Correlation between aggregated molecular cancer subtypes and selected clinical features

Ovarian Serous Cystadenocarcinoma (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/C1HD7TF8

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 13 different clustering approaches and 8 clinical features across 578 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based miR expression data identified 10 subtypes that correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.
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 'Time to Death'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 13 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.


Clinical Features	Time to Death	AGE	PRIMARY SITE OF DISEASE	KARNOFSKY PERFORMANCE SCORE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.0159 (1.00)	0.00831 (0.764)	0.43 (1.00)	0.14 (1.00)	0.794 (1.00)	0.152 (1.00)	0.058 (1.00)	0.154 (1.00)
mRNA cHierClus subtypes	0.253 (1.00)	0.0223 (1.00)	0.24 (1.00)	0.407 (1.00)	0.125 (1.00)	0.154 (1.00)	0.757 (1.00)	0.0634 (1.00)
miR CNMF subtypes	0.0391 (1.00)	0.691 (1.00)	0.22 (1.00)	0.669 (1.00)	0.262 (1.00)	0.211 (1.00)	0.286 (1.00)	0.236 (1.00)
miR cHierClus subtypes	0.106 (1.00)	0.00044 (0.0418)	0.803 (1.00)	0.903 (1.00)	0.361 (1.00)	0.263 (1.00)	0.908 (1.00)	0.441 (1.00)
Copy Number Ratio CNMF subtypes	0.203 (1.00)	5.1e-10 (4.94e-08)	0.33 (1.00)	0.8 (1.00)	0.411 (1.00)	0.276 (1.00)	0.741 (1.00)	0.337 (1.00)
RPPA CNMF subtypes	0.0245 (1.00)	0.476 (1.00)	0.771 (1.00)	0.743 (1.00)	0.133 (1.00)	0.341 (1.00)	0.887 (1.00)	0.898 (1.00)
RPPA cHierClus subtypes	0.00165 (0.153)	0.0367 (1.00)	0.694 (1.00)	0.253 (1.00)	1 (1.00)	0.88 (1.00)	0.0739 (1.00)	0.0948 (1.00)
RNAseq CNMF subtypes	0.416 (1.00)	0.00134 (0.126)	1 (1.00)		1 (1.00)		0.685 (1.00)	0.47 (1.00)
RNAseq cHierClus subtypes	0.645 (1.00)	0.0889 (1.00)	0.552 (1.00)	0.33 (1.00)	0.265 (1.00)		0.703 (1.00)	0.776 (1.00)
MIRSEQ CNMF	0.301 (1.00)	0.506 (1.00)	0.0206 (1.00)	0.235 (1.00)	0.49 (1.00)		0.552 (1.00)	0.0471 (1.00)
MIRSEQ CHIERARCHICAL	0.28 (1.00)	0.000116 (0.0112)	0.869 (1.00)	0.677 (1.00)	0.684 (1.00)		0.813 (1.00)	0.772 (1.00)
MIRseq Mature CNMF subtypes	0.0657 (1.00)	0.361 (1.00)	0.147 (1.00)	0.331 (1.00)	0.0516 (1.00)		0.424 (1.00)	0.722 (1.00)
MIRseq Mature cHierClus subtypes	0.721 (1.00)	0.625 (1.00)	0.257 (1.00)	0.539 (1.00)	0.124 (1.00)		0.912 (1.00)	0.741 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	219	214	129

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0159 (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	557	293	0.3 - 180.2 (28.6)
subtype1	216	124	0.3 - 152.0 (30.2)
subtype2	212	93	0.4 - 180.2 (28.2)
subtype3	129	76	0.3 - 119.1 (25.9)

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 = 0.00831 (Kruskal-Wallis (anova)), Q value = 0.76

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

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	213	61.4 (11.6)
subtype2	210	57.9 (11.4)
subtype3	128	60.0 (11.7)

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 = 0.43 (Fisher's exact test), Q value = 1

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

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	558	2
subtype1	0	219	0
subtype2	1	212	1
subtype3	1	127	1

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

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.14 (Kruskal-Wallis (anova)), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	31	78.1 (13.0)
subtype2	28	76.4 (12.2)
subtype3	19	70.5 (12.2)

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

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

P value = 0.794 (Fisher's exact test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	2	217
subtype2	1	213
subtype3	0	129

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.152 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	6	9	0
subtype2	7	9	0
subtype3	1	9	1

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.058 (Fisher's exact test), Q value = 1

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	19	24	1	485
subtype1	0	7	12	0	186
subtype2	0	11	9	1	181
subtype3	2	1	3	0	118

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.154 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	328
subtype1	6	130
subtype2	5	120
subtype3	0	78

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	313	160	89

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.253 (logrank test), Q value = 1

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	557	293	0.3 - 180.2 (28.6)
subtype1	309	155	0.3 - 130.0 (28.6)
subtype2	160	87	0.3 - 180.2 (24.7)
subtype3	88	51	1.0 - 152.0 (32.1)

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0223 (Kruskal-Wallis (anova)), Q value = 1

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	551	59.7 (11.6)
subtype1	307	59.5 (11.4)
subtype2	157	58.4 (12.0)
subtype3	87	62.9 (11.3)

Figure S10. 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 = 0.24 (Fisher's exact test), Q value = 1

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	558	2
subtype1	1	312	0
subtype2	1	157	2
subtype3	0	89	0

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.407 (Kruskal-Wallis (anova)), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	43	76.7 (11.5)
subtype2	21	72.4 (13.4)
subtype3	14	77.1 (15.4)

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.125 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	1	312
subtype2	0	160
subtype3	2	87

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.154 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	8	11	0
subtype2	2	12	1
subtype3	4	4	0

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.757 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	19	24	1	485
subtype1	0	10	14	1	273
subtype2	2	6	6	0	136
subtype3	0	3	4	0	76

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0634 (Fisher's exact test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	328
subtype1	8	178
subtype2	0	93
subtype3	3	57

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S19. Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	157	165	238

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.0391 (logrank test), Q value = 1

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	293	0.3 - 180.2 (28.5)
subtype1	156	88	0.3 - 130.0 (27.3)
subtype2	162	92	0.3 - 115.9 (24.7)
subtype3	237	113	0.3 - 180.2 (30.4)

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.691 (Kruskal-Wallis (anova)), Q value = 1

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	549	59.7 (11.6)
subtype1	153	59.3 (12.5)
subtype2	162	59.3 (11.6)
subtype3	234	60.3 (11.1)

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.22 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	556	2
subtype1	0	155	2
subtype2	1	164	0
subtype3	1	237	0

Figure S19. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.669 (Kruskal-Wallis (anova)), Q value = 1

Table S23. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	24	76.7 (12.7)
subtype2	23	73.9 (15.3)
subtype3	31	76.1 (10.9)

Figure S20. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S24. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	557
subtype1	0	157
subtype2	0	165
subtype3	3	235

Figure S21. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.211 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	4	12	0
subtype2	3	9	1
subtype3	7	6	0

Figure S22. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'miR CNMF subtypes' versus 'RACE'

P value = 0.286 (Fisher's exact test), Q value = 1

Table S26. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	19	23	1	484
subtype1	0	2	8	0	136
subtype2	2	6	5	0	143
subtype3	0	11	10	1	205

Figure S23. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #7: 'RACE'

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 0.236 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	327
subtype1	5	86
subtype2	1	97
subtype3	5	144

Figure S24. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S28. Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels	1	10	2	3	4	5	6	7	8	9
Number of samples	82	57	58	86	35	75	36	36	51	44

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.106 (logrank test), Q value = 1

Table S29. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	555	293	0.3 - 180.2 (28.5)
subtype1	82	46	0.3 - 130.0 (25.4)
subtype10	56	28	0.8 - 115.9 (25.6)
subtype2	56	35	0.3 - 106.0 (26.0)
subtype3	86	42	0.3 - 125.8 (30.6)
subtype4	35	25	1.2 - 101.8 (22.2)
subtype5	74	36	1.7 - 180.2 (34.3)
subtype6	36	19	1.0 - 73.5 (32.0)
subtype7	36	13	0.5 - 98.0 (30.0)
subtype8	50	25	0.5 - 152.0 (21.6)
subtype9	44	24	1.0 - 107.2 (29.7)

Figure S25. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.00044 (Kruskal-Wallis (anova)), Q value = 0.042

Table S30. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	549	59.7 (11.6)
subtype1	79	58.6 (12.2)
subtype10	56	55.6 (11.2)
subtype2	58	61.7 (12.3)
subtype3	85	59.3 (11.2)
subtype4	35	60.3 (11.7)
subtype5	74	60.0 (10.0)
subtype6	35	63.3 (10.9)
subtype7	35	62.9 (12.0)
subtype8	49	63.6 (11.4)
subtype9	43	54.3 (11.3)

Figure S26. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.803 (Fisher's exact test), Q value = 1

Table S31. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	556	2
subtype1	0	81	1
subtype10	0	57	0
subtype2	0	58	0
subtype3	1	85	0
subtype4	0	34	1
subtype5	1	74	0
subtype6	0	36	0
subtype7	0	36	0
subtype8	0	51	0
subtype9	0	44	0

Figure S27. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.903 (Kruskal-Wallis (anova)), Q value = 1

Table S32. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	75.6 (12.8)
subtype1	13	73.8 (15.0)
subtype10	6	73.3 (16.3)
subtype2	4	75.0 (10.0)
subtype3	13	80.0 (11.5)
subtype4	3	73.3 (11.5)
subtype5	12	76.7 (16.7)
subtype6	7	77.1 (13.8)
subtype7	6	76.7 (8.2)
subtype8	5	68.0 (11.0)
subtype9	9	75.6 (8.8)

Figure S28. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.361 (Fisher's exact test), Q value = 1

Table S33. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	557
subtype1	0	82
subtype10	0	57
subtype2	0	58
subtype3	0	86
subtype4	0	35
subtype5	1	74
subtype6	0	36
subtype7	1	35
subtype8	1	50
subtype9	0	44

Figure S29. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.263 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	27	1
subtype1	2	7	0
subtype10	2	1	0
subtype2	1	2	0
subtype3	2	4	0
subtype4	0	5	0
subtype5	1	6	0
subtype6	1	1	0
subtype7	2	1	0
subtype8	2	0	0
subtype9	1	0	1

Figure S30. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.908 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	2	19	23	1	484
subtype1	0	1	3	0	71
subtype10	1	2	4	0	47
subtype2	0	2	5	0	47
subtype3	0	4	4	1	73
subtype4	0	0	1	0	33
subtype5	1	3	2	0	66
subtype6	0	1	0	0	34
subtype7	0	1	1	0	32
subtype8	0	4	1	0	42
subtype9	0	1	2	0	39

Figure S31. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

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

Table S36. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	11	327
subtype1	3	45
subtype10	0	36
subtype2	0	24
subtype3	3	56
subtype4	1	18
subtype5	1	44
subtype6	2	23
subtype7	1	19
subtype8	0	34
subtype9	0	28

Figure S32. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S37. Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	173	201	188

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

P value = 0.203 (logrank test), Q value = 1

Table S38. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	547	287	0.3 - 180.2 (28.5)
subtype1	168	97	0.8 - 119.1 (29.8)
subtype2	196	92	0.3 - 130.0 (30.1)
subtype3	183	98	0.3 - 180.2 (22.3)

Figure S33. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 5.1e-10 (Kruskal-Wallis (anova)), Q value = 4.9e-08

Table S39. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	541	59.8 (11.6)
subtype1	165	60.0 (12.1)
subtype2	197	56.1 (11.0)
subtype3	179	63.7 (10.6)

Figure S34. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.33 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	548	2
subtype1	1	169	0
subtype2	1	197	0
subtype3	0	182	2

Figure S35. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.8 (Kruskal-Wallis (anova)), Q value = 1

Table S41. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	76	75.5 (12.9)
subtype1	21	76.2 (10.2)
subtype2	28	73.6 (13.4)
subtype3	27	77.0 (14.4)

Figure S36. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.411 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	559
subtype1	1	172
subtype2	0	201
subtype3	2	186

Figure S37. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.276 (Fisher's exact test), Q value = 1

Table S43. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	14	26	2
subtype1	2	11	0
subtype2	4	6	1
subtype3	8	9	1

Figure S38. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.741 (Fisher's exact test), Q value = 1

Table S44. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	3	18	24	1	475
subtype1	1	4	6	0	148
subtype2	0	8	11	1	167
subtype3	2	6	7	0	160

Figure S39. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.337 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	10	319
subtype1	1	105
subtype2	5	109
subtype3	4	105

Figure S40. Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #6: 'RPPA CNMF subtypes'

Table S46. Description of clustering approach #6: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	123	199	85

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0245 (logrank test), Q value = 1

Table S47. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	401	213	0.3 - 180.2 (28.7)
subtype1	120	66	0.4 - 125.8 (31.1)
subtype2	197	92	0.3 - 180.2 (25.1)
subtype3	84	55	0.5 - 89.3 (27.2)

Figure S41. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.476 (Kruskal-Wallis (anova)), Q value = 1

Table S48. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	121	60.0 (12.4)
subtype2	196	59.2 (11.7)
subtype3	83	60.4 (11.4)

Figure S42. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S49. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	403	2
subtype1	1	122	0
subtype2	1	197	1
subtype3	0	84	1

Figure S43. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.743 (Kruskal-Wallis (anova)), Q value = 1

Table S50. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	51	74.9 (11.9)
subtype1	21	76.2 (10.2)
subtype2	24	75.0 (12.2)
subtype3	6	70.0 (16.7)

Figure S44. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.133 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	405
subtype1	2	121
subtype2	0	199
subtype3	0	85

Figure S45. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

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

Table S52. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	13	28	2
subtype1	2	10	0
subtype2	8	9	1
subtype3	3	9	1

Figure S46. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.887 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	16	19	342
subtype1	2	5	6	100
subtype2	1	9	10	169
subtype3	0	2	3	73

Figure S47. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.898 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	207
subtype1	2	65
subtype2	5	102
subtype3	1	40

Figure S48. Get High-res Image Clustering Approach #6: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'RPPA cHierClus subtypes'

Table S55. Description of clustering approach #7: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	120	98	130	59

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.15

Table S56. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	401	213	0.3 - 180.2 (28.7)
subtype1	119	77	0.4 - 125.8 (25.9)
subtype2	95	43	0.3 - 115.9 (28.4)
subtype3	128	59	0.5 - 152.0 (26.4)
subtype4	59	34	0.8 - 180.2 (36.2)

Figure S49. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0367 (Kruskal-Wallis (anova)), Q value = 1

Table S57. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	400	59.7 (11.8)
subtype1	119	60.7 (12.0)
subtype2	96	58.3 (12.7)
subtype3	127	58.5 (11.1)
subtype4	58	62.5 (11.2)

Figure S50. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.694 (Fisher's exact test), Q value = 1

Table S58. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	403	2
subtype1	1	118	1
subtype2	0	98	0
subtype3	0	129	1
subtype4	1	58	0

Figure S51. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.253 (Kruskal-Wallis (anova)), Q value = 1

Table S59. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	51	74.9 (11.9)
subtype1	9	75.6 (8.8)
subtype2	6	83.3 (8.2)
subtype3	28	74.3 (12.0)
subtype4	8	70.0 (15.1)

Figure S52. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S60. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	405
subtype1	1	119
subtype2	0	98
subtype3	1	129
subtype4	0	59

Figure S53. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.88 (Fisher's exact test), Q value = 1

Table S61. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	13	28	2
subtype1	3	11	1
subtype2	4	4	0
subtype3	5	10	1
subtype4	1	3	0

Figure S54. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0739 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	3	16	19	342
subtype1	1	2	5	105
subtype2	0	4	1	85
subtype3	2	9	7	103
subtype4	0	1	6	49

Figure S55. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0948 (Fisher's exact test), Q value = 1

Table S63. Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	207
subtype1	0	64
subtype2	1	44
subtype3	5	75
subtype4	2	24

Figure S56. Get High-res Image Clustering Approach #7: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #8: 'RNAseq CNMF subtypes'

Table S64. Description of clustering approach #8: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	102	67	92

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.416 (logrank test), Q value = 1

Table S65. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	259	147	0.3 - 180.2 (28.2)
subtype1	102	55	0.4 - 180.2 (28.5)
subtype2	66	36	1.0 - 106.0 (34.6)
subtype3	91	56	0.3 - 152.0 (25.0)

Figure S57. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00134 (Kruskal-Wallis (anova)), Q value = 0.13

Table S66. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	59.0 (10.8)
subtype1	99	59.2 (10.3)
subtype2	66	55.2 (10.6)
subtype3	89	61.5 (10.9)

Figure S58. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

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

Table S67. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	260
subtype1	1	101
subtype2	0	67
subtype3	0	92

Figure S59. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

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

Table S68. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	259
subtype1	1	101
subtype2	0	67
subtype3	1	91

Figure S60. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.685 (Fisher's exact test), Q value = 1

Table S69. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	1	12	16	1	222
subtype1	1	6	5	0	86
subtype2	0	3	3	1	58
subtype3	0	3	8	0	78

Figure S61. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.47 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	137
subtype1	2	53
subtype2	1	34
subtype3	0	50

Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'RNAseq cHierClus subtypes'

Table S71. Description of clustering approach #9: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	107	117	37

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.645 (logrank test), Q value = 1

Table S72. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	259	147	0.3 - 180.2 (28.2)
subtype1	106	55	0.4 - 180.2 (27.7)
subtype2	116	68	0.3 - 125.8 (30.3)
subtype3	37	24	1.0 - 152.0 (25.7)

Figure S63. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0889 (Kruskal-Wallis (anova)), Q value = 1

Table S73. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	254	59.0 (10.8)
subtype1	105	59.0 (10.6)
subtype2	113	57.6 (10.5)
subtype3	36	62.9 (11.9)

Figure S64. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.552 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY
ALL	1	260
subtype1	1	106
subtype2	0	117
subtype3	0	37

Figure S65. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.33 (Kruskal-Wallis (anova)), Q value = 1

Table S75. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	14	74.3 (12.2)
subtype1	11	72.7 (13.5)
subtype2	3	80.0 (0.0)

Figure S66. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.265 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	2	259
subtype1	0	107
subtype2	1	116
subtype3	1	36

Figure S67. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.703 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER	WHITE
ALL	1	12	16	1	222
subtype1	1	6	5	0	90
subtype2	0	4	7	1	102
subtype3	0	2	4	0	30

Figure S68. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.776 (Fisher's exact test), Q value = 1

Table S78. Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	137
subtype1	2	54
subtype2	1	59
subtype3	0	24

Figure S69. Get High-res Image Clustering Approach #9: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CNMF'

Table S79. Description of clustering approach #10: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	124	185	144

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.301 (logrank test), Q value = 1

Table S80. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	124	78	0.3 - 152.0 (28.6)
subtype2	184	98	0.3 - 180.2 (32.1)
subtype3	142	76	0.3 - 130.0 (28.7)

Figure S70. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.506 (Kruskal-Wallis (anova)), Q value = 1

Table S81. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	123	60.7 (11.7)
subtype2	182	59.4 (11.3)
subtype3	140	59.4 (11.6)

Figure S71. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.0206 (Fisher's exact test), Q value = 1

Table S82. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	2	121	1
subtype2	0	185	0
subtype3	0	144	0

Figure S72. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.235 (Kruskal-Wallis (anova)), Q value = 1

Table S83. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	11	81.8 (14.0)
subtype2	45	74.2 (12.5)
subtype3	8	72.5 (14.9)

Figure S73. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.49 (Fisher's exact test), Q value = 1

Table S84. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	124
subtype2	1	184
subtype3	2	142

Figure S74. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.552 (Fisher's exact test), Q value = 1

Table S85. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	21	400
subtype1	2	3	6	106
subtype2	0	6	10	166
subtype3	0	6	5	128

Figure S75. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0471 (Fisher's exact test), Q value = 1

Table S86. Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	262
subtype1	0	66
subtype2	7	113
subtype3	1	83

Figure S76. Get High-res Image Clustering Approach #10: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CHIERARCHICAL'

Table S87. Description of clustering approach #11: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	105	73	169	106

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.28 (logrank test), Q value = 1

Table S88. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	105	53	0.3 - 107.2 (32.8)
subtype2	72	45	1.0 - 89.3 (28.0)
subtype3	168	99	0.3 - 180.2 (28.3)
subtype4	105	55	0.3 - 130.0 (30.5)

Figure S77. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000116 (Kruskal-Wallis (anova)), Q value = 0.011

Table S89. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	104	56.9 (11.5)
subtype2	73	62.5 (11.8)
subtype3	166	61.8 (11.1)
subtype4	102	57.4 (10.9)

Figure S78. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

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

P value = 0.869 (Fisher's exact test), Q value = 1

Table S90. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	0	105	0
subtype2	0	73	0
subtype3	2	166	1
subtype4	0	106	0

Figure S79. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.677 (Kruskal-Wallis (anova)), Q value = 1

Table S91. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	28	76.4 (11.0)
subtype2	6	70.0 (21.0)
subtype3	26	74.6 (14.5)
subtype4	4	80.0 (0.0)

Figure S80. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.684 (Fisher's exact test), Q value = 1

Table S92. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	105
subtype2	1	72
subtype3	1	168
subtype4	1	105

Figure S81. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.813 (Fisher's exact test), Q value = 1

Table S93. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	21	400
subtype1	0	6	4	94
subtype2	0	3	4	61
subtype3	1	3	8	151
subtype4	1	3	5	94

Figure S82. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.772 (Fisher's exact test), Q value = 1

Table S94. Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	262
subtype1	3	70
subtype2	2	45
subtype3	2	90
subtype4	1	57

Figure S83. Get High-res Image Clustering Approach #11: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature CNMF subtypes'

Table S95. Description of clustering approach #12: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	98	198	157

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

P value = 0.0657 (logrank test), Q value = 1

Table S96. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	98	69	0.3 - 152.0 (28.6)
subtype2	197	100	0.3 - 180.2 (31.8)
subtype3	155	83	0.3 - 130.0 (29.2)

Figure S84. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.361 (Kruskal-Wallis (anova)), Q value = 1

Table S97. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	97	61.1 (12.0)
subtype2	195	59.4 (11.3)
subtype3	153	59.4 (11.5)

Figure S85. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.147 (Fisher's exact test), Q value = 1

Table S98. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	1	96	1
subtype2	1	197	0
subtype3	0	157	0

Figure S86. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.331 (Kruskal-Wallis (anova)), Q value = 1

Table S99. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	8	82.5 (16.7)
subtype2	48	74.6 (12.2)
subtype3	8	72.5 (14.9)

Figure S87. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.0516 (Fisher's exact test), Q value = 1

Table S100. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	98
subtype2	0	198
subtype3	3	154

Figure S88. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.424 (Fisher's exact test), Q value = 1

Table S101. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	21	400
subtype1	2	3	6	84
subtype2	0	6	9	177
subtype3	0	6	6	139

Figure S89. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.722 (Fisher's exact test), Q value = 1

Table S102. Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	262
subtype1	1	54
subtype2	5	119
subtype3	2	89

Figure S90. Get High-res Image Clustering Approach #12: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature cHierClus subtypes'

Table S103. Description of clustering approach #13: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	173	176	104

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

P value = 0.721 (logrank test), Q value = 1

Table S104. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	450	252	0.3 - 180.2 (30.1)
subtype1	172	102	0.3 - 152.0 (29.2)
subtype2	174	96	0.3 - 130.0 (30.2)
subtype3	104	54	0.3 - 180.2 (31.7)

Figure S91. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.625 (Kruskal-Wallis (anova)), Q value = 1

Table S105. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	445	59.8 (11.5)
subtype1	171	59.7 (11.5)
subtype2	172	59.2 (11.6)
subtype3	102	60.7 (11.3)

Figure S92. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.257 (Fisher's exact test), Q value = 1

Table S106. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients	OMENTUM	OVARY	PERITONEUM OVARY
ALL	2	450	1
subtype1	2	170	1
subtype2	0	176	0
subtype3	0	104	0

Figure S93. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.539 (Kruskal-Wallis (anova)), Q value = 1

Table S107. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	64	75.3 (13.2)
subtype1	27	75.6 (11.5)
subtype2	11	78.2 (16.6)
subtype3	26	73.8 (13.6)

Figure S94. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.124 (Fisher's exact test), Q value = 1

Table S108. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	3	450
subtype1	0	173
subtype2	3	173
subtype3	0	104

Figure S95. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.912 (Fisher's exact test), Q value = 1

Table S109. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	21	400
subtype1	1	4	8	152
subtype2	1	6	7	157
subtype3	0	5	6	91

Figure S96. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.741 (Fisher's exact test), Q value = 1

Table S110. Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	262
subtype1	4	99
subtype2	2	101
subtype3	2	62

Figure S97. Get High-res Image Clustering Approach #13: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data

Input

Cluster data file = OV-TP.mergedcluster.txt
Clinical data file = OV-TP.merged_data.txt
Number of patients = 578
Number of clustering approaches = 13
Number of selected clinical features = 8
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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