure S16.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0505 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 47 15 65 5 10 3 22 20 33 1
subtype1 9 3 17 4 3 1 6 2 5 1
subtype2 4 2 10 1 0 1 4 7 12 0
subtype3 20 8 23 0 6 1 5 5 9 0
subtype4 14 2 15 0 1 0 7 6 7 0

Figure S17.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.062 (Fisher's exact test), Q value = 1

Table S20.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 9 46 148 19
subtype1 1 10 33 8
subtype2 0 7 28 6
subtype3 7 15 51 4
subtype4 1 14 36 1

Figure S18.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.459 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 136 43 43
subtype1 34 9 9
subtype2 19 11 11
subtype3 52 12 13
subtype4 31 11 10

Figure S19.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0579 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A
ALL 186 33 1
subtype1 45 5 1
subtype2 29 12 0
subtype3 68 9 0
subtype4 44 7 0

Figure S20.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.19 (Fisher's exact test), Q value = 1

Table S23.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 106 116
subtype1 30 22
subtype2 22 19
subtype3 34 43
subtype4 20 32

Figure S21.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S24.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 129 22 58 7
subtype1 27 18 2 3
subtype2 24 0 17 0
subtype3 44 4 23 3
subtype4 34 0 16 1

Figure S22.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.654 (Fisher's exact test), Q value = 1

Table S25.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 1 221
subtype1 1 51
subtype2 0 41
subtype3 0 77
subtype4 0 52

Figure S23.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0105 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 185 2 29
subtype1 45 0 3
subtype2 28 2 11
subtype3 69 0 8
subtype4 43 0 7

Figure S24.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.174 (Kruskal-Wallis (anova)), Q value = 1

Table S27.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 221 2.2 (4.7)
subtype1 52 2.8 (6.4)
subtype2 41 3.3 (6.4)
subtype3 76 1.5 (2.9)
subtype4 52 1.6 (2.6)

Figure S25.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.168 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15
subtype1 0 6
subtype2 2 3
subtype3 0 5
subtype4 0 1

Figure S26.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S29.  Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 250 203 151
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.681 (logrank test), Q value = 1

Table S30.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 575 109 0.1 - 140.4 (17.8)
subtype1 238 49 0.1 - 140.4 (17.9)
subtype2 193 31 0.1 - 131.5 (17.0)
subtype3 144 29 0.2 - 130.7 (19.0)

Figure S27.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.324 (Kruskal-Wallis (anova)), Q value = 1

Table S31.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 602 66.3 (12.7)
subtype1 248 66.9 (14.1)
subtype2 203 66.0 (11.2)
subtype3 151 65.7 (12.4)

Figure S28.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0042

Table S32.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 438 162
subtype1 207 43
subtype2 128 71
subtype3 103 48

Figure S29.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00451 (Fisher's exact test), Q value = 0.55

Table S33.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 102 1 36 171 10 3 26 21 78 53 61 27 2
subtype1 52 1 18 82 6 0 11 7 26 18 15 7 1
subtype2 32 0 5 54 3 1 9 7 33 14 26 13 1
subtype3 18 0 13 35 1 2 6 7 19 21 20 7 0

Figure S30.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.606 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 412 65
subtype1 10 49 162 28
subtype2 8 32 141 21
subtype3 2 24 109 16

Figure S31.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.0097

Table S35.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 341 148 112
subtype1 166 48 36
subtype2 102 65 34
subtype3 73 35 42

Figure S32.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.031 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 446 74 10 2 63
subtype1 196 18 2 1 29
subtype2 143 34 4 1 17
subtype3 107 22 4 0 17

Figure S33.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.322 (Fisher's exact test), Q value = 1

Table S37.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 283 321
subtype1 125 125
subtype2 87 116
subtype3 71 80

Figure S34.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S38.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 378 59 146 13
subtype1 160 46 33 9
subtype2 123 5 71 0
subtype3 95 8 42 4

Figure S35.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.84 (Fisher's exact test), Q value = 1

Table S39.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 595
subtype1 3 247
subtype2 3 200
subtype3 3 148

Figure S36.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0175 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 436 5 36 29
subtype1 183 3 8 17
subtype2 152 0 13 6
subtype3 101 2 15 6

Figure S37.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00127 (Kruskal-Wallis (anova)), Q value = 0.16

Table S41.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 570 2.2 (4.7)
subtype1 234 1.8 (4.2)
subtype2 193 2.0 (3.4)
subtype3 143 3.3 (6.5)

Figure S38.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.797 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 56 292
subtype1 1 7 23 127
subtype2 0 2 21 93
subtype3 0 3 12 72

Figure S39.  Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #4: 'METHLYATION CNMF'

Table S43.  Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 98 158 120
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.945 (logrank test), Q value = 1

Table S44.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 367 72 0.1 - 140.4 (16.2)
subtype1 97 16 0.1 - 102.4 (15.0)
subtype2 155 33 0.1 - 139.2 (17.0)
subtype3 115 23 0.1 - 140.4 (17.1)

Figure S40.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00707 (Kruskal-Wallis (anova)), Q value = 0.84

Table S45.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 375 64.5 (12.9)
subtype1 97 61.2 (13.1)
subtype2 158 64.7 (12.5)
subtype3 120 66.8 (12.9)

Figure S41.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.00031 (Fisher's exact test), Q value = 0.04

Table S46.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 278 96
subtype1 68 28
subtype2 106 52
subtype3 104 16

Figure S42.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.799 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 52 1 20 109 7 3 11 18 56 34 26 25 1
subtype1 13 0 5 29 1 0 1 7 13 11 6 7 0
subtype2 18 0 9 42 2 2 5 9 28 13 12 12 0
subtype3 21 1 6 38 4 1 5 2 15 10 8 6 1

Figure S43.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.755 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 55 262 46
subtype1 3 11 68 15
subtype2 5 22 114 17
subtype3 3 22 80 14

Figure S44.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.323 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 206 100 66
subtype1 52 28 17
subtype2 80 48 27
subtype3 74 24 22

Figure S45.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.884 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 260 38 9 1 61
subtype1 70 9 2 0 14
subtype2 105 19 5 0 28
subtype3 85 10 2 1 19

Figure S46.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0789 (Fisher's exact test), Q value = 1

Table S51.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 170 206
subtype1 38 60
subtype2 68 90
subtype3 64 56

Figure S47.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00022 (Fisher's exact test), Q value = 0.029

Table S52.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 244 34 90 6
subtype1 56 12 27 1
subtype2 100 6 49 3
subtype3 88 16 14 2

Figure S48.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0623 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 368
subtype1 4 94
subtype2 4 154
subtype3 0 120

Figure S49.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.219 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 245 4 6 28
subtype1 67 1 0 4
subtype2 103 1 2 11
subtype3 75 2 4 13

Figure S50.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.681 (Kruskal-Wallis (anova)), Q value = 1

Table S55.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 342 2.2 (4.8)
subtype1 89 2.6 (5.1)
subtype2 145 2.3 (5.3)
subtype3 108 1.9 (3.7)

Figure S51.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.756 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 48 282
subtype1 1 4 11 78
subtype2 0 4 19 118
subtype3 0 4 18 86

Figure S52.  Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'RACE'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S57.  Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 100 179 42 140
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.433 (logrank test), Q value = 1

Table S58.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 439 87 0.1 - 140.4 (18.2)
subtype1 92 19 0.2 - 117.1 (17.1)
subtype2 168 39 0.1 - 140.4 (18.3)
subtype3 42 5 0.1 - 62.0 (19.0)
subtype4 137 24 0.1 - 135.7 (19.7)

Figure S53.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.955 (Kruskal-Wallis (anova)), Q value = 1

Table S59.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 460 66.8 (12.6)
subtype1 100 67.0 (12.7)
subtype2 178 67.1 (12.5)
subtype3 42 67.4 (10.6)
subtype4 140 66.1 (13.5)

Figure S54.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.828 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 331 130
subtype1 70 30
subtype2 126 53
subtype3 32 10
subtype4 103 37

Figure S55.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0984 (Fisher's exact test), Q value = 1

Table S61.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 70 1 27 139 9 2 24 16 62 39 44 19 1
subtype1 10 0 8 26 3 0 3 4 18 10 10 7 0
subtype2 27 0 11 44 4 1 11 7 27 10 26 7 1
subtype3 8 1 3 17 0 0 2 0 3 5 1 1 0
subtype4 25 0 5 52 2 1 8 5 14 14 7 4 0

Figure S56.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.335 (Fisher's exact test), Q value = 1

Table S62.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 76 324 48
subtype1 0 13 72 14
subtype2 6 31 123 19
subtype3 0 8 32 1
subtype4 5 24 97 14

Figure S57.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0713 (Fisher's exact test), Q value = 1

Table S63.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 260 117 81
subtype1 47 33 19
subtype2 95 50 34
subtype3 29 6 6
subtype4 89 28 22

Figure S58.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00105 (Fisher's exact test), Q value = 0.13

Table S64.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 357 54 8 1 36
subtype1 78 12 5 0 3
subtype2 130 32 0 1 15
subtype3 37 2 0 0 3
subtype4 112 8 3 0 15

Figure S59.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.717 (Fisher's exact test), Q value = 1

Table S65.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 215 246
subtype1 42 58
subtype2 88 91
subtype3 19 23
subtype4 66 74

Figure S60.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.18 (Fisher's exact test), Q value = 1

Table S66.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 293 38 117 10
subtype1 61 9 24 4
subtype2 119 7 48 4
subtype3 27 5 9 1
subtype4 86 17 36 1

Figure S61.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.731 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 453
subtype1 3 97
subtype2 3 176
subtype3 0 42
subtype4 2 138

Figure S62.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0172 (Fisher's exact test), Q value = 1

Table S68.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 338 2 28 19
subtype1 74 0 7 7
subtype2 114 1 18 6
subtype3 38 0 1 1
subtype4 112 1 2 5

Figure S63.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0256 (Kruskal-Wallis (anova)), Q value = 1

Table S69.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 435 2.1 (4.6)
subtype1 93 2.3 (3.8)
subtype2 169 2.3 (4.4)
subtype3 41 1.5 (3.1)
subtype4 132 2.0 (5.6)

Figure S64.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0465 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 24 246
subtype1 0 6 8 45
subtype2 0 5 8 88
subtype3 0 0 2 12
subtype4 1 1 6 101

Figure S65.  Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S71.  Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 54 307 100
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.236 (logrank test), Q value = 1

Table S72.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 439 87 0.1 - 140.4 (18.2)
subtype1 53 8 0.1 - 130.7 (17.0)
subtype2 288 65 0.1 - 140.4 (18.1)
subtype3 98 14 0.1 - 135.7 (19.8)

Figure S66.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.65 (Kruskal-Wallis (anova)), Q value = 1

Table S73.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 460 66.8 (12.6)
subtype1 54 66.5 (11.9)
subtype2 306 67.1 (12.6)
subtype3 100 65.8 (13.2)

Figure S67.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.77 (Fisher's exact test), Q value = 1

Table S74.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 331 130
subtype1 39 15
subtype2 223 84
subtype3 69 31

Figure S68.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0296 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 70 1 27 139 9 2 24 16 62 39 44 19 1
subtype1 6 0 1 15 1 0 1 3 10 5 4 8 0
subtype2 40 1 22 90 7 1 19 8 41 27 36 8 1
subtype3 24 0 4 34 1 1 4 5 11 7 4 3 0

Figure S69.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0432 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 11 76 324 48
subtype1 0 7 36 11
subtype2 7 45 223 30
subtype3 4 24 65 7

Figure S70.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0167 (Fisher's exact test), Q value = 1

Table S77.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 260 117 81
subtype1 23 17 14
subtype2 168 82 55
subtype3 69 18 12

Figure S71.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 1e-04 (Fisher's exact test), Q value = 0.014

Table S78.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 357 54 8 1 36
subtype1 37 7 5 0 4
subtype2 239 43 0 1 21
subtype3 81 4 3 0 11

Figure S72.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.325 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 215 246
subtype1 26 28
subtype2 149 158
subtype3 40 60

Figure S73.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.207 (Fisher's exact test), Q value = 1

Table S80.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 293 38 117 10
subtype1 33 6 13 2
subtype2 202 21 73 8
subtype3 58 11 31 0

Figure S74.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0564 (Fisher's exact test), Q value = 1

Table S81.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 8 453
subtype1 3 51
subtype2 3 304
subtype3 2 98

Figure S75.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0148 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 338 2 28 19
subtype1 44 1 1 1
subtype2 212 1 26 14
subtype3 82 0 1 4

Figure S76.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.000274 (Kruskal-Wallis (anova)), Q value = 0.036

Table S83.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 435 2.1 (4.6)
subtype1 51 3.9 (8.1)
subtype2 290 2.0 (3.9)
subtype3 94 1.3 (3.6)

Figure S77.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.76 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 24 246
subtype1 0 2 3 39
subtype2 1 9 15 137
subtype3 0 1 6 70

Figure S78.  Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S85.  Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 139 131 135 196
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.065 (logrank test), Q value = 1

Table S86.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 572 109 0.1 - 140.4 (18.0)
subtype1 134 33 0.1 - 140.4 (17.1)
subtype2 127 29 0.1 - 131.5 (13.6)
subtype3 127 20 0.5 - 135.7 (19.0)
subtype4 184 27 0.9 - 117.1 (20.5)

Figure S79.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00232 (Kruskal-Wallis (anova)), Q value = 0.29

Table S87.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 600 66.5 (12.6)
subtype1 139 63.6 (12.4)
subtype2 131 65.7 (13.0)
subtype3 134 67.0 (13.0)
subtype4 196 68.7 (12.0)

Figure S80.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0304 (Fisher's exact test), Q value = 1

Table S88.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 437 160
subtype1 93 46
subtype2 103 28
subtype3 106 27
subtype4 135 59

Figure S81.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00252 (Fisher's exact test), Q value = 0.31

Table S89.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 102 1 35 171 11 3 27 21 78 52 59 26 1
subtype1 24 0 7 31 2 2 4 5 23 10 13 11 0
subtype2 12 0 5 44 2 0 2 5 18 18 14 7 0
subtype3 28 1 10 42 2 1 8 6 15 5 7 7 1
subtype4 38 0 13 54 5 0 13 5 22 19 25 1 0

Figure S82.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0444 (Fisher's exact test), Q value = 1

Table S90.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 409 65
subtype1 6 28 89 15
subtype2 1 13 95 22
subtype3 4 28 90 12
subtype4 9 36 135 16

Figure S83.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.134 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 341 147 109
subtype1 78 38 20
subtype2 64 34 32
subtype3 87 30 18
subtype4 112 45 39

Figure S84.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S92.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 449 71 10 1 61
subtype1 91 18 6 0 22
subtype2 91 17 3 0 17
subtype3 100 11 0 1 22
subtype4 167 25 1 0 0

Figure S85.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.492 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 279 322
subtype1 63 76
subtype2 55 76
subtype3 62 73
subtype4 99 97

Figure S86.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0042

Table S94.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 379 57 144 13
subtype1 90 3 46 0
subtype2 86 17 25 3
subtype3 85 20 20 6
subtype4 118 17 53 4

Figure S87.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.428 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 592
subtype1 2 137
subtype2 3 128
subtype3 3 132
subtype4 1 195

Figure S88.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S96.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 434 5 35 28
subtype1 79 2 7 10
subtype2 96 1 3 9
subtype3 91 1 3 9
subtype4 168 1 22 0

Figure S89.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0693 (Kruskal-Wallis (anova)), Q value = 1

Table S97.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 566 2.2 (4.7)
subtype1 125 1.7 (2.9)
subtype2 121 3.0 (6.3)
subtype3 126 1.8 (4.1)
subtype4 194 2.3 (4.9)

Figure S90.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.699 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 47 294
subtype1 0 4 12 98
subtype2 1 4 14 104
subtype3 0 4 17 72
subtype4 0 0 4 20

Figure S91.  Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S99.  Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 79 121 96 63 153 39 50
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.594 (logrank test), Q value = 1

Table S100.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 572 109 0.1 - 140.4 (18.0)
subtype1 77 20 0.1 - 140.4 (15.8)
subtype2 116 16 0.1 - 124.3 (16.3)
subtype3 96 21 0.3 - 129.3 (14.9)
subtype4 61 14 0.1 - 135.7 (17.9)
subtype5 141 23 0.9 - 117.1 (20.0)
subtype6 34 5 0.9 - 54.0 (20.0)
subtype7 47 10 0.9 - 62.0 (27.0)

Figure S92.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000335 (Kruskal-Wallis (anova)), Q value = 0.043

Table S101.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 600 66.5 (12.6)
subtype1 79 64.9 (12.9)
subtype2 121 64.8 (12.9)
subtype3 96 64.0 (11.9)
subtype4 62 65.2 (15.1)
subtype5 153 68.2 (11.0)
subtype6 39 73.8 (12.0)
subtype7 50 68.7 (12.3)

Figure S93.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.00016 (Fisher's exact test), Q value = 0.021

Table S102.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 437 160
subtype1 57 22
subtype2 87 32
subtype3 64 32
subtype4 59 4
subtype5 100 51
subtype6 34 5
subtype7 36 14

Figure S94.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S103.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 102 1 35 171 11 3 27 21 78 52 59 26 1
subtype1 14 0 0 25 1 1 0 4 12 9 5 5 0
subtype2 20 1 8 25 2 1 7 8 21 6 9 9 0
subtype3 8 0 4 26 1 1 2 3 16 12 9 10 0
subtype4 13 0 4 28 1 0 2 1 4 5 1 1 1
subtype5 32 0 7 39 1 0 9 5 16 16 27 1 0
subtype6 4 0 4 14 3 0 5 0 5 1 3 0 0
subtype7 11 0 8 14 2 0 2 0 4 3 5 0 0

Figure S95.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.293 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 105 409 65
subtype1 1 15 54 9
subtype2 7 20 77 16
subtype3 0 12 70 13
subtype4 2 11 44 6
subtype5 6 35 100 12
subtype6 1 3 29 6
subtype7 3 9 35 3

Figure S96.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0106 (Fisher's exact test), Q value = 1

Table S105.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 341 147 109
subtype1 44 18 16
subtype2 66 39 15
subtype3 41 30 23
subtype4 46 9 8
subtype5 83 37 33
subtype6 25 7 7
subtype7 36 7 7

Figure S97.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S106.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 449 71 10 1 61
subtype1 55 8 2 0 13
subtype2 81 12 2 0 26
subtype3 62 14 5 0 14
subtype4 48 2 0 1 8
subtype5 123 27 1 0 0
subtype6 36 3 0 0 0
subtype7 44 5 0 0 0

Figure S98.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.151 (Fisher's exact test), Q value = 1

Table S107.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 279 322
subtype1 36 43
subtype2 53 68
subtype3 43 53
subtype4 26 37
subtype5 72 81
subtype6 27 12
subtype7 22 28

Figure S99.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S108.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 379 57 144 13
subtype1 54 3 22 0
subtype2 75 12 29 3
subtype3 58 6 31 1
subtype4 47 12 3 1
subtype5 98 2 49 1
subtype6 21 13 2 2
subtype7 26 9 8 5

Figure S100.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0277 (Fisher's exact test), Q value = 1

Table S109.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 592
subtype1 0 79
subtype2 2 119
subtype3 5 91
subtype4 1 62
subtype5 0 153
subtype6 1 38
subtype7 0 50

Figure S101.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S110.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 434 5 35 28
subtype1 43 0 2 8
subtype2 77 2 3 6
subtype3 69 0 0 6
subtype4 41 1 0 8
subtype5 126 2 24 0
subtype6 35 0 1 0
subtype7 43 0 5 0

Figure S102.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00627 (Kruskal-Wallis (anova)), Q value = 0.75

Table S111.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 566 2.2 (4.7)
subtype1 71 2.2 (3.2)
subtype2 111 2.1 (4.9)
subtype3 88 2.9 (6.2)
subtype4 56 1.3 (3.1)
subtype5 151 2.3 (4.5)
subtype6 39 2.6 (6.3)
subtype7 50 1.5 (4.1)

Figure S103.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.425 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 47 294
subtype1 0 3 12 60
subtype2 0 1 17 80
subtype3 1 4 8 80
subtype4 0 4 5 51
subtype5 0 0 4 14
subtype6 0 0 0 6
subtype7 0 0 1 3

Figure S104.  Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S113.  Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 212 96 241
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0499 (logrank test), Q value = 1

Table S114.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 520 102 0.1 - 140.4 (19.1)
subtype1 205 40 0.1 - 140.4 (17.8)
subtype2 95 22 0.1 - 100.0 (13.6)
subtype3 220 40 0.9 - 117.1 (21.0)

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00875 (Kruskal-Wallis (anova)), Q value = 1

Table S115.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 548 66.8 (12.7)
subtype1 211 65.9 (12.5)
subtype2 96 64.0 (13.8)
subtype3 241 68.8 (12.1)

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.04 (Fisher's exact test), Q value = 1

Table S116.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 405 140
subtype1 150 61
subtype2 80 15
subtype3 175 64

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00012 (Fisher's exact test), Q value = 0.016

Table S117.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 94 1 36 154 10 2 27 16 69 45 57 23 1
subtype1 32 1 8 67 4 2 8 9 33 13 16 11 1
subtype2 9 0 5 26 0 0 3 3 15 14 7 8 0
subtype3 53 0 23 61 6 0 16 4 21 18 34 4 0

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.106 (Fisher's exact test), Q value = 1

Table S118.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 95 376 56
subtype1 8 33 151 18
subtype2 2 10 69 15
subtype3 10 52 156 23

Figure S109.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.014 (Fisher's exact test), Q value = 1

Table S119.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 313 133 99
subtype1 121 58 30
subtype2 43 29 23
subtype3 149 46 46

Figure S110.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S120.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 412 68 9 1 50
subtype1 149 19 6 1 33
subtype2 65 12 2 0 15
subtype3 198 37 1 0 2

Figure S111.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.54 (Fisher's exact test), Q value = 1

Table S121.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 257 292
subtype1 102 110
subtype2 40 56
subtype3 115 126

Figure S112.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00024 (Fisher's exact test), Q value = 0.032

Table S122.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 351 53 127 10
subtype1 140 10 60 1
subtype2 61 19 13 2
subtype3 150 24 54 7

Figure S113.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.0952 (Fisher's exact test), Q value = 1

Table S123.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 540
subtype1 3 209
subtype2 4 92
subtype3 2 239

Figure S114.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S124.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 384 5 36 26
subtype1 121 2 5 17
subtype2 66 0 2 7
subtype3 197 3 29 2

Figure S115.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0488 (Kruskal-Wallis (anova)), Q value = 1

Table S125.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 514 2.2 (4.7)
subtype1 189 1.8 (3.5)
subtype2 89 3.3 (7.1)
subtype3 236 2.1 (4.4)

Figure S116.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.659 (Fisher's exact test), Q value = 1

Table S126.  Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 26 280
subtype1 0 7 14 171
subtype2 1 4 7 73
subtype3 0 1 5 36

Figure S117.  Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'RACE'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S127.  Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 229 144 176
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.678 (logrank test), Q value = 1

Table S128.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 520 102 0.1 - 140.4 (19.1)
subtype1 223 48 0.1 - 140.4 (16.1)
subtype2 135 25 0.1 - 139.2 (17.9)
subtype3 162 29 0.9 - 83.8 (24.0)

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00428 (Kruskal-Wallis (anova)), Q value = 0.53

Table S129.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 548 66.8 (12.7)
subtype1 228 65.8 (12.8)
subtype2 144 65.1 (13.0)
subtype3 176 69.6 (11.9)

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.262 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 405 140
subtype1 176 51
subtype2 100 43
subtype3 129 46

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0103 (Fisher's exact test), Q value = 1

Table S131.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 94 1 36 154 10 2 27 16 69 45 57 23 1
subtype1 36 0 10 78 4 1 8 8 28 18 13 14 1
subtype2 22 1 6 34 2 1 7 6 21 14 23 4 0
subtype3 36 0 20 42 4 0 12 2 20 13 21 5 0

Figure S121.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.919 (Fisher's exact test), Q value = 1

Table S132.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 95 376 56
subtype1 8 37 158 25
subtype2 6 22 101 14
subtype3 6 36 117 17

Figure S122.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.123 (Fisher's exact test), Q value = 1

Table S133.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 313 133 99
subtype1 138 53 35
subtype2 69 42 32
subtype3 106 38 32

Figure S123.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S134.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A M1B MX
ALL 412 68 9 1 50
subtype1 163 19 5 1 36
subtype2 103 25 2 0 13
subtype3 146 24 2 0 1

Figure S124.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.517 (Fisher's exact test), Q value = 1

Table S135.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 257 292
subtype1 101 128
subtype2 72 72
subtype3 84 92

Figure S125.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.341 (Fisher's exact test), Q value = 1

Table S136.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 351 53 127 10
subtype1 151 25 49 2
subtype2 90 9 40 3
subtype3 110 19 38 5

Figure S126.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.398 (Fisher's exact test), Q value = 1

Table S137.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 9 540
subtype1 6 223
subtype2 1 143
subtype3 2 174

Figure S127.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 7e-05 (Fisher's exact test), Q value = 0.0097

Table S138.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 384 5 36 26
subtype1 142 2 4 17
subtype2 95 1 15 7
subtype3 147 2 17 2

Figure S128.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.127 (Kruskal-Wallis (anova)), Q value = 1

Table S139.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 514 2.2 (4.7)
subtype1 212 1.9 (3.9)
subtype2 129 2.5 (5.4)
subtype3 173 2.3 (5.2)

Figure S129.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.792 (Fisher's exact test), Q value = 1

Table S140.  Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 12 26 280
subtype1 1 8 15 181
subtype2 0 4 7 68
subtype3 0 0 4 31

Figure S130.  Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'RACE'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S141.  Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 24 20 29 28 18 17
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.588 (logrank test), Q value = 1

Table S142.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 133 36 0.1 - 140.4 (15.0)
subtype1 23 6 0.2 - 130.7 (20.6)
subtype2 20 6 0.1 - 47.1 (15.6)
subtype3 28 6 0.2 - 129.3 (13.2)
subtype4 27 10 0.1 - 140.4 (12.6)
subtype5 18 4 1.0 - 139.2 (13.9)
subtype6 17 4 0.1 - 131.5 (21.4)

Figure S131.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.201 (Kruskal-Wallis (anova)), Q value = 1

Table S143.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 135 64.9 (12.6)
subtype1 24 68.0 (12.5)
subtype2 20 62.6 (14.2)
subtype3 29 61.9 (12.3)
subtype4 28 66.4 (10.9)
subtype5 18 62.4 (12.8)
subtype6 16 68.6 (13.2)

Figure S132.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S144.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 85 50
subtype1 13 11
subtype2 20 0
subtype3 6 22
subtype4 16 12
subtype5 16 2
subtype6 14 3

Figure S133.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.196 (Fisher's exact test), Q value = 1

Table S145.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 15 7 39 1 1 2 4 21 14 13 9
subtype1 6 1 7 0 0 1 0 4 1 2 1
subtype2 2 1 4 0 0 0 0 4 4 4 1
subtype3 0 1 8 0 0 0 1 5 4 4 4
subtype4 1 0 10 0 1 1 3 4 2 0 1
subtype5 2 2 4 1 0 0 0 1 1 3 2
subtype6 4 2 6 0 0 0 0 3 2 0 0

Figure S134.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.143 (Fisher's exact test), Q value = 1

Table S146.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 4 16 100 15
subtype1 2 5 15 2
subtype2 0 2 16 2
subtype3 0 0 26 2
subtype4 1 3 21 3
subtype5 0 3 10 5
subtype6 1 3 12 1

Figure S135.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.183 (Fisher's exact test), Q value = 1

Table S147.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 66 39 29
subtype1 14 6 4
subtype2 7 5 8
subtype3 9 10 8
subtype4 13 10 5
subtype5 10 6 2
subtype6 13 2 2

Figure S136.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.127 (Fisher's exact test), Q value = 1

Table S148.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 87 17 4 23
subtype1 17 2 0 5
subtype2 11 5 0 3
subtype3 17 6 1 4
subtype4 18 1 1 5
subtype5 8 3 2 5
subtype6 16 0 0 1

Figure S137.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.857 (Fisher's exact test), Q value = 1

Table S149.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 58 78
subtype1 8 16
subtype2 9 11
subtype3 15 14
subtype4 12 16
subtype5 7 11
subtype6 7 10

Figure S138.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S150.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 75 10 49 1
subtype1 13 0 11 0
subtype2 16 4 0 0
subtype3 6 0 22 0
subtype4 14 2 11 1
subtype5 12 4 2 0
subtype6 14 0 3 0

Figure S139.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.881 (Fisher's exact test), Q value = 1

Table S151.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 4 132
subtype1 1 23
subtype2 0 20
subtype3 2 27
subtype4 1 27
subtype5 0 18
subtype6 0 17

Figure S140.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.533 (Fisher's exact test), Q value = 1

Table S152.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 73 1 2 7
subtype1 12 0 0 2
subtype2 14 0 0 0
subtype3 18 0 2 2
subtype4 11 1 0 0
subtype5 9 0 0 1
subtype6 9 0 0 2

Figure S141.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0318 (Kruskal-Wallis (anova)), Q value = 1

Table S153.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 125 2.9 (6.2)
subtype1 23 2.6 (4.4)
subtype2 19 6.9 (11.6)
subtype3 25 3.1 (4.7)
subtype4 26 2.0 (5.4)
subtype5 17 1.4 (3.0)
subtype6 15 1.1 (2.3)

Figure S142.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.218 (Fisher's exact test), Q value = 1

Table S154.  Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 8 117
subtype1 0 0 2 20
subtype2 1 0 2 16
subtype3 0 1 0 25
subtype4 0 0 1 27
subtype5 0 0 2 15
subtype6 0 2 1 14

Figure S143.  Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'RACE'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S155.  Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 55 15 34 32
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.557 (logrank test), Q value = 1

Table S156.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 133 36 0.1 - 140.4 (15.0)
subtype1 55 17 0.1 - 100.0 (13.4)
subtype2 15 3 0.7 - 129.3 (14.5)
subtype3 33 9 0.2 - 131.5 (24.1)
subtype4 30 7 0.2 - 140.4 (13.8)

Figure S144.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.516 (Kruskal-Wallis (anova)), Q value = 1

Table S157.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 135 64.9 (12.6)
subtype1 55 64.6 (13.8)
subtype2 15 61.7 (12.3)
subtype3 33 67.3 (12.6)
subtype4 32 64.3 (10.5)

Figure S145.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S158.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients COLON RECTUM
ALL 85 50
subtype1 50 5
subtype2 5 9
subtype3 18 16
subtype4 12 20

Figure S146.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.77 (Fisher's exact test), Q value = 1

Table S159.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE IIA STAGE IIB STAGE IIC STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA
ALL 15 7 39 1 1 2 4 21 14 13 9
subtype1 9 3 15 1 0 0 0 7 7 6 2
subtype2 0 1 5 0 0 0 1 4 1 1 2
subtype3 4 2 9 0 1 1 2 7 3 2 1
subtype4 2 1 10 0 0 1 1 3 3 4 4

Figure S147.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.441 (Fisher's exact test), Q value = 1

Table S160.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 4 16 100 15
subtype1 1 9 36 9
subtype2 0 0 14 1
subtype3 2 5 25 2
subtype4 1 2 25 3

Figure S148.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.788 (Fisher's exact test), Q value = 1

Table S161.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2
ALL 66 39 29
subtype1 31 12 12
subtype2 6 5 4
subtype3 15 12 7
subtype4 14 10 6

Figure S149.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.491 (Fisher's exact test), Q value = 1

Table S162.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 M1A MX
ALL 87 17 4 23
subtype1 34 7 1 11
subtype2 10 1 1 3
subtype3 23 3 0 7
subtype4 20 6 2 2

Figure S150.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.326 (Fisher's exact test), Q value = 1

Table S163.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

nPatients FEMALE MALE
ALL 58 78
subtype1 23 32
subtype2 9 6
subtype3 11 23
subtype4 15 17

Figure S151.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0016

Table S164.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients COLON ADENOCARCINOMA COLON MUCINOUS ADENOCARCINOMA RECTAL ADENOCARCINOMA RECTAL MUCINOUS ADENOCARCINOMA
ALL 75 10 49 1
subtype1 42 8 5 0
subtype2 5 0 9 0
subtype3 18 0 15 1
subtype4 10 2 20 0

Figure S152.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.144 (Fisher's exact test), Q value = 1

Table S165.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 4 132
subtype1 0 55
subtype2 1 14
subtype3 2 32
subtype4 1 31

Figure S153.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.355 (Fisher's exact test), Q value = 1

Table S166.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 73 1 2 7
subtype1 33 0 0 3
subtype2 11 0 0 1
subtype3 13 1 0 2
subtype4 16 0 2 1

Figure S154.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.348 (Kruskal-Wallis (anova)), Q value = 1

Table S167.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 125 2.9 (6.2)
subtype1 51 3.0 (7.7)
subtype2 14 3.6 (5.8)
subtype3 32 3.3 (5.8)
subtype4 28 1.8 (2.9)

Figure S155.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.793 (Fisher's exact test), Q value = 1

Table S168.  Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 3 8 117
subtype1 1 1 5 46
subtype2 0 1 0 13
subtype3 0 1 2 30
subtype4 0 0 1 28

Figure S156.  Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'RACE'

Methods & Data
Input
  • Cluster data file = COADREAD-TP.mergedcluster.txt

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

  • Number of patients = 617

  • Number of clustering approaches = 12

  • Number of selected clinical features = 13

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)