Correlation between aggregated molecular cancer subtypes and selected clinical features

Colorectal Adenocarcinoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1RB7398

Overview

Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 13 clinical features across 608 patients, 27 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'PATHOLOGY.M.STAGE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGY.M.STAGE' and 'NUMBER.OF.LYMPH.NODES'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'PRIMARY.SITE.OF.DISEASE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.M.STAGE', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.M.STAGE' and 'COMPLETENESS.OF.RESECTION'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'PRIMARY.SITE.OF.DISEASE' and 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 27 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.2 (1.00)	0.223 (1.00)	0.872 (1.00)	0.966 (1.00)	0.433 (1.00)	0.236 (1.00)	0.144 (1.00)	0.309 (1.00)	0.0413 (1.00)	0.743 (1.00)	0.0611 (1.00)	0.625 (1.00)
AGE	Kruskal-Wallis (anova)	0.434 (1.00)	0.0483 (1.00)	0.686 (1.00)	0.00507 (0.633)	0.955 (1.00)	0.65 (1.00)	0.00981 (1.00)	0.00622 (0.759)	0.00875 (1.00)	0.00428 (0.539)	0.126 (1.00)	0.636 (1.00)
PRIMARY SITE OF DISEASE	Fisher's exact test	0.0248 (1.00)	0.00271 (0.344)	0.00015 (0.0205)	0.00047 (0.0616)	0.828 (1.00)	0.771 (1.00)	0.0299 (1.00)	1e-05 (0.00156)	0.0398 (1.00)	0.264 (1.00)	0.00016 (0.0218)	0.00025 (0.0332)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.00531 (0.658)	0.0492 (1.00)	0.0462 (1.00)	0.759 (1.00)	0.0982 (1.00)	0.0298 (1.00)	0.00214 (0.276)	5e-05 (0.00705)	0.00012 (0.0166)	0.01 (1.00)	0.0911 (1.00)	0.076 (1.00)
PATHOLOGY T STAGE	Fisher's exact test	0.056 (1.00)	0.0624 (1.00)	0.795 (1.00)	0.75 (1.00)	0.336 (1.00)	0.0428 (1.00)	0.0762 (1.00)	0.493 (1.00)	0.104 (1.00)	0.918 (1.00)	0.0205 (1.00)	0.561 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.0172 (1.00)	0.459 (1.00)	0.00591 (0.727)	0.373 (1.00)	0.0723 (1.00)	0.0174 (1.00)	0.163 (1.00)	0.00239 (0.306)	0.0128 (1.00)	0.121 (1.00)	0.0103 (1.00)	0.00676 (0.818)
PATHOLOGY M STAGE	Fisher's exact test	0.00897 (1.00)	0.0577 (1.00)	0.0466 (1.00)	0.798 (1.00)	0.00118 (0.153)	8e-05 (0.0112)	1e-05 (0.00156)	1e-05 (0.00156)	1e-05 (0.00156)	1e-05 (0.00156)	0.0995 (1.00)	0.37 (1.00)
GENDER	Fisher's exact test	0.0618 (1.00)	0.189 (1.00)	0.921 (1.00)	0.0715 (1.00)	0.717 (1.00)	0.326 (1.00)	0.38 (1.00)	0.332 (1.00)	0.542 (1.00)	0.519 (1.00)	0.841 (1.00)	0.573 (1.00)
HISTOLOGICAL TYPE	Fisher's exact test	1e-05 (0.00156)	1e-05 (0.00156)	1e-05 (0.00156)	0.00021 (0.0281)	0.178 (1.00)	0.204 (1.00)	3e-05 (0.00429)	1e-05 (0.00156)	0.00017 (0.0229)	0.339 (1.00)	3e-05 (0.00429)	1e-05 (0.00156)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.396 (1.00)	0.654 (1.00)	0.344 (1.00)	0.0622 (1.00)	0.729 (1.00)	0.0574 (1.00)	0.44 (1.00)	0.0307 (1.00)	0.096 (1.00)	0.399 (1.00)	0.895 (1.00)	0.282 (1.00)
COMPLETENESS OF RESECTION	Fisher's exact test	0.0182 (1.00)	0.0107 (1.00)	0.0268 (1.00)	0.214 (1.00)	0.0167 (1.00)	0.0144 (1.00)	1e-05 (0.00156)	1e-05 (0.00156)	1e-05 (0.00156)	0.0001 (0.0139)	0.423 (1.00)	0.083 (1.00)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.0127 (1.00)	0.174 (1.00)	0.0112 (1.00)	0.757 (1.00)	0.0256 (1.00)	0.000274 (0.0362)	0.0862 (1.00)	0.0385 (1.00)	0.0488 (1.00)	0.127 (1.00)	0.0659 (1.00)	0.0689 (1.00)
RACE	Fisher's exact test	0.132 (1.00)	0.168 (1.00)	0.157 (1.00)	0.773 (1.00)	0.0469 (1.00)	0.762 (1.00)	0.486 (1.00)	0.572 (1.00)	0.659 (1.00)	0.79 (1.00)	0.69 (1.00)	0.121 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	46	62	72	42

'mRNA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'mRNA CNMF subtypes' versus 'AGE'

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

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

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

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

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

P value = 0.0248 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00531 (Fisher's exact test), Q value = 0.66

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

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

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

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

P value = 0.056 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	9	30	6
subtype2	1	12	41	8
subtype3	5	16	51	0
subtype4	2	9	26	5

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

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

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

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

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

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

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

P value = 0.00897 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0618 (Fisher's exact test), Q value = 1

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

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

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

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

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

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

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

P value = 0.0182 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.132 (Fisher's exact test), Q value = 1

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	52	41	77	52

'mRNA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'mRNA cHierClus subtypes' versus 'AGE'

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

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

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

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

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

P value = 0.00271 (Fisher's exact test), Q value = 0.34

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

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

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

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0492 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0624 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	9	46	148	19
subtype1	1	10	33	8
subtype2	0	7	28	6
subtype3	7	15	51	4
subtype4	1	14	36	1

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

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

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

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

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

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

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

P value = 0.0577 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.189 (Fisher's exact test), Q value = 1

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

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

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

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

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

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

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

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

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

P value = 0.0107 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

'mRNA cHierClus subtypes' versus 'RACE'

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

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

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

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

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

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

Cluster Labels	1	2	3	4
Number of samples	234	214	109	35

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

P value = 0.872 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	563	106	0.1 - 140.4 (17.9)
subtype1	224	45	0.1 - 140.4 (17.8)
subtype2	202	33	0.1 - 131.5 (19.1)
subtype3	104	22	0.5 - 119.7 (18.2)
subtype4	33	6	1.0 - 91.8 (16.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.686 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	591	66.5 (12.6)
subtype1	233	66.8 (14.1)
subtype2	214	66.5 (11.1)
subtype3	109	66.5 (12.3)
subtype4	35	64.6 (12.7)

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

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

P value = 0.00015 (Fisher's exact test), Q value = 0.021

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

nPatients	COLON	RECTUM
ALL	429	159
subtype1	193	41
subtype2	136	74
subtype3	76	33
subtype4	24	11

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

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

P value = 0.0462 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	102	1	36	165	10	3	26	22	76	52	60	25	1
subtype1	50	1	16	75	6	0	10	7	26	17	12	7	1
subtype2	30	0	8	53	3	1	10	8	37	20	29	11	0
subtype3	17	0	11	24	1	2	5	4	12	11	15	6	0
subtype4	5	0	1	13	0	0	1	3	1	4	4	1	0

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

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

P value = 0.795 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	20	105	403	62
subtype1	10	46	150	27
subtype2	8	33	151	22
subtype3	2	18	78	11
subtype4	0	8	24	2

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

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

P value = 0.00591 (Fisher's exact test), Q value = 0.73

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

nPatients	N0	N1	N2
ALL	335	146	108
subtype1	155	45	34
subtype2	103	68	42
subtype3	57	25	26
subtype4	20	8	6

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

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

P value = 0.0466 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	443	72	9	1	58
subtype1	187	15	2	1	25
subtype2	152	36	3	0	21
subtype3	78	17	3	0	10
subtype4	26	4	1	0	2

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.921 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	277	315
subtype1	113	121
subtype2	97	117
subtype3	50	59
subtype4	17	18

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

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

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

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	373	55	143	13
subtype1	146	46	31	9
subtype2	133	3	74	0
subtype3	70	6	27	4
subtype4	24	0	11	0

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

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

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

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

nPatients	NO	YES
ALL	9	583
subtype1	2	232
subtype2	3	211
subtype3	3	106
subtype4	1	34

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

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

P value = 0.0268 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	426	5	36	27
subtype1	176	3	6	13
subtype2	152	1	17	6
subtype3	72	0	11	5
subtype4	26	1	2	3

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

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

P value = 0.0112 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	558	2.2 (4.7)
subtype1	219	1.9 (4.7)
subtype2	205	2.3 (4.7)
subtype3	102	2.7 (5.1)
subtype4	32	1.8 (2.6)

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.157 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	44	292
subtype1	1	7	22	120
subtype2	0	1	16	100
subtype3	0	2	3	54
subtype4	0	2	3	18

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	98	156	119

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.966 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	364	69	0.1 - 140.4 (16.3)
subtype1	97	16	0.1 - 102.4 (15.0)
subtype2	153	31	0.1 - 139.2 (17.0)
subtype3	114	22	0.1 - 140.4 (17.3)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00507 (Kruskal-Wallis (anova)), Q value = 0.63

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

	nPatients	Mean (Std.Dev)
ALL	372	64.6 (12.9)
subtype1	97	61.2 (13.1)
subtype2	156	64.8 (12.5)
subtype3	119	67.0 (12.7)

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

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

P value = 0.00047 (Fisher's exact test), Q value = 0.062

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

nPatients	COLON	RECTUM
ALL	275	96
subtype1	68	28
subtype2	104	52
subtype3	103	16

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.759 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	52	1	20	108	7	3	11	19	54	34	26	24	1
subtype1	13	0	5	29	1	0	1	7	13	11	6	7	0
subtype2	18	0	9	41	2	2	5	10	26	13	12	12	0
subtype3	21	1	6	38	4	1	5	2	15	10	8	5	1

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

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

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

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

nPatients	T0+T1	T2	T3	T4
ALL	11	55	260	45
subtype1	3	11	68	15
subtype2	5	22	112	17
subtype3	3	22	80	13

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

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

P value = 0.373 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2
ALL	205	99	65
subtype1	52	28	17
subtype2	80	47	26
subtype3	73	24	22

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

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

P value = 0.798 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	260	38	8	1	59
subtype1	70	9	2	0	14
subtype2	105	19	5	0	26
subtype3	85	10	1	1	19

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0715 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	170	203
subtype1	38	60
subtype2	68	88
subtype3	64	55

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00021 (Fisher's exact test), Q value = 0.028

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	242	33	90	6
subtype1	56	12	27	1
subtype2	98	6	49	3
subtype3	88	15	14	2

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

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

P value = 0.0622 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	8	365
subtype1	4	94
subtype2	4	152
subtype3	0	119

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

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

P value = 0.214 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	242	4	6	28
subtype1	67	1	0	4
subtype2	101	1	2	11
subtype3	74	2	4	13

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

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

P value = 0.757 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	339	2.2 (4.8)
subtype1	89	2.6 (5.1)
subtype2	143	2.3 (5.3)
subtype3	107	1.9 (3.8)

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.773 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	45	282
subtype1	1	4	11	78
subtype2	0	4	17	118
subtype3	0	4	17	86

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	100	179	42	140

'RPPA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RPPA CNMF subtypes' versus 'AGE'

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

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

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

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

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

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

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

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

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

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0982 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.336 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	11	76	324	48
subtype1	0	13	72	14
subtype2	6	31	123	19
subtype3	0	8	32	1
subtype4	5	24	97	14

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

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

P value = 0.0723 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.00118 (Fisher's exact test), Q value = 0.15

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

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

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

'RPPA CNMF subtypes' versus 'GENDER'

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

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

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

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.178 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.729 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

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

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

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

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

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.0469 (Fisher's exact test), Q value = 1

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

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

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	54	307	100

'RPPA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RPPA cHierClus subtypes' versus 'AGE'

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

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

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

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

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

P value = 0.771 (Fisher's exact test), Q value = 1

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

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

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

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0298 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0428 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	11	76	324	48
subtype1	0	7	36	11
subtype2	7	45	223	30
subtype3	4	24	65	7

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

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

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

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

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

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

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

P value = 8e-05 (Fisher's exact test), Q value = 0.011

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

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

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

'RPPA cHierClus subtypes' versus 'GENDER'

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

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

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

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.204 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0574 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.0144 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.762 (Fisher's exact test), Q value = 1

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

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

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	128	131	141	193

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.144 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	564	106	0.1 - 140.4 (18.1)
subtype1	124	28	0.1 - 131.5 (14.4)
subtype2	123	20	0.5 - 135.7 (19.0)
subtype3	136	31	0.1 - 140.4 (17.5)
subtype4	181	27	0.9 - 117.1 (20.0)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00981 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	592	66.5 (12.6)
subtype1	128	65.6 (12.8)
subtype2	130	67.0 (13.1)
subtype3	141	64.1 (12.4)
subtype4	193	68.6 (12.0)

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

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

P value = 0.0299 (Fisher's exact test), Q value = 1

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

nPatients	COLON	RECTUM
ALL	429	160
subtype1	100	28
subtype2	103	26
subtype3	95	46
subtype4	131	60

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00214 (Fisher's exact test), Q value = 0.28

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	101	1	35	168	11	3	27	22	76	50	59	25	1
subtype1	12	0	5	44	2	0	2	5	17	17	14	6	0
subtype2	28	1	9	41	2	1	8	6	15	4	6	7	1
subtype3	23	0	8	30	2	2	4	6	22	11	15	11	0
subtype4	38	0	13	53	5	0	13	5	22	18	24	1	0

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

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

P value = 0.0762 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	20	104	404	63
subtype1	1	13	94	20
subtype2	4	28	87	11
subtype3	6	27	91	16
subtype4	9	36	132	16

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

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

P value = 0.163 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1	N2
ALL	337	146	106
subtype1	64	32	31
subtype2	85	30	16
subtype3	77	39	22
subtype4	111	45	37

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

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

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

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

nPatients	M0	M1	M1A	M1B	MX
ALL	447	71	9	1	56
subtype1	92	17	2	0	14
subtype2	98	10	0	1	21
subtype3	92	20	6	0	21
subtype4	165	24	1	0	0

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.38 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	275	318
subtype1	54	74
subtype2	58	73
subtype3	64	77
subtype4	99	94

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 3e-05 (Fisher's exact test), Q value = 0.0043

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	372	56	144	13
subtype1	83	17	25	3
subtype2	82	20	20	5
subtype3	93	2	46	0
subtype4	114	17	53	5

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

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

P value = 0.44 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	9	584
subtype1	3	125
subtype2	3	128
subtype3	2	139
subtype4	1	192

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

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

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

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

nPatients	R0	R1	R2	RX
ALL	426	5	35	28
subtype1	93	1	3	9
subtype2	88	1	2	9
subtype3	79	2	9	10
subtype4	166	1	21	0

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

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

P value = 0.0862 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	558	2.2 (4.7)
subtype1	118	3.0 (6.4)
subtype2	122	1.7 (4.2)
subtype3	127	2.0 (3.9)
subtype4	191	2.2 (4.4)

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.486 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	39	294
subtype1	1	4	11	104
subtype2	0	4	15	72
subtype3	0	4	9	99
subtype4	0	0	4	19

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	64	107	45	67	65	101	144

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.309 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	564	106	0.1 - 140.4 (18.1)
subtype1	64	17	0.1 - 100.0 (15.9)
subtype2	102	12	0.1 - 124.3 (16.9)
subtype3	43	10	0.9 - 135.7 (19.7)
subtype4	65	19	0.1 - 140.4 (16.3)
subtype5	65	10	0.3 - 129.3 (13.6)
subtype6	93	19	0.7 - 131.5 (24.0)
subtype7	132	19	0.9 - 117.1 (20.0)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00622 (Kruskal-Wallis (anova)), Q value = 0.76

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

	nPatients	Mean (Std.Dev)
ALL	592	66.5 (12.6)
subtype1	64	64.0 (14.3)
subtype2	107	65.4 (12.5)
subtype3	44	65.4 (14.8)
subtype4	67	65.3 (12.7)
subtype5	65	63.6 (10.8)
subtype6	101	69.9 (12.8)
subtype7	144	68.5 (11.0)

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

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

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

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

nPatients	COLON	RECTUM
ALL	429	160
subtype1	54	10
subtype2	72	33
subtype3	43	2
subtype4	49	18
subtype5	40	25
subtype6	81	20
subtype7	90	52

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.007

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	101	1	35	168	11	3	27	22	76	50	59	25	1
subtype1	6	0	4	21	0	0	2	3	8	10	4	4	0
subtype2	21	1	7	21	2	1	7	7	17	3	9	7	0
subtype3	9	0	3	23	1	0	1	0	2	3	1	1	1
subtype4	11	0	0	18	1	1	0	5	12	6	5	5	0
subtype5	6	0	2	18	1	1	1	2	12	6	5	7	0
subtype6	18	0	12	28	5	0	8	1	10	8	9	1	0
subtype7	30	0	7	39	1	0	8	4	15	14	26	0	0

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

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

P value = 0.493 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	20	104	404	63
subtype1	1	7	47	9
subtype2	7	21	65	13
subtype3	1	8	32	4
subtype4	1	12	46	8
subtype5	0	8	48	8
subtype6	4	16	69	12
subtype7	6	32	97	9

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

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

P value = 0.00239 (Fisher's exact test), Q value = 0.31

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

nPatients	N0	N1	N2
ALL	337	146	106
subtype1	31	15	18
subtype2	61	35	10
subtype3	36	4	5
subtype4	35	19	12
subtype5	29	22	12
subtype6	64	18	19
subtype7	81	33	30

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

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

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

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

nPatients	M0	M1	M1A	M1B	MX
ALL	447	71	9	1	56
subtype1	44	7	0	0	10
subtype2	71	12	1	0	23
subtype3	36	2	0	1	5
subtype4	45	8	2	0	11
subtype5	45	7	5	0	7
subtype6	90	9	1	0	0
subtype7	116	26	0	0	0

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.332 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	275	318
subtype1	21	43
subtype2	48	59
subtype3	23	22
subtype4	32	35
subtype5	30	35
subtype6	53	48
subtype7	68	76

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

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

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	372	56	144	13
subtype1	41	13	8	2
subtype2	66	6	31	2
subtype3	33	10	1	1
subtype4	47	2	18	0
subtype5	39	1	25	0
subtype6	57	23	11	7
subtype7	89	1	50	1

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

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

P value = 0.0307 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	9	584
subtype1	3	61
subtype2	2	105
subtype3	0	45
subtype4	0	67
subtype5	3	62
subtype6	1	100
subtype7	0	144

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

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

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

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

nPatients	R0	R1	R2	RX
ALL	426	5	35	28
subtype1	45	0	0	5
subtype2	64	2	3	6
subtype3	28	1	0	5
subtype4	36	0	2	7
subtype5	47	0	0	5
subtype6	88	0	7	0
subtype7	118	2	23	0

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

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

P value = 0.0385 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	558	2.2 (4.7)
subtype1	58	3.9 (8.2)
subtype2	98	1.8 (4.3)
subtype3	40	1.1 (2.9)
subtype4	60	2.2 (3.3)
subtype5	59	1.8 (2.5)
subtype6	101	2.3 (5.1)
subtype7	142	2.2 (4.4)

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.572 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	39	294
subtype1	1	3	5	53
subtype2	0	1	13	71
subtype3	0	3	4	36
subtype4	0	2	8	54
subtype5	0	3	4	55
subtype6	0	0	1	13
subtype7	0	0	4	12

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	212	96	241

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0413 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	520	101	0.1 - 140.4 (19.1)
subtype1	205	39	0.1 - 140.4 (17.8)
subtype2	95	22	0.1 - 100.0 (13.6)
subtype3	220	40	0.9 - 117.1 (21.0)

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

'MIRSEQ CNMF' versus 'AGE'

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

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

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

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

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

P value = 0.0398 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00012 (Fisher's exact test), Q value = 0.017

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	36	153	10	2	27	17	69	45	57	23	1
subtype1	32	1	8	66	4	2	8	10	33	13	16	11	1
subtype2	9	0	5	26	0	0	3	3	15	14	7	8	0
subtype3	53	0	23	61	6	0	16	4	21	18	34	4	0

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

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

P value = 0.104 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	56
subtype1	8	33	151	18
subtype2	2	10	69	15
subtype3	10	52	156	23

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

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

P value = 0.0128 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.542 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00017 (Fisher's exact test), Q value = 0.023

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

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

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

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

P value = 0.096 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRSEQ CNMF' versus 'RACE'

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

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

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

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3
Number of samples	229	144	176

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.743 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	520	101	0.1 - 140.4 (19.1)
subtype1	223	47	0.1 - 140.4 (16.1)
subtype2	135	25	0.1 - 139.2 (17.9)
subtype3	162	29	0.9 - 83.8 (24.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00428 (Kruskal-Wallis (anova)), Q value = 0.54

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

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

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

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

P value = 0.264 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.01 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	94	1	36	153	10	2	27	17	69	45	57	23	1
subtype1	36	0	10	77	4	1	8	9	28	18	13	14	1
subtype2	22	1	6	34	2	1	7	6	21	14	23	4	0
subtype3	36	0	20	42	4	0	12	2	20	13	21	5	0

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

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

P value = 0.918 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	20	95	376	56
subtype1	8	37	158	25
subtype2	6	22	101	14
subtype3	6	36	117	17

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

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

P value = 0.121 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.519 (Fisher's exact test), Q value = 1

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.339 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.399 (Fisher's exact test), Q value = 1

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

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

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

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

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

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

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

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

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

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.79 (Fisher's exact test), Q value = 1

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

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

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

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	101	109	85

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

P value = 0.0611 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	287	56	0.1 - 140.4 (16.1)
subtype1	95	20	0.1 - 140.4 (15.0)
subtype2	107	17	0.1 - 135.7 (18.3)
subtype3	85	19	0.2 - 129.3 (14.9)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.126 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	294	65.2 (13.0)
subtype1	101	65.3 (12.6)
subtype2	108	66.4 (13.8)
subtype3	85	63.5 (12.2)

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

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

P value = 0.00016 (Fisher's exact test), Q value = 0.022

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

nPatients	COLON	RECTUM
ALL	220	73
subtype1	62	39
subtype2	93	15
subtype3	65	19

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

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

P value = 0.0911 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	12	90	5	2	9	13	44	27	19	18	1
subtype1	16	0	2	29	2	0	4	7	14	11	6	5	0
subtype2	19	1	6	34	2	2	5	4	18	4	5	4	1
subtype3	6	0	4	27	1	0	0	2	12	12	8	9	0

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

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

P value = 0.0205 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	9	43	208	33
subtype1	6	16	69	10
subtype2	2	22	75	9
subtype3	1	5	64	14

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

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

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

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

nPatients	N0	N1	N2
ALL	162	78	51
subtype1	53	28	19
subtype2	69	30	9
subtype3	40	20	23

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

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

P value = 0.0995 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	204	27	7	1	50
subtype1	76	7	3	0	14
subtype2	73	7	1	1	25
subtype3	55	13	3	0	11

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.841 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	130	165
subtype1	42	59
subtype2	49	60
subtype3	39	46

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

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

P value = 3e-05 (Fisher's exact test), Q value = 0.0043

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	191	29	71	2
subtype1	61	1	38	1
subtype2	76	17	15	0
subtype3	54	11	18	1

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

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

P value = 0.895 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	7	288
subtype1	3	98
subtype2	2	107
subtype3	2	83

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

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

P value = 0.423 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	175	2	4	24
subtype1	54	0	2	9
subtype2	64	2	1	11
subtype3	57	0	1	4

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

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

P value = 0.0659 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	264	2.2 (4.8)
subtype1	88	2.1 (3.5)
subtype2	97	1.4 (3.5)
subtype3	79	3.2 (6.9)

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.69 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	20	244
subtype1	0	3	4	83
subtype2	0	4	9	91
subtype3	1	4	7	70

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

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	82	53	67	33	60

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

P value = 0.625 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	287	56	0.1 - 140.4 (16.1)
subtype1	79	17	0.1 - 140.4 (17.8)
subtype2	51	13	0.2 - 131.5 (16.0)
subtype3	67	13	0.2 - 129.3 (15.0)
subtype4	31	4	0.1 - 139.2 (15.0)
subtype5	59	9	0.1 - 135.7 (17.9)

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

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.636 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	294	65.2 (13.0)
subtype1	82	66.2 (12.3)
subtype2	53	66.1 (13.4)
subtype3	67	63.6 (12.2)
subtype4	33	66.5 (11.6)
subtype5	59	64.0 (14.9)

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

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

P value = 0.00025 (Fisher's exact test), Q value = 0.033

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

nPatients	COLON	RECTUM
ALL	220	73
subtype1	57	24
subtype2	35	18
subtype3	49	17
subtype4	22	11
subtype5	57	3

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

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

P value = 0.076 (Fisher's exact test), Q value = 1

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

nPatients	STAGE I	STAGE IA	STAGE II	STAGE IIA	STAGE IIB	STAGE IIC	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	41	1	12	90	5	2	9	13	44	27	19	18	1
subtype1	11	1	3	26	2	1	4	7	11	4	3	4	0
subtype2	8	0	2	8	0	1	3	2	10	4	6	5	0
subtype3	5	0	3	20	1	0	0	3	10	11	3	8	0
subtype4	5	0	1	9	1	0	1	1	7	4	3	1	0
subtype5	12	0	3	27	1	0	1	0	6	4	4	0	1

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

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

P value = 0.561 (Fisher's exact test), Q value = 1

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

nPatients	T0+T1	T2	T3	T4
ALL	9	43	208	33
subtype1	5	14	56	6
subtype2	2	7	37	6
subtype3	0	6	50	11
subtype4	1	5	23	4
subtype5	1	11	42	6

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

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

P value = 0.00676 (Fisher's exact test), Q value = 0.82

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

nPatients	N0	N1	N2
ALL	162	78	51
subtype1	49	24	8
subtype2	20	19	12
subtype3	31	18	17
subtype4	18	9	6
subtype5	44	8	8

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

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

P value = 0.37 (Fisher's exact test), Q value = 1

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

nPatients	M0	M1	M1A	M1B	MX
ALL	204	27	7	1	50
subtype1	57	5	1	0	17
subtype2	30	8	3	0	10
subtype3	46	7	3	0	10
subtype4	24	3	0	0	6
subtype5	47	4	0	1	7

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.573 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	130	165
subtype1	39	43
subtype2	21	32
subtype3	33	34
subtype4	15	18
subtype5	22	38

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

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

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

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

nPatients	COLON ADENOCARCINOMA	COLON MUCINOUS ADENOCARCINOMA	RECTAL ADENOCARCINOMA	RECTAL MUCINOUS ADENOCARCINOMA
ALL	191	29	71	2
subtype1	56	1	24	0
subtype2	32	3	17	1
subtype3	42	7	17	0
subtype4	20	2	11	0
subtype5	41	16	2	1

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

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

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

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

nPatients	NO	YES
ALL	7	288
subtype1	3	79
subtype2	0	53
subtype3	3	64
subtype4	1	32
subtype5	0	60

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

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

P value = 0.083 (Fisher's exact test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	175	2	4	24
subtype1	43	2	0	4
subtype2	29	0	3	4
subtype3	46	0	0	3
subtype4	21	0	0	4
subtype5	36	0	1	9

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

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

P value = 0.0689 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	264	2.2 (4.8)
subtype1	78	1.8 (4.1)
subtype2	45	2.5 (3.8)
subtype3	63	2.6 (4.3)
subtype4	27	3.4 (9.8)
subtype5	51	1.4 (2.9)

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.121 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	11	20	244
subtype1	0	0	5	71
subtype2	0	1	2	42
subtype3	1	2	4	58
subtype4	0	2	4	26
subtype5	0	6	5	47

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

Methods & Data

Input

Cluster data file = COADREAD-TP.mergedcluster.txt
Clinical data file = COADREAD-TP.merged_data.txt
Number of patients = 608
Number of clustering approaches = 12
Number of selected clinical features = 13
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

Download Results

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

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[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)

Made with Nozzle