Correlation between aggregated molecular cancer subtypes and selected clinical features
Stomach Adenocarcinoma (Primary solid tumor)
15 July 2014  |  analyses__2014_07_15
Maintainer Information
Citation Information
doi:10.7908/C17P8X61
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/C17P8X61
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 10 different clustering approaches and 12 clinical features across 351 patients, 16 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 5 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGY.M.STAGE' and 'HISTOLOGICAL.TYPE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGY.T.STAGE'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY.T.STAGE',  'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'AGE' and 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE',  'PATHOLOGY.T.STAGE',  'HISTOLOGICAL.TYPE',  'COMPLETENESS.OF.RESECTION', and 'RACE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 16 significant findings detected.

Clinical
Features 		Statistical
Tests 		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 0.882
(1.00) 		0.0764
(1.00) 		0.618
(1.00) 		0.0424
(1.00) 		0.0148
(1.00) 		0.0797
(1.00) 		0.154
(1.00) 		0.55
(1.00) 		0.114
(1.00) 		0.103
(1.00)
AGE Kruskal-Wallis (anova) 0.217
(1.00) 		0.0359
(1.00) 		0.0595
(1.00) 		0.328
(1.00) 		0.043
(1.00) 		0.000276
(0.0309) 		0.00127
(0.136) 		0.117
(1.00) 		0.00576
(0.587) 		0.0412
(1.00)
NEOPLASM DISEASESTAGE Fisher's exact test 0.307
(1.00) 		0.338
(1.00) 		0.153
(1.00) 		0.00762
(0.754) 		0.0458
(1.00) 		0.033
(1.00) 		0.0174
(1.00) 		0.142
(1.00) 		0.0648
(1.00) 		0.282
(1.00)
PATHOLOGY T STAGE Fisher's exact test 0.0435
(1.00) 		0.209
(1.00) 		0.00806
(0.79) 		0.00024
(0.0274) 		0.00023
(0.0264) 		0.0445
(1.00) 		0.00047
(0.0522) 		0.00676
(0.676) 		0.00292
(0.304) 		0.0134
(1.00)
PATHOLOGY N STAGE Fisher's exact test 0.00817
(0.792) 		0.892
(1.00) 		0.873
(1.00) 		0.964
(1.00) 		0.038
(1.00) 		0.00579
(0.587) 		0.2
(1.00) 		0.303
(1.00) 		0.657
(1.00) 		0.208
(1.00)
PATHOLOGY M STAGE Fisher's exact test 0.8
(1.00) 		0.00083
(0.0905) 		0.344
(1.00) 		0.0406
(1.00) 		0.128
(1.00) 		0.466
(1.00) 		0.0315
(1.00) 		0.996
(1.00) 		0.0389
(1.00) 		0.522
(1.00)
GENDER Fisher's exact test 0.111
(1.00) 		0.851
(1.00) 		0.228
(1.00) 		0.769
(1.00) 		0.829
(1.00) 		0.0178
(1.00) 		0.819
(1.00) 		0.938
(1.00) 		0.877
(1.00) 		0.39
(1.00)
HISTOLOGICAL TYPE Fisher's exact test 0.0341
(1.00) 		1e-05
(0.0012) 		0.0108
(1.00) 		0.177
(1.00) 		0.00025
(0.0282) 		1e-05
(0.0012) 		1e-05
(0.0012) 		0.00156
(0.165) 		1e-05
(0.0012) 		0.0008
(0.088)
RADIATIONS RADIATION REGIMENINDICATION Fisher's exact test 0.0898
(1.00) 		0.0572
(1.00) 		0.0531
(1.00) 		0.0366
(1.00) 		0.00156
(0.165) 		0.0657
(1.00) 		0.51
(1.00) 		0.695
(1.00) 		0.381
(1.00) 		0.445
(1.00)
COMPLETENESS OF RESECTION Fisher's exact test 0.961
(1.00) 		0.71
(1.00) 		0.545
(1.00) 		0.0445
(1.00) 		0.558
(1.00) 		0.75
(1.00) 		1e-05
(0.0012) 		0.404
(1.00) 		0.0134
(1.00) 		0.748
(1.00)
NUMBER OF LYMPH NODES Kruskal-Wallis (anova) 0.305
(1.00) 		0.36
(1.00) 		0.45
(1.00) 		0.751
(1.00) 		0.00484
(0.498) 		0.369
(1.00) 		0.028
(1.00) 		0.724
(1.00) 		0.229
(1.00) 		0.937
(1.00)
RACE Fisher's exact test 0.341
(1.00) 		0.593
(1.00) 		0.151
(1.00) 		0.181
(1.00) 		0.606
(1.00) 		0.646
(1.00) 		0.00095
(0.103) 		0.483
(1.00) 		0.072
(1.00) 		0.472
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 160 30 62 10 84
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.882 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 315 93 0.1 - 122.3 (11.1)
subtype1 148 48 0.1 - 116.4 (11.5)
subtype2 29 9 0.1 - 79.1 (11.7)
subtype3 54 14 0.1 - 122.3 (6.3)
subtype4 7 1 3.2 - 19.4 (14.0)
subtype5 77 21 0.1 - 72.2 (11.8)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.217 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 339 66.1 (10.7)
subtype1 158 65.1 (11.5)
subtype2 30 68.4 (9.2)
subtype3 60 64.9 (10.1)
subtype4 9 70.3 (9.4)
subtype5 82 67.7 (10.1)

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

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

P value = 0.307 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 12 38 27 32 47 3 61 45 32 31
subtype1 1 3 18 10 16 22 2 24 26 17 13
subtype2 0 3 3 2 3 2 0 7 5 1 3
subtype3 0 0 3 8 4 10 1 14 6 8 6
subtype4 1 1 1 0 1 0 0 2 2 1 0
subtype5 0 5 13 7 8 13 0 14 6 5 9

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0435 (Fisher's exact test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 85 146 85
subtype1 6 36 66 47
subtype2 3 9 16 2
subtype3 2 13 28 17
subtype4 2 4 1 2
subtype5 7 23 35 17

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00817 (Fisher's exact test), Q value = 0.79

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 109 92 66 67
subtype1 48 43 29 34
subtype2 11 1 9 8
subtype3 14 22 17 9
subtype4 3 1 2 3
subtype5 33 25 9 13

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.8 (Fisher's exact test), Q value = 1

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 308 21 17
subtype1 140 12 8
subtype2 29 1 0
subtype3 56 4 2
subtype4 10 0 0
subtype5 73 4 7

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.111 (Fisher's exact test), Q value = 1

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 137 209
subtype1 72 88
subtype2 9 21
subtype3 24 38
subtype4 6 4
subtype5 26 58

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0341 (Fisher's exact test), Q value = 1

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 62 154 54 45 1 19 5 4
subtype1 39 71 20 16 0 10 1 3
subtype2 4 10 8 4 0 2 1 1
subtype3 12 30 8 8 0 3 0 0
subtype4 3 3 2 2 0 0 0 0
subtype5 4 40 16 15 1 4 3 0

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0898 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 339
subtype1 1 159
subtype2 0 30
subtype3 2 60
subtype4 1 9
subtype5 3 81

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.961 (Fisher's exact test), Q value = 1

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 277 13 13 25
subtype1 129 5 6 13
subtype2 24 3 1 1
subtype3 49 2 2 5
subtype4 9 0 0 1
subtype5 66 3 4 5

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.305 (Kruskal-Wallis (anova)), Q value = 1

Table S12.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 306 5.2 (7.7)
subtype1 145 4.4 (5.2)
subtype2 26 7.3 (8.9)
subtype3 53 5.3 (6.7)
subtype4 9 6.9 (7.8)
subtype5 73 5.7 (11.4)

Figure S11.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

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

Table S13.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 84 3 1 208
subtype1 38 1 0 102
subtype2 3 0 0 19
subtype3 17 0 0 37
subtype4 4 0 0 4
subtype5 22 2 1 46

Figure S12.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S14.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 113 88 102
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0764 (logrank test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 292 80 0.1 - 122.3 (10.9)
subtype1 109 20 0.1 - 65.1 (11.7)
subtype2 84 26 0.1 - 79.1 (10.2)
subtype3 99 34 0.1 - 122.3 (10.1)

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0359 (Kruskal-Wallis (anova)), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 296 65.5 (10.7)
subtype1 111 66.3 (10.1)
subtype2 84 67.3 (9.9)
subtype3 101 63.2 (11.5)

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.338 (Fisher's exact test), Q value = 1

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 11 32 25 33 46 2 56 44 32 20
subtype1 2 7 12 7 13 21 0 16 18 14 3
subtype2 0 3 9 10 8 14 0 19 10 8 7
subtype3 0 1 11 8 12 11 2 21 16 10 10

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.209 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 18 71 137 77
subtype1 11 24 48 30
subtype2 4 27 38 19
subtype3 3 20 51 28

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.892 (Fisher's exact test), Q value = 1

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 103 76 60 63
subtype1 43 26 19 25
subtype2 29 23 19 17
subtype3 31 27 22 21

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.00083 (Fisher's exact test), Q value = 0.09

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 274 14 15
subtype1 111 1 1
subtype2 75 4 9
subtype3 88 9 5

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.851 (Fisher's exact test), Q value = 1

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 118 185
subtype1 43 70
subtype2 33 55
subtype3 42 60

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 59 126 45 45 1 18 5 4
subtype1 19 51 13 20 0 7 2 1
subtype2 5 39 17 22 1 2 2 0
subtype3 35 36 15 3 0 9 1 3

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0572 (Fisher's exact test), Q value = 1

Table S23.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 296
subtype1 1 112
subtype2 5 83
subtype3 1 101

Figure S21.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.71 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 265 13 6
subtype1 103 4 1
subtype2 73 4 3
subtype3 89 5 2

Figure S22.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.36 (Kruskal-Wallis (anova)), Q value = 1

Table S25.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 275 5.1 (7.6)
subtype1 104 4.6 (7.2)
subtype2 78 6.1 (10.1)
subtype3 93 4.8 (5.2)

Figure S23.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.593 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 86 4 1 185
subtype1 34 2 0 67
subtype2 24 2 1 52
subtype3 28 0 0 66

Figure S24.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S27.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 82 91 91
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.618 (logrank test), Q value = 1

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 234 72 0.1 - 105.1 (11.0)
subtype1 70 19 0.1 - 105.1 (9.6)
subtype2 85 27 0.1 - 65.1 (9.2)
subtype3 79 26 0.1 - 79.1 (12.8)

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0595 (Kruskal-Wallis (anova)), Q value = 1

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 257 65.8 (10.9)
subtype1 80 66.7 (10.6)
subtype2 91 63.7 (11.7)
subtype3 86 67.3 (10.0)

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.153 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 6 25 28 25 36 3 40 34 24 25
subtype1 0 0 9 4 10 10 1 16 12 8 6
subtype2 0 2 3 12 10 15 1 13 11 11 10
subtype3 2 4 13 12 5 11 1 11 11 5 9

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00806 (Fisher's exact test), Q value = 0.79

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 8 67 105 74
subtype1 0 20 39 20
subtype2 2 16 37 33
subtype3 6 31 29 21

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.873 (Fisher's exact test), Q value = 1

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 87 73 42 50
subtype1 27 22 14 15
subtype2 26 28 15 20
subtype3 34 23 13 15

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.344 (Fisher's exact test), Q value = 1

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 237 16 11
subtype1 72 4 6
subtype2 84 6 1
subtype3 81 6 4

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.228 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 100 164
subtype1 34 48
subtype2 38 53
subtype3 28 63

Figure S31.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0108 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 42 135 37 26 1 16 3 2
subtype1 9 45 13 11 0 3 0 1
subtype2 19 43 9 4 0 12 1 1
subtype3 14 47 15 11 1 1 2 0

Figure S32.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0531 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 257
subtype1 2 80
subtype2 0 91
subtype3 5 86

Figure S33.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.545 (Fisher's exact test), Q value = 1

Table S37.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 205 9 8 25
subtype1 64 3 1 11
subtype2 72 4 2 7
subtype3 69 2 5 7

Figure S34.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.45 (Kruskal-Wallis (anova)), Q value = 1

Table S38.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 223 4.8 (6.3)
subtype1 71 4.4 (6.4)
subtype2 80 4.6 (4.8)
subtype3 72 5.2 (7.6)

Figure S35.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA CNMF subtypes' versus 'RACE'

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

Table S39.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 60 2 159
subtype1 15 0 57
subtype2 23 0 57
subtype3 22 2 45

Figure S36.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S40.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 34 84 55 91
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0424 (logrank test), Q value = 1

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 234 72 0.1 - 105.1 (11.0)
subtype1 32 6 0.1 - 105.1 (12.3)
subtype2 72 23 0.1 - 79.1 (11.6)
subtype3 45 11 0.1 - 72.2 (11.4)
subtype4 85 32 0.1 - 65.1 (7.5)

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.328 (Kruskal-Wallis (anova)), Q value = 1

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 257 65.8 (10.9)
subtype1 34 65.9 (11.4)
subtype2 79 66.4 (11.3)
subtype3 53 67.5 (10.0)
subtype4 91 64.4 (10.8)

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00762 (Fisher's exact test), Q value = 0.75

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 6 25 28 25 36 3 40 34 24 25
subtype1 0 1 7 4 3 4 0 5 5 3 0
subtype2 0 3 10 11 4 9 1 14 10 2 14
subtype3 2 2 4 3 7 4 1 11 8 5 3
subtype4 0 0 4 10 11 19 1 10 11 14 8

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00024 (Fisher's exact test), Q value = 0.027

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 8 67 105 74
subtype1 1 16 9 7
subtype2 3 27 27 23
subtype3 4 8 31 10
subtype4 0 16 38 34

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.964 (Fisher's exact test), Q value = 1

Table S45.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 87 73 42 50
subtype1 14 6 6 6
subtype2 27 24 13 15
subtype3 19 15 8 10
subtype4 27 28 15 19

Figure S41.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0406 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 237 16 11
subtype1 34 0 0
subtype2 70 10 4
subtype3 48 2 5
subtype4 85 4 2

Figure S42.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.769 (Fisher's exact test), Q value = 1

Table S47.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 100 164
subtype1 14 20
subtype2 28 56
subtype3 22 33
subtype4 36 55

Figure S43.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.177 (Fisher's exact test), Q value = 1

Table S48.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 42 135 37 26 1 16 3 2
subtype1 4 14 6 7 0 2 1 0
subtype2 15 40 13 8 0 5 1 2
subtype3 8 32 7 7 0 0 1 0
subtype4 15 49 11 4 1 9 0 0

Figure S44.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0366 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 257
subtype1 0 34
subtype2 6 78
subtype3 0 55
subtype4 1 90

Figure S45.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S50.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 205 9 8 25
subtype1 30 0 0 3
subtype2 60 2 7 7
subtype3 41 2 1 9
subtype4 74 5 0 6

Figure S46.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.751 (Kruskal-Wallis (anova)), Q value = 1

Table S51.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 223 4.8 (6.3)
subtype1 28 4.5 (6.4)
subtype2 66 5.2 (6.8)
subtype3 48 4.9 (8.0)
subtype4 81 4.5 (4.7)

Figure S47.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.181 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 60 2 159
subtype1 7 0 20
subtype2 21 0 39
subtype3 14 2 34
subtype4 18 0 66

Figure S48.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S53.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 66 63 46 37 62
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0148 (logrank test), Q value = 1

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 243 67 0.1 - 105.1 (12.0)
subtype1 61 8 0.1 - 105.1 (12.9)
subtype2 58 22 0.1 - 59.5 (9.9)
subtype3 37 11 0.1 - 55.0 (9.0)
subtype4 32 8 0.6 - 47.0 (13.2)
subtype5 55 18 0.1 - 72.2 (9.2)

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.043 (Kruskal-Wallis (anova)), Q value = 1

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 267 65.7 (10.8)
subtype1 65 65.6 (11.1)
subtype2 63 62.3 (11.6)
subtype3 43 65.8 (10.6)
subtype4 36 69.9 (10.6)
subtype5 60 67.0 (9.0)

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0458 (Fisher's exact test), Q value = 1

Table S56.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 10 27 23 29 43 3 40 32 23 27
subtype1 0 2 8 6 12 11 0 8 6 6 3
subtype2 0 0 1 5 7 9 1 9 12 10 6
subtype3 0 2 3 4 2 7 0 7 5 3 10
subtype4 2 3 8 1 3 7 1 5 3 0 3
subtype5 0 3 7 7 5 9 1 11 6 4 5

Figure S51.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00023 (Fisher's exact test), Q value = 0.026

Table S57.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 13 70 107 75
subtype1 2 19 24 18
subtype2 0 5 30 25
subtype3 3 13 16 13
subtype4 6 15 8 8
subtype5 2 18 29 11

Figure S52.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.038 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 91 76 46 49
subtype1 30 17 8 9
subtype2 14 19 8 19
subtype3 10 15 11 8
subtype4 17 7 9 3
subtype5 20 18 10 10

Figure S53.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.128 (Fisher's exact test), Q value = 1

Table S59.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 243 18 13
subtype1 63 2 1
subtype2 58 3 2
subtype3 35 7 4
subtype4 34 2 1
subtype5 53 4 5

Figure S54.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.829 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 105 169
subtype1 28 38
subtype2 25 38
subtype3 18 28
subtype4 14 23
subtype5 20 42

Figure S55.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00025 (Fisher's exact test), Q value = 0.028

Table S61.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 51 131 34 36 1 14 4 1
subtype1 12 42 3 8 0 0 1 0
subtype2 19 28 4 3 0 7 0 1
subtype3 10 19 6 7 1 3 0 0
subtype4 5 13 7 8 0 3 1 0
subtype5 5 29 14 10 0 1 2 0

Figure S56.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.00156 (Fisher's exact test), Q value = 0.17

Table S62.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 267
subtype1 0 66
subtype2 0 63
subtype3 5 41
subtype4 0 37
subtype5 2 60

Figure S57.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.558 (Fisher's exact test), Q value = 1

Table S63.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 216 9 10 22
subtype1 55 1 1 5
subtype2 47 2 0 6
subtype3 35 2 5 4
subtype4 30 1 2 3
subtype5 49 3 2 4

Figure S58.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.00484 (Kruskal-Wallis (anova)), Q value = 0.5

Table S64.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 237 4.9 (7.1)
subtype1 57 4.2 (8.5)
subtype2 53 5.5 (5.4)
subtype3 40 6.3 (7.9)
subtype4 35 4.1 (8.2)
subtype5 52 4.4 (5.2)

Figure S59.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'RACE'

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

Table S65.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 73 4 156
subtype1 20 1 38
subtype2 19 0 43
subtype3 13 0 19
subtype4 6 1 21
subtype5 15 2 35

Figure S60.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S66.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 38 21 50 62 42 40 21
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0797 (logrank test), Q value = 1

Table S67.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 243 67 0.1 - 105.1 (12.0)
subtype1 34 12 0.1 - 105.1 (4.5)
subtype2 20 4 0.3 - 24.7 (13.1)
subtype3 47 18 0.1 - 59.5 (12.6)
subtype4 52 15 0.1 - 55.0 (9.9)
subtype5 39 10 0.1 - 54.1 (11.1)
subtype6 34 8 0.1 - 72.2 (11.0)
subtype7 17 0 0.1 - 32.9 (15.8)

Figure S61.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000276 (Kruskal-Wallis (anova)), Q value = 0.031

Table S68.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 267 65.7 (10.8)
subtype1 38 65.2 (10.4)
subtype2 20 61.6 (11.5)
subtype3 50 59.7 (11.1)
subtype4 59 67.9 (9.8)
subtype5 41 70.2 (10.8)
subtype6 38 67.2 (9.3)
subtype7 21 67.6 (8.5)

Figure S62.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.033 (Fisher's exact test), Q value = 1

Table S69.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 10 27 23 29 43 3 40 32 23 27
subtype1 0 0 2 6 2 8 1 3 4 1 7
subtype2 0 1 1 0 2 4 0 4 6 2 1
subtype3 0 0 2 5 7 8 1 7 8 7 4
subtype4 0 4 8 7 3 10 0 9 5 4 9
subtype5 2 3 7 1 9 5 1 4 3 1 5
subtype6 0 2 4 3 4 4 0 12 3 3 1
subtype7 0 0 3 1 2 4 0 1 3 5 0

Figure S63.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0445 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 13 70 107 75
subtype1 0 10 16 10
subtype2 1 2 11 7
subtype3 0 9 23 17
subtype4 6 21 18 15
subtype5 5 13 16 8
subtype6 1 11 18 8
subtype7 0 4 5 10

Figure S64.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.00579 (Fisher's exact test), Q value = 0.59

Table S71.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 91 76 46 49
subtype1 10 17 4 4
subtype2 5 9 2 5
subtype3 13 12 7 16
subtype4 18 19 16 7
subtype5 22 6 9 4
subtype6 13 10 7 6
subtype7 10 3 1 7

Figure S65.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.466 (Fisher's exact test), Q value = 1

Table S72.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 243 18 13
subtype1 34 3 1
subtype2 19 1 1
subtype3 45 4 1
subtype4 51 7 4
subtype5 37 3 2
subtype6 36 0 4
subtype7 21 0 0

Figure S66.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0178 (Fisher's exact test), Q value = 1

Table S73.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 105 169
subtype1 17 21
subtype2 2 19
subtype3 22 28
subtype4 24 38
subtype5 19 23
subtype6 10 30
subtype7 11 10

Figure S67.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S74.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 51 131 34 36 1 14 4 1
subtype1 8 19 6 1 0 2 0 0
subtype2 5 11 0 4 0 0 1 0
subtype3 22 17 3 1 0 6 0 1
subtype4 9 29 7 13 1 3 0 0
subtype5 4 16 10 10 0 1 1 0
subtype6 1 24 6 6 0 1 2 0
subtype7 2 15 2 1 0 1 0 0

Figure S68.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.0657 (Fisher's exact test), Q value = 1

Table S75.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 267
subtype1 0 38
subtype2 0 21
subtype3 0 50
subtype4 5 57
subtype5 0 42
subtype6 2 38
subtype7 0 21

Figure S69.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.75 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 216 9 10 22
subtype1 27 1 1 5
subtype2 20 0 0 1
subtype3 37 3 1 3
subtype4 48 3 6 4
subtype5 34 1 2 2
subtype6 33 1 0 4
subtype7 17 0 0 3

Figure S70.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.369 (Kruskal-Wallis (anova)), Q value = 1

Table S77.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 237 4.9 (7.1)
subtype1 29 5.0 (7.8)
subtype2 19 6.8 (11.6)
subtype3 44 5.5 (5.9)
subtype4 55 4.7 (6.6)
subtype5 38 4.4 (8.2)
subtype6 32 3.6 (4.0)
subtype7 20 4.7 (5.9)

Figure S71.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.646 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 73 4 156
subtype1 8 0 21
subtype2 7 0 14
subtype3 15 0 34
subtype4 19 1 25
subtype5 10 2 20
subtype6 10 1 26
subtype7 4 0 16

Figure S72.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 117 103 127
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.154 (logrank test), Q value = 1

Table S80.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 316 92 0.1 - 122.3 (11.1)
subtype1 100 32 0.1 - 105.1 (12.5)
subtype2 97 36 0.1 - 116.4 (11.3)
subtype3 119 24 0.1 - 122.3 (9.2)

Figure S73.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00127 (Kruskal-Wallis (anova)), Q value = 0.14

Table S81.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 340 66.1 (10.7)
subtype1 116 67.8 (9.9)
subtype2 102 62.4 (11.5)
subtype3 122 67.5 (10.2)

Figure S74.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0174 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 12 38 28 33 47 3 61 45 32 30
subtype1 1 3 11 11 10 15 2 21 9 7 16
subtype2 0 0 8 8 9 15 1 15 21 12 10
subtype3 1 9 19 9 14 17 0 25 15 13 4

Figure S75.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00047 (Fisher's exact test), Q value = 0.052

Table S83.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 85 147 85
subtype1 5 37 48 21
subtype2 0 20 45 35
subtype3 15 28 54 29

Figure S76.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.2 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 111 92 66 66
subtype1 32 34 25 18
subtype2 28 26 19 27
subtype3 51 32 22 21

Figure S77.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0315 (Fisher's exact test), Q value = 1

Table S85.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 309 21 17
subtype1 98 13 6
subtype2 93 6 4
subtype3 118 2 7

Figure S78.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.819 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 137 210
subtype1 48 69
subtype2 38 65
subtype3 51 76

Figure S79.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S87.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 62 155 53 45 1 20 5 4
subtype1 16 59 25 12 0 3 0 1
subtype2 34 39 9 8 0 11 0 1
subtype3 12 57 19 25 1 6 5 2

Figure S80.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.51 (Fisher's exact test), Q value = 1

Table S88.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 340
subtype1 1 116
subtype2 2 101
subtype3 4 123

Figure S81.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S89.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 278 13 12 25
subtype1 78 4 11 18
subtype2 86 4 0 6
subtype3 114 5 1 1

Figure S82.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.028 (Kruskal-Wallis (anova)), Q value = 1

Table S90.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 306 5.0 (7.2)
subtype1 99 5.4 (7.5)
subtype2 92 5.3 (5.3)
subtype3 115 4.4 (8.2)

Figure S83.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.00095 (Fisher's exact test), Q value = 0.1

Table S91.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 85 4 208
subtype1 14 1 69
subtype2 25 0 72
subtype3 46 3 67

Figure S84.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S92.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 169 81 97
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.55 (logrank test), Q value = 1

Table S93.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 316 92 0.1 - 122.3 (11.1)
subtype1 151 41 0.1 - 122.3 (11.4)
subtype2 73 25 0.1 - 116.4 (11.9)
subtype3 92 26 0.1 - 79.1 (9.2)

Figure S85.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.117 (Kruskal-Wallis (anova)), Q value = 1

Table S94.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 340 66.1 (10.7)
subtype1 165 65.9 (10.4)
subtype2 80 64.2 (12.0)
subtype3 95 68.0 (9.9)

Figure S86.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.142 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 12 38 28 33 47 3 61 45 32 30
subtype1 2 11 19 10 17 21 2 30 21 15 12
subtype2 0 0 7 5 10 9 0 12 16 8 10
subtype3 0 1 12 13 6 17 1 19 8 9 8

Figure S87.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00676 (Fisher's exact test), Q value = 0.68

Table S96.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 20 85 147 85
subtype1 15 49 63 38
subtype2 0 12 40 25
subtype3 5 24 44 22

Figure S88.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.303 (Fisher's exact test), Q value = 1

Table S97.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 111 92 66 66
subtype1 57 42 33 31
subtype2 24 21 11 22
subtype3 30 29 22 13

Figure S89.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.996 (Fisher's exact test), Q value = 1

Table S98.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 309 21 17
subtype1 150 11 8
subtype2 72 5 4
subtype3 87 5 5

Figure S90.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.938 (Fisher's exact test), Q value = 1

Table S99.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 137 210
subtype1 67 102
subtype2 33 48
subtype3 37 60

Figure S91.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00156 (Fisher's exact test), Q value = 0.17

Table S100.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 62 155 53 45 1 20 5 4
subtype1 33 77 26 21 0 7 2 3
subtype2 22 36 6 6 0 8 0 1
subtype3 7 42 21 18 1 5 3 0

Figure S92.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.695 (Fisher's exact test), Q value = 1

Table S101.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 340
subtype1 3 166
subtype2 1 80
subtype3 3 94

Figure S93.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.404 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 278 13 12 25
subtype1 133 6 8 15
subtype2 62 4 1 7
subtype3 83 3 3 3

Figure S94.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.724 (Kruskal-Wallis (anova)), Q value = 1

Table S103.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 306 5.0 (7.2)
subtype1 153 5.1 (7.5)
subtype2 72 4.9 (5.3)
subtype3 81 4.9 (8.0)

Figure S95.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.483 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 85 4 208
subtype1 37 3 102
subtype2 20 1 54
subtype3 28 0 52

Figure S96.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S105.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 78 126 96
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.114 (logrank test), Q value = 1

Table S106.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 289 77 0.1 - 122.3 (11.1)
subtype1 76 23 0.1 - 105.1 (12.8)
subtype2 119 22 0.1 - 122.3 (9.2)
subtype3 94 32 0.1 - 116.4 (9.7)

Figure S97.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00576 (Kruskal-Wallis (anova)), Q value = 0.59

Table S107.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 293 65.5 (10.7)
subtype1 76 67.0 (10.5)
subtype2 121 67.1 (9.6)
subtype3 96 62.3 (11.5)

Figure S98.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0648 (Fisher's exact test), Q value = 1

Table S108.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 11 32 25 33 46 2 55 44 31 19
subtype1 1 1 7 10 9 11 1 15 9 6 8
subtype2 1 10 16 7 15 20 0 25 15 14 3
subtype3 0 0 9 8 9 15 1 15 20 11 8

Figure S99.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.00292 (Fisher's exact test), Q value = 0.3

Table S109.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 18 71 135 76
subtype1 2 25 33 18
subtype2 15 30 54 27
subtype3 1 16 48 31

Figure S100.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.657 (Fisher's exact test), Q value = 1

Table S110.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 103 75 60 61
subtype1 26 22 17 13
subtype2 48 29 26 23
subtype3 29 24 17 25

Figure S101.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.0389 (Fisher's exact test), Q value = 1

Table S111.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 272 14 14
subtype1 69 7 2
subtype2 118 1 7
subtype3 85 6 5

Figure S102.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.877 (Fisher's exact test), Q value = 1

Table S112.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 185
subtype1 28 50
subtype2 50 76
subtype3 37 59

Figure S103.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 1e-05 (Fisher's exact test), Q value = 0.0012

Table S113.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 57 126 44 44 1 19 5 4
subtype1 12 32 20 9 0 3 1 1
subtype2 11 59 15 29 1 5 4 2
subtype3 34 35 9 6 0 11 0 1

Figure S104.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.381 (Fisher's exact test), Q value = 1

Table S114.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 293
subtype1 1 77
subtype2 5 121
subtype3 1 95

Figure S105.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.0134 (Fisher's exact test), Q value = 1

Table S115.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 264 12 5
subtype1 66 2 5
subtype2 114 6 0
subtype3 84 4 0

Figure S106.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.229 (Kruskal-Wallis (anova)), Q value = 1

Table S116.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 272 4.9 (7.0)
subtype1 70 5.1 (6.7)
subtype2 117 4.6 (8.2)
subtype3 85 5.0 (5.3)

Figure S107.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.072 (Fisher's exact test), Q value = 1

Table S117.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 85 4 183
subtype1 11 1 48
subtype2 44 2 71
subtype3 30 1 64

Figure S108.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S118.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 166 60 74
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.103 (logrank test), Q value = 1

Table S119.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 289 77 0.1 - 122.3 (11.1)
subtype1 158 37 0.1 - 122.3 (11.9)
subtype2 59 19 0.1 - 116.4 (12.8)
subtype3 72 21 0.1 - 60.6 (6.7)

Figure S109.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0412 (Kruskal-Wallis (anova)), Q value = 1

Table S120.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 293 65.5 (10.7)
subtype1 161 66.3 (10.6)
subtype2 60 62.1 (12.0)
subtype3 72 66.6 (9.1)

Figure S110.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.282 (Fisher's exact test), Q value = 1

Table S121.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE IA STAGE IB STAGE II STAGE IIA STAGE IIB STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV
ALL 2 11 32 25 33 46 2 55 44 31 19
subtype1 2 10 17 12 19 25 1 29 22 19 10
subtype2 0 0 7 3 8 8 0 7 14 7 6
subtype3 0 1 8 10 6 13 1 19 8 5 3

Figure S111.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.0134 (Fisher's exact test), Q value = 1

Table S122.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 18 71 135 76
subtype1 16 44 67 39
subtype2 0 9 30 21
subtype3 2 18 38 16

Figure S112.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.208 (Fisher's exact test), Q value = 1

Table S123.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients N0 N1 N2 N3
ALL 103 75 60 61
subtype1 57 41 35 33
subtype2 22 11 8 18
subtype3 24 23 17 10

Figure S113.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.522 (Fisher's exact test), Q value = 1

Table S124.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 272 14 14
subtype1 150 7 9
subtype2 52 5 3
subtype3 70 2 2

Figure S114.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.39 (Fisher's exact test), Q value = 1

Table S125.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 115 185
subtype1 58 108
subtype2 26 34
subtype3 31 43

Figure S115.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 8e-04 (Fisher's exact test), Q value = 0.088

Table S126.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

nPatients STOMACH ADENOCARCINOMA DIFFUSE TYPE STOMACH ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA NOT OTHERWISE SPECIFIED (NOS) STOMACH INTESTINAL ADENOCARCINOMA TUBULAR TYPE STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE STOMACH INTESTINAL ADENOCARCINOMA  MUCINOUS TYPE STOMACH INTESTINAL ADENOCARCINOMA  PAPILLARY TYPE STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL 57 126 44 44 1 19 5 4
subtype1 31 74 23 25 0 8 2 3
subtype2 21 22 5 4 0 7 0 1
subtype3 5 30 16 15 1 4 3 0

Figure S116.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL.TYPE'

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

P value = 0.445 (Fisher's exact test), Q value = 1

Table S127.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 7 293
subtype1 4 162
subtype2 0 60
subtype3 3 71

Figure S117.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.748 (Fisher's exact test), Q value = 1

Table S128.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 264 12 5
subtype1 148 7 4
subtype2 50 3 1
subtype3 66 2 0

Figure S118.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.937 (Kruskal-Wallis (anova)), Q value = 1

Table S129.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

nPatients Mean (Std.Dev)
ALL 272 4.9 (7.0)
subtype1 155 5.1 (8.0)
subtype2 54 4.8 (5.5)
subtype3 63 4.3 (5.5)

Figure S119.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.472 (Fisher's exact test), Q value = 1

Table S130.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 85 4 183
subtype1 43 4 99
subtype2 17 0 41
subtype3 25 0 43

Figure S120.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

Methods & Data
Input

  • Cluster data file = STAD-TP.mergedcluster.txt

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

  • Number of patients = 351

  • Number of clustering approaches = 10

  • Number of selected clinical features = 12

  • 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)
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