Correlation between aggregated molecular cancer subtypes and selected clinical features

Stomach 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/C1QZ28XD

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 397 patients, 11 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' and 'HISTOLOGICAL.TYPE'.
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 'PATHOLOGY.T.STAGE' and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
6 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, 11 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	0.935 (1.00)	0.037 (1.00)	0.574 (1.00)	0.0285 (1.00)	0.0108 (1.00)	0.0809 (1.00)	0.0511 (1.00)	0.106 (1.00)	0.0854 (1.00)	0.277 (1.00)
AGE	Kruskal-Wallis (anova)	0.364 (1.00)	0.0293 (1.00)	0.0595 (1.00)	0.328 (1.00)	0.043 (1.00)	0.000276 (0.0311)	0.0122 (1.00)	0.0585 (1.00)	0.0511 (1.00)	0.0744 (1.00)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.376 (1.00)	0.284 (1.00)	0.213 (1.00)	0.0111 (1.00)	0.0358 (1.00)	0.0103 (1.00)	0.199 (1.00)	0.109 (1.00)	0.077 (1.00)	0.485 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.156 (1.00)	0.17 (1.00)	0.00772 (0.795)	0.00029 (0.0325)	0.0001 (0.0115)	0.0439 (1.00)	0.00017 (0.0194)	0.048 (1.00)	0.00673 (0.707)	0.213 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.0768 (1.00)	0.834 (1.00)	0.873 (1.00)	0.965 (1.00)	0.0378 (1.00)	0.00636 (0.674)	0.212 (1.00)	0.236 (1.00)	0.167 (1.00)	0.22 (1.00)
PATHOLOGY M STAGE	Fisher's exact test	0.789 (1.00)	0.00068 (0.0748)	0.342 (1.00)	0.0407 (1.00)	0.129 (1.00)	0.469 (1.00)	0.366 (1.00)	0.918 (1.00)	0.211 (1.00)	0.589 (1.00)
GENDER	Fisher's exact test	0.0384 (1.00)	0.839 (1.00)	0.227 (1.00)	0.766 (1.00)	0.826 (1.00)	0.0185 (1.00)	0.697 (1.00)	0.568 (1.00)	0.899 (1.00)	0.318 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	0.0211 (1.00)	1e-05 (0.0012)	0.00756 (0.786)	0.224 (1.00)	0.00029 (0.0325)	2e-05 (0.00236)	7e-05 (0.00812)	0.00346 (0.377)	5e-05 (0.00585)	1e-05 (0.0012)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.184 (1.00)	0.165 (1.00)	0.124 (1.00)	0.00621 (0.664)	0.00795 (0.811)	0.146 (1.00)	0.77 (1.00)	0.34 (1.00)	0.538 (1.00)	0.23 (1.00)
COMPLETENESS OF RESECTION	Fisher's exact test	0.888 (1.00)	0.477 (1.00)	0.547 (1.00)	0.0446 (1.00)	0.554 (1.00)	0.749 (1.00)	0.135 (1.00)	0.771 (1.00)	0.299 (1.00)	0.0734 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.198 (1.00)	0.262 (1.00)	0.45 (1.00)	0.751 (1.00)	0.00484 (0.522)	0.369 (1.00)	0.103 (1.00)	0.0497 (1.00)	0.155 (1.00)	0.0993 (1.00)
RACE	Fisher's exact test	0.553 (1.00)	0.403 (1.00)	0.15 (1.00)	0.182 (1.00)	0.608 (1.00)	0.642 (1.00)	0.237 (1.00)	0.644 (1.00)	0.765 (1.00)	0.211 (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	108	173	32	64	18

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.935 (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	364	122	0.1 - 122.3 (11.6)
subtype1	101	34	0.1 - 72.2 (11.5)
subtype2	161	56	0.1 - 116.4 (12.3)
subtype3	30	13	0.1 - 79.1 (12.9)
subtype4	57	15	0.1 - 122.3 (7.4)
subtype5	15	4	0.8 - 24.2 (12.0)

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 = 0.364 (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	388	66.0 (10.8)
subtype1	106	67.3 (10.2)
subtype2	172	65.1 (11.6)
subtype3	31	67.5 (9.7)
subtype4	62	64.5 (10.3)
subtype5	17	68.2 (9.3)

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 = 0.376 (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	13	39	31	36	49	3	77	55	37	37
subtype1	0	5	15	8	9	15	0	25	10	5	12
subtype2	1	3	18	12	17	23	2	30	26	17	16
subtype3	0	2	3	2	4	1	0	5	8	3	3
subtype4	0	1	2	7	5	9	1	15	8	10	4
subtype5	1	2	1	2	1	1	0	2	3	2	2

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 = 0.156 (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	21	87	173	104
subtype1	6	28	50	22
subtype2	7	34	72	55
subtype3	2	7	17	6
subtype4	3	11	30	18
subtype5	3	7	4	3

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 = 0.0768 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2	N3
ALL	122	107	75	79
subtype1	36	33	19	16
subtype2	55	48	30	34
subtype3	11	2	7	11
subtype4	16	21	16	11
subtype5	4	3	3	7

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 = 0.789 (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	353	24	18
subtype1	94	7	7
subtype2	153	13	7
subtype3	30	2	0
subtype4	59	2	3
subtype5	17	0	1

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 = 0.0384 (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	151	244
subtype1	35	73
subtype2	77	96
subtype3	6	26
subtype4	25	39
subtype5	8	10

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 = 0.0211 (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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	65	157	69	64	21	8	9
subtype1	6	44	23	24	4	4	2
subtype2	38	71	24	19	12	2	7
subtype3	7	8	9	6	1	1	0
subtype4	11	28	10	11	3	0	0
subtype5	3	6	3	4	1	1	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 = 0.184 (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	6	389
subtype1	2	106
subtype2	1	172
subtype3	0	32
subtype4	2	62
subtype5	1	17

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 = 0.888 (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	318	16	18	25
subtype1	84	4	8	5
subtype2	139	8	7	13
subtype3	26	2	2	1
subtype4	52	2	1	5
subtype5	17	0	0	1

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 = 0.198 (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	355	5.6 (8.5)
subtype1	95	5.8 (10.4)
subtype2	159	4.7 (6.9)
subtype3	28	8.6 (10.0)
subtype4	56	5.6 (8.1)
subtype5	17	8.5 (9.8)

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 = 0.553 (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	88	6	1	243
subtype1	26	3	1	60
subtype2	34	2	0	117
subtype3	6	1	0	16
subtype4	18	0	0	41
subtype5	4	0	0	9

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	121	92	106

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.037 (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	308	90	0.1 - 122.3 (11.3)
subtype1	117	23	0.1 - 65.1 (12.1)
subtype2	88	30	0.1 - 79.1 (10.4)
subtype3	103	37	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 = 0.0293 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	312	65.6 (10.7)
subtype1	119	66.5 (10.2)
subtype2	88	67.2 (10.0)
subtype3	105	63.2 (11.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.284 (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	33	26	37	47	2	64	46	32	19
subtype1	2	7	12	8	15	22	0	19	19	14	3
subtype2	0	3	10	10	8	15	0	20	10	8	8
subtype3	0	1	11	8	14	10	2	25	17	10	8

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 = 0.17 (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	72	148	81
subtype1	11	24	53	33
subtype2	4	28	40	20
subtype3	3	20	55	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 = 0.834 (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	110	81	62	65
subtype1	47	29	19	26
subtype2	30	24	20	18
subtype3	33	28	23	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 = 0.00068 (Fisher's exact test), Q value = 0.075

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

nPatients	M0	M1	MX
ALL	289	15	15
subtype1	119	1	1
subtype2	78	5	9
subtype3	92	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 = 0.839 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	122	197
subtype1	45	76
subtype2	34	58
subtype3	43	63

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	59	128	50	50	19	7	6
subtype1	19	52	17	21	8	3	1
subtype2	5	39	18	24	2	3	1
subtype3	35	37	15	5	9	1	4

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 = 0.165 (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	6	313
subtype1	1	120
subtype2	4	88
subtype3	1	105

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 = 0.477 (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	280	13	7
subtype1	111	4	1
subtype2	76	4	4
subtype3	93	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 = 0.262 (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	291	5.0 (7.5)
subtype1	112	4.4 (7.1)
subtype2	82	6.1 (9.9)
subtype3	97	4.7 (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 = 0.403 (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	88	6	1	196
subtype1	34	3	0	73
subtype2	25	3	1	54
subtype3	29	0	0	69

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 = 0.574 (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	73	0.1 - 105.1 (11.0)
subtype1	70	19	0.1 - 105.1 (8.5)
subtype2	85	28	0.1 - 65.1 (9.6)
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 = 0.213 (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	35	3	43	34	24	23
subtype1	0	0	9	4	10	11	1	15	12	8	6
subtype2	0	2	3	12	10	14	1	15	11	11	9
subtype3	2	4	13	12	5	10	1	13	11	5	8

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 = 0.00772 (Fisher's exact test), Q value = 0.8

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 = 0.342 (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 = 0.227 (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 = 0.00756 (Fisher's exact test), Q value = 0.79

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	42	135	37	26	16	4	2
subtype1	9	45	13	11	3	0	1
subtype2	19	43	9	4	12	1	1
subtype3	14	47	15	11	1	3	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 = 0.124 (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	6	258
subtype1	2	80
subtype2	0	91
subtype3	4	87

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 = 0.547 (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 = 0.15 (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 = 0.0285 (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	73	0.1 - 105.1 (11.0)
subtype1	32	6	0.1 - 105.1 (12.3)
subtype2	72	23	0.1 - 79.1 (12.7)
subtype3	45	11	0.1 - 72.2 (10.6)
subtype4	85	33	0.1 - 65.1 (7.4)

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 = 0.0111 (Fisher's exact test), Q value = 1

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	35	3	43	34	24	23
subtype1	0	1	7	4	3	5	0	4	5	3	0
subtype2	0	3	10	11	4	8	1	16	10	2	13
subtype3	2	2	4	3	7	4	1	11	8	5	3
subtype4	0	0	4	10	11	18	1	12	11	14	7

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 = 0.00029 (Fisher's exact test), Q value = 0.032

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 = 0.965 (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 = 0.0407 (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 = 0.766 (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 = 0.224 (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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	42	135	37	26	16	4	2
subtype1	4	14	6	7	2	1	0
subtype2	15	40	13	8	5	1	2
subtype3	8	32	7	7	0	1	0
subtype4	15	49	11	4	9	1	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 = 0.00621 (Fisher's exact test), Q value = 0.66

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

nPatients	NO	YES
ALL	6	258
subtype1	0	34
subtype2	6	78
subtype3	0	55
subtype4	0	91

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 = 0.0446 (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 = 0.182 (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 = 0.0108 (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	68	0.1 - 105.1 (11.8)
subtype1	61	8	0.1 - 105.1 (12.9)
subtype2	58	23	0.1 - 59.5 (9.9)
subtype3	37	11	0.1 - 55.0 (8.8)
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 = 0.0358 (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	42	32	23	25
subtype1	0	2	8	6	12	11	0	8	6	6	3
subtype2	0	0	1	5	7	9	1	10	12	10	5
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	12	6	4	4

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 = 1e-04 (Fisher's exact test), Q value = 0.011

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 = 0.0378 (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 = 0.129 (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 = 0.826 (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 = 0.00029 (Fisher's exact test), Q value = 0.032

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	12	42	3	8	0	1	0
subtype2	19	28	4	3	7	0	1
subtype3	10	19	6	7	3	1	0
subtype4	5	13	7	8	3	1	0
subtype5	5	29	14	10	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 = 0.00795 (Fisher's exact test), Q value = 0.81

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

nPatients	NO	YES
ALL	6	268
subtype1	0	66
subtype2	0	63
subtype3	4	42
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 = 0.554 (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 = 0.52

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 = 0.608 (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 = 0.0809 (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	68	0.1 - 105.1 (11.8)
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 (9.3)
subtype5	39	11	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 = 0.0103 (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	42	32	23	25
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	8	8	7	3
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	13	3	3	0
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 = 0.0439 (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 = 0.00636 (Fisher's exact test), Q value = 0.67

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 = 0.469 (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 = 0.0185 (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 = 2e-05 (Fisher's exact test), Q value = 0.0024

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	131	34	36	14	5	1
subtype1	8	19	6	1	2	0	0
subtype2	5	11	0	4	0	1	0
subtype3	22	17	3	1	6	0	1
subtype4	9	29	7	13	3	1	0
subtype5	4	16	10	10	1	1	0
subtype6	1	24	6	6	1	2	0
subtype7	2	15	2	1	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 = 0.146 (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	6	268
subtype1	0	38
subtype2	0	21
subtype3	0	50
subtype4	4	58
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 = 0.749 (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 = 0.642 (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	166	125	105

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0511 (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	365	121	0.1 - 122.3 (11.6)
subtype1	147	44	0.1 - 122.3 (12.8)
subtype2	118	47	0.1 - 116.4 (10.3)
subtype3	100	30	0.1 - 72.2 (9.9)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0122 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	389	65.9 (10.8)
subtype1	162	67.5 (10.3)
subtype2	125	63.4 (11.7)
subtype3	102	66.5 (10.1)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.199 (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	13	39	32	37	49	3	77	55	37	36
subtype1	2	8	17	18	17	19	2	30	17	11	15
subtype2	0	0	9	8	12	15	1	23	22	17	14
subtype3	0	5	13	6	8	15	0	24	16	9	7

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 = 0.00017 (Fisher's exact test), Q value = 0.019

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

nPatients	T1	T2	T3	T4
ALL	21	87	174	104
subtype1	13	44	72	31
subtype2	0	21	54	47
subtype3	8	22	48	26

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 = 0.212 (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	124	107	75	78
subtype1	55	50	31	23
subtype2	35	31	22	34
subtype3	34	26	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 = 0.366 (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	354	24	18
subtype1	148	11	7
subtype2	111	10	4
subtype3	95	3	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 = 0.697 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	151	245
subtype1	67	99
subtype2	47	78
subtype3	37	68

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 7e-05 (Fisher's exact test), Q value = 0.0081

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	65	158	68	64	22	8	9
subtype1	20	68	34	28	6	4	5
subtype2	38	49	14	11	10	1	1
subtype3	7	41	20	25	6	3	3

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 = 0.77 (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	6	390
subtype1	3	163
subtype2	1	124
subtype3	2	103

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 = 0.135 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	319	16	17	25
subtype1	126	6	9	17
subtype2	103	6	4	6
subtype3	90	4	4	2

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 = 0.103 (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	355	5.5 (8.1)
subtype1	148	5.0 (8.2)
subtype2	113	5.8 (7.2)
subtype3	94	5.8 (9.2)

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 = 0.237 (Fisher's exact test), Q value = 1

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

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	89	7	243
subtype1	29	3	98
subtype2	28	2	88
subtype3	32	2	57

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	4
Number of samples	73	155	76	92

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.106 (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	365	121	0.1 - 122.3 (11.6)
subtype1	65	17	0.1 - 34.7 (9.3)
subtype2	141	43	0.1 - 122.3 (12.8)
subtype3	72	28	0.1 - 116.4 (11.7)
subtype4	87	33	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 = 0.0585 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	389	65.9 (10.8)
subtype1	72	68.2 (9.9)
subtype2	151	65.1 (11.2)
subtype3	76	63.9 (11.6)
subtype4	90	67.1 (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 = 0.109 (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	13	39	32	37	49	3	77	55	37	36
subtype1	2	3	10	6	11	8	2	10	6	4	5
subtype2	0	9	10	12	11	20	0	33	25	16	15
subtype3	0	0	8	4	9	6	0	15	15	9	7
subtype4	0	1	11	10	6	15	1	19	9	8	9

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 = 0.048 (Fisher's exact test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	21	87	174	104
subtype1	8	20	29	13
subtype2	10	35	65	43
subtype3	0	12	36	25
subtype4	3	20	44	23

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 = 0.236 (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	124	107	75	78
subtype1	30	19	11	9
subtype2	44	41	33	34
subtype3	23	20	10	21
subtype4	27	27	21	14

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 = 0.918 (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	354	24	18
subtype1	65	5	3
subtype2	142	7	6
subtype3	67	5	4
subtype4	80	7	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 = 0.568 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	151	245
subtype1	32	41
subtype2	54	101
subtype3	31	45
subtype4	34	58

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00346 (Fisher's exact test), Q value = 0.38

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	65	158	68	64	22	8	9
subtype1	8	34	15	10	3	1	2
subtype2	28	60	25	26	7	3	5
subtype3	23	31	6	7	7	0	1
subtype4	6	33	22	21	5	4	1

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 = 0.34 (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	6	390
subtype1	0	73
subtype2	4	151
subtype3	0	76
subtype4	2	90

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 = 0.771 (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	319	16	17	25
subtype1	55	2	3	8
subtype2	130	7	7	9
subtype3	58	4	2	5
subtype4	76	3	5	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 = 0.0497 (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	355	5.5 (8.1)
subtype1	63	3.9 (7.1)
subtype2	145	6.3 (8.8)
subtype3	69	5.3 (7.5)
subtype4	78	5.2 (8.2)

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 = 0.644 (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	89	7	243
subtype1	18	2	43
subtype2	27	3	98
subtype3	20	1	52
subtype4	24	1	50

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	4	5	6
Number of samples	62	43	61	49	31	56

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.0854 (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	292	95	0.1 - 122.3 (11.4)
subtype1	60	18	0.1 - 60.6 (10.2)
subtype2	43	10	0.1 - 122.3 (11.4)
subtype3	59	22	0.1 - 105.1 (12.7)
subtype4	45	11	0.1 - 72.2 (12.4)
subtype5	30	10	0.1 - 57.4 (12.9)
subtype6	55	24	0.1 - 59.5 (7.4)

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 = 0.0511 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	296	65.5 (10.8)
subtype1	61	64.3 (11.0)
subtype2	43	67.9 (8.9)
subtype3	59	66.8 (11.4)
subtype4	46	68.2 (10.5)
subtype5	31	64.9 (10.2)
subtype6	56	61.5 (11.2)

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 = 0.077 (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	6	29	29	31	41	2	64	45	30	23
subtype1	0	1	9	2	7	6	0	18	9	5	5
subtype2	0	2	4	2	2	7	0	10	10	5	1
subtype3	1	1	7	12	8	5	0	13	5	2	7
subtype4	1	2	3	7	5	10	1	6	5	7	2
subtype5	0	0	2	4	2	6	1	8	4	1	3
subtype6	0	0	4	2	7	7	0	9	12	10	5

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 = 0.00673 (Fisher's exact test), Q value = 0.71

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

nPatients	T1	T2	T3	T4
ALL	13	67	143	79
subtype1	3	11	34	14
subtype2	5	10	18	10
subtype3	2	25	26	8
subtype4	3	8	22	16
subtype5	0	6	15	10
subtype6	0	7	28	21

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 = 0.167 (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	98	78	63	62
subtype1	20	16	14	12
subtype2	9	11	15	8
subtype3	23	17	12	9
subtype4	23	11	7	8
subtype5	8	12	3	8
subtype6	15	11	12	17

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 = 0.211 (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	275	16	11
subtype1	58	3	1
subtype2	42	0	1
subtype3	55	5	1
subtype4	42	2	5
subtype5	28	1	2
subtype6	50	5	1

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 = 0.899 (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	113	189
subtype1	23	39
subtype2	13	30
subtype3	22	39
subtype4	19	30
subtype5	13	18
subtype6	23	33

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 = 5e-05 (Fisher's exact test), Q value = 0.0058

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	110	56	55	19	6	5
subtype1	1	24	16	17	2	2	0
subtype2	12	9	5	11	2	1	3
subtype3	6	23	17	10	3	2	0
subtype4	6	25	6	7	4	0	1
subtype5	6	12	5	4	3	1	0
subtype6	20	17	7	6	5	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 = 0.538 (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	5	297
subtype1	1	61
subtype2	0	43
subtype3	1	60
subtype4	2	47
subtype5	1	30
subtype6	0	56

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 = 0.299 (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	265	12	9
subtype1	56	1	2
subtype2	37	3	1
subtype3	50	3	4
subtype4	48	0	0
subtype5	28	1	0
subtype6	46	4	2

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 = 0.155 (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	277	5.4 (8.0)
subtype1	58	5.3 (9.2)
subtype2	42	6.9 (10.1)
subtype3	53	5.2 (6.9)
subtype4	45	3.6 (5.6)
subtype5	28	4.9 (6.8)
subtype6	51	6.3 (8.3)

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 = 0.765 (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	68	6	196
subtype1	18	1	36
subtype2	12	1	28
subtype3	7	1	36
subtype4	11	2	32
subtype5	8	0	21
subtype6	12	1	43

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	174	69	59

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.277 (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	292	95	0.1 - 122.3 (11.4)
subtype1	164	57	0.1 - 122.3 (11.4)
subtype2	69	24	0.1 - 115.7 (12.6)
subtype3	59	14	0.1 - 79.1 (11.3)

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 = 0.0744 (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	296	65.5 (10.8)
subtype1	168	65.9 (10.5)
subtype2	69	62.9 (11.9)
subtype3	59	67.1 (10.3)

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 = 0.485 (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	6	29	29	31	41	2	64	45	30	23
subtype1	1	4	17	19	13	25	2	42	21	16	14
subtype2	0	0	5	5	9	6	0	14	14	9	7
subtype3	1	2	7	5	9	10	0	8	10	5	2

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 = 0.213 (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	13	67	143	79
subtype1	8	41	83	42
subtype2	0	12	35	22
subtype3	5	14	25	15

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 = 0.22 (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	98	78	63	62
subtype1	56	47	37	34
subtype2	18	17	12	21
subtype3	24	14	14	7

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 = 0.589 (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	275	16	11
subtype1	158	8	8
subtype2	61	6	2
subtype3	56	2	1

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 = 0.318 (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	113	189
subtype1	60	114
subtype2	31	38
subtype3	22	37

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

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 MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA PAPILLARY TYPE	STOMACH ADENOCARCINOMA SIGNET RING TYPE
ALL	51	110	56	55	19	6	5
subtype1	20	78	34	29	8	2	3
subtype2	25	21	6	6	9	1	1
subtype3	6	11	16	20	2	3	1

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 = 0.23 (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	5	297
subtype1	5	169
subtype2	0	69
subtype3	0	59

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 = 0.0734 (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	265	12	9
subtype1	155	6	4
subtype2	56	6	2
subtype3	54	0	3

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 = 0.0993 (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	277	5.4 (8.0)
subtype1	157	5.3 (7.5)
subtype2	63	6.2 (8.1)
subtype3	57	4.8 (9.3)

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 = 0.211 (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	68	6	196
subtype1	32	4	113
subtype2	18	0	49
subtype3	18	2	34

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 = 397
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)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[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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