Correlation between aggregated molecular cancer subtypes and selected clinical features

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 8 clinical features across 515 patients, 48 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 correlate to 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'GENDER', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', and 'HISTOLOGICAL_TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'RACE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 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, 48 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RADIATION THERAPY	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.374 (0.479)	0.247 (0.37)	0.102 (0.179)	0.697 (0.76)	0.475 (0.562)	0.0232 (0.0518)	0.514 (0.595)	1 (1.00)
mRNA cHierClus subtypes	0.0394 (0.0805)	0.0787 (0.143)	0.149 (0.246)	0.841 (0.849)	0.364 (0.473)	0.00846 (0.0193)	0.222 (0.343)	0.427 (0.526)
Copy Number Ratio CNMF subtypes	1.21e-14 (1.94e-13)	2.72e-12 (2.61e-11)	0.809 (0.835)	1e-05 (3.69e-05)	0.000486 (0.00142)	1e-05 (3.69e-05)	0.00325 (0.00807)	0.793 (0.835)
METHLYATION CNMF	1.67e-15 (3.2e-14)	4.35e-13 (4.64e-12)	0.305 (0.412)	1e-05 (3.69e-05)	0.00129 (0.00354)	1e-05 (3.69e-05)	0.0385 (0.0803)	0.619 (0.685)
RPPA CNMF subtypes	0.000357 (0.00111)	0.0502 (0.0983)	0.00328 (0.00807)	0.391 (0.494)	0.155 (0.253)	0.0011 (0.00311)	0.48 (0.562)	0.552 (0.631)
RPPA cHierClus subtypes	0.00217 (0.00563)	0.0687 (0.127)	0.406 (0.506)	0.065 (0.122)	0.458 (0.557)	1e-05 (3.69e-05)	0.756 (0.806)	0.284 (0.395)
RNAseq CNMF subtypes	0 (0)	4.21e-11 (3.67e-10)	0.708 (0.764)	1e-05 (3.69e-05)	0.00138 (0.00368)	1e-05 (3.69e-05)	0.329 (0.439)	0.121 (0.203)
RNAseq cHierClus subtypes	0 (0)	1.45e-15 (3.2e-14)	0.282 (0.395)	1e-05 (3.69e-05)	0.000445 (0.00134)	1e-05 (3.69e-05)	0.177 (0.283)	0.834 (0.849)
MIRSEQ CNMF	0.0253 (0.0539)	0.804 (0.835)	0.621 (0.685)	0.00551 (0.0129)	0.0898 (0.16)	1e-05 (3.69e-05)	0.302 (0.412)	0.259 (0.377)
MIRSEQ CHIERARCHICAL	3.47e-14 (4.77e-13)	1.78e-08 (1.43e-07)	0.283 (0.395)	2e-05 (7.11e-05)	0.000189 (0.000606)	1e-05 (3.69e-05)	0.562 (0.635)	0.235 (0.357)
MIRseq Mature CNMF subtypes	0.000159 (0.000528)	0.112 (0.192)	0.473 (0.562)	5e-05 (0.000171)	0.0239 (0.0522)	1e-05 (3.69e-05)	0.348 (0.457)	0.0636 (0.122)
MIRseq Mature cHierClus subtypes	0 (0)	1.88e-13 (2.26e-12)	0.217 (0.342)	1e-05 (3.69e-05)	0.00414 (0.00993)	1e-05 (3.69e-05)	0.0453 (0.0907)	0.259 (0.377)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	9	10	8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.374 (logrank test), Q value = 0.48

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	26	11	0.3 - 134.3 (52.2)
subtype1	9	5	36.4 - 130.8 (60.1)
subtype2	9	3	0.3 - 78.2 (41.1)
subtype3	8	3	21.0 - 134.3 (75.6)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 0.37

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	9	39.2 (6.2)
subtype2	10	42.3 (7.6)
subtype3	8	35.8 (12.6)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.102 (Fisher's exact test), Q value = 0.18

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	18
subtype1	2	7
subtype2	6	4
subtype3	1	7

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.697 (Fisher's exact test), Q value = 0.76

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

nPatients	NO	YES
ALL	5	21
subtype1	2	6
subtype2	1	9
subtype3	2	6

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.475 (Kruskal-Wallis (anova)), Q value = 0.56

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

	nPatients	Mean (Std.Dev)
ALL	17	87.6 (13.5)
subtype1	7	82.9 (18.0)
subtype2	7	92.9 (7.6)
subtype3	3	86.7 (11.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0232 (Fisher's exact test), Q value = 0.052

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	7	2	0
subtype2	2	3	5
subtype3	1	4	3

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.514 (Fisher's exact test), Q value = 0.59

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

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	25
subtype1	1	8
subtype2	0	10
subtype3	1	7

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 1 (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	1	20
subtype1	0	6
subtype2	1	7
subtype3	0	7

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	4
Number of samples	7	7	7	6

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0394 (logrank test), Q value = 0.08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	26	11	0.3 - 134.3 (52.2)
subtype1	7	4	21.0 - 82.0 (50.1)
subtype2	7	5	20.0 - 130.8 (47.9)
subtype3	6	1	0.3 - 78.2 (42.7)
subtype4	6	1	46.1 - 134.3 (84.7)

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

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0787 (Kruskal-Wallis (anova)), Q value = 0.14

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	7	41.7 (5.3)
subtype2	7	36.3 (4.0)
subtype3	7	43.9 (8.6)
subtype4	6	34.8 (14.6)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.149 (Fisher's exact test), Q value = 0.25

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	9	18
subtype1	1	6
subtype2	2	5
subtype3	5	2
subtype4	1	5

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.841 (Fisher's exact test), Q value = 0.85

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

nPatients	NO	YES
ALL	5	21
subtype1	1	6
subtype2	1	5
subtype3	1	6
subtype4	2	4

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

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.364 (Kruskal-Wallis (anova)), Q value = 0.47

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

	nPatients	Mean (Std.Dev)
ALL	17	87.6 (13.5)
subtype1	5	86.0 (13.4)
subtype2	5	82.0 (17.9)
subtype3	5	94.0 (8.9)
subtype4	2	90.0 (14.1)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00846 (Fisher's exact test), Q value = 0.019

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	5	1	1
subtype2	4	3	0
subtype3	1	1	5
subtype4	0	4	2

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

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.222 (Fisher's exact test), Q value = 0.34

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

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	25
subtype1	2	5
subtype2	0	7
subtype3	0	7
subtype4	0	6

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.427 (Fisher's exact test), Q value = 0.53

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	20
subtype1	0	4
subtype2	0	6
subtype3	1	4
subtype4	0	6

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

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

Table S19. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	226	111	175

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

P value = 1.21e-14 (logrank test), Q value = 1.9e-13

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	508	125	0.0 - 211.2 (22.4)
subtype1	226	44	0.1 - 172.8 (24.3)
subtype2	110	60	0.2 - 211.2 (19.3)
subtype3	172	21	0.0 - 182.3 (23.2)

Figure S17. 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 'YEARS_TO_BIRTH'

P value = 2.72e-12 (Kruskal-Wallis (anova)), Q value = 2.6e-11

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	511	43.0 (13.3)
subtype1	226	38.4 (12.3)
subtype2	111	48.8 (13.6)
subtype3	174	45.2 (12.5)

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.809 (Fisher's exact test), Q value = 0.84

Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	228	284
subtype1	97	129
subtype2	51	60
subtype3	80	95

Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	295
subtype1	69	142
subtype2	21	82
subtype3	94	71

Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000486 (Kruskal-Wallis (anova)), Q value = 0.0014

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	306	86.7 (12.6)
subtype1	131	88.3 (11.0)
subtype2	71	81.3 (14.6)
subtype3	104	88.3 (12.1)

Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	194	129	189
subtype1	129	68	29
subtype2	57	21	33
subtype3	8	40	127

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.00325 (Fisher's exact test), Q value = 0.0081

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	472
subtype1	0	2	4	216
subtype2	1	1	11	98
subtype3	0	5	6	158

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.793 (Fisher's exact test), Q value = 0.84

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	446
subtype1	13	198
subtype2	6	97
subtype3	13	151

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

Clustering Approach #4: 'METHLYATION CNMF'

Table S28. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	208	136	158	13

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.67e-15 (logrank test), Q value = 3.2e-14

Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	125	0.0 - 211.2 (22.3)
subtype1	208	41	0.0 - 172.8 (27.7)
subtype2	136	58	0.1 - 211.2 (17.9)
subtype3	155	20	0.1 - 182.3 (23.2)
subtype4	12	6	2.3 - 51.5 (19.6)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.35e-13 (Kruskal-Wallis (anova)), Q value = 4.6e-12

Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	514	42.9 (13.4)
subtype1	208	37.7 (11.2)
subtype2	136	48.9 (14.5)
subtype3	157	45.0 (12.8)
subtype4	13	39.2 (9.1)

Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.305 (Fisher's exact test), Q value = 0.41

Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	285
subtype1	89	119
subtype2	63	73
subtype3	69	89
subtype4	9	4

Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	186	296
subtype1	62	135
subtype2	33	91
subtype3	88	61
subtype4	3	9

Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00129 (Kruskal-Wallis (anova)), Q value = 0.0035

Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	307	86.6 (12.6)
subtype1	130	88.0 (11.8)
subtype2	77	82.9 (13.6)
subtype3	90	88.7 (12.1)
subtype4	10	80.0 (12.5)

Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	194	130	191
subtype1	109	67	32
subtype2	70	27	39
subtype3	4	35	119
subtype4	11	1	1

Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RACE'

P value = 0.0385 (Fisher's exact test), Q value = 0.08

Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	475
subtype1	0	1	6	199
subtype2	1	2	11	120
subtype3	0	4	4	144
subtype4	0	1	0	12

Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.619 (Fisher's exact test), Q value = 0.68

Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	449
subtype1	14	178
subtype2	6	122
subtype3	12	136
subtype4	0	13

Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S37. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	90	95	126	117

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000357 (logrank test), Q value = 0.0011

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	424	98	0.0 - 211.2 (21.0)
subtype1	88	17	0.0 - 117.5 (23.6)
subtype2	94	37	0.1 - 156.2 (19.5)
subtype3	126	22	0.1 - 211.2 (24.8)
subtype4	116	22	0.1 - 154.4 (18.6)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0502 (Kruskal-Wallis (anova)), Q value = 0.098

Table S39. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	427	42.7 (13.3)
subtype1	90	39.2 (12.0)
subtype2	95	44.0 (13.9)
subtype3	125	43.3 (13.1)
subtype4	117	43.7 (13.8)

Figure S34. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00328 (Fisher's exact test), Q value = 0.0081

Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	237
subtype1	35	55
subtype2	52	43
subtype3	43	83
subtype4	61	56

Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.391 (Fisher's exact test), Q value = 0.49

Table S41. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	152	244
subtype1	37	45
subtype2	29	57
subtype3	42	77
subtype4	44	65

Figure S36. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.155 (Kruskal-Wallis (anova)), Q value = 0.25

Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	239	85.7 (12.8)
subtype1	56	86.8 (10.8)
subtype2	53	82.5 (14.7)
subtype3	63	88.3 (11.7)
subtype4	67	85.1 (13.3)

Figure S37. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0011 (Fisher's exact test), Q value = 0.0031

Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	147	114	167
subtype1	35	30	25
subtype2	44	15	36
subtype3	41	37	48
subtype4	27	32	58

Figure S38. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.48 (Fisher's exact test), Q value = 0.56

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	18	393
subtype1	0	0	6	82
subtype2	0	4	3	85
subtype3	0	2	5	117
subtype4	1	2	4	109

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.552 (Fisher's exact test), Q value = 0.63

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	377
subtype1	8	76
subtype2	6	83
subtype3	7	111
subtype4	5	107

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	83	160	185

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00217 (logrank test), Q value = 0.0056

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	424	98	0.0 - 211.2 (21.0)
subtype1	83	8	0.0 - 182.3 (23.7)
subtype2	157	30	0.1 - 154.4 (18.6)
subtype3	184	60	0.1 - 211.2 (21.8)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0687 (Kruskal-Wallis (anova)), Q value = 0.13

Table S48. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	427	42.7 (13.3)
subtype1	82	40.0 (11.5)
subtype2	160	42.2 (13.3)
subtype3	185	44.3 (14.0)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.406 (Fisher's exact test), Q value = 0.51

Table S49. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	191	237
subtype1	32	51
subtype2	76	84
subtype3	83	102

Figure S43. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.065 (Fisher's exact test), Q value = 0.12

Table S50. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	152	244
subtype1	34	44
subtype2	64	84
subtype3	54	116

Figure S44. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.458 (Kruskal-Wallis (anova)), Q value = 0.56

Table S51. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	239	85.7 (12.8)
subtype1	52	88.1 (10.1)
subtype2	88	85.7 (13.2)
subtype3	99	84.5 (13.6)

Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	147	114	167
subtype1	40	25	18
subtype2	33	50	77
subtype3	74	39	72

Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.756 (Fisher's exact test), Q value = 0.81

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	18	393
subtype1	0	0	4	76
subtype2	1	3	6	149
subtype3	0	5	8	168

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.284 (Fisher's exact test), Q value = 0.39

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	377
subtype1	8	67
subtype2	8	146
subtype3	10	164

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S55. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	129	115	106	135	30

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	125	0.0 - 211.2 (22.3)
subtype1	129	27	0.0 - 145.1 (28.9)
subtype2	114	55	0.1 - 211.2 (17.7)
subtype3	104	12	0.1 - 182.3 (22.8)
subtype4	134	28	0.1 - 172.8 (25.5)
subtype5	30	3	0.2 - 93.2 (19.7)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.21e-11 (Kruskal-Wallis (anova)), Q value = 3.7e-10

Table S57. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	514	42.9 (13.4)
subtype1	129	36.6 (10.6)
subtype2	115	48.9 (14.0)
subtype3	106	45.0 (12.3)
subtype4	134	41.9 (13.8)
subtype5	30	44.6 (11.7)

Figure S50. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.708 (Fisher's exact test), Q value = 0.76

Table S58. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	285
subtype1	51	78
subtype2	55	60
subtype3	50	56
subtype4	61	74
subtype5	13	17

Figure S51. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S59. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	186	296
subtype1	42	81
subtype2	20	84
subtype3	57	42
subtype4	54	73
subtype5	13	16

Figure S52. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00138 (Kruskal-Wallis (anova)), Q value = 0.0037

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

	nPatients	Mean (Std.Dev)
ALL	307	86.6 (12.6)
subtype1	82	89.3 (10.9)
subtype2	74	82.3 (14.2)
subtype3	64	89.5 (11.9)
subtype4	69	85.4 (12.6)
subtype5	18	87.2 (10.7)

Figure S53. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S61. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	194	130	191
subtype1	66	40	23
subtype2	80	17	18
subtype3	3	21	82
subtype4	36	39	60
subtype5	9	13	8

Figure S54. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.329 (Fisher's exact test), Q value = 0.44

Table S62. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	475
subtype1	0	0	6	121
subtype2	1	2	6	105
subtype3	0	4	2	96
subtype4	0	1	6	125
subtype5	0	1	1	28

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.121 (Fisher's exact test), Q value = 0.2

Table S63. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	449
subtype1	8	110
subtype2	3	105
subtype3	11	87
subtype4	7	121
subtype5	3	26

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4	5	6	7
Number of samples	134	71	36	68	91	41	74

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	511	125	0.0 - 211.2 (22.3)
subtype1	134	28	0.0 - 145.1 (29.5)
subtype2	71	40	0.1 - 133.7 (14.3)
subtype3	35	5	0.1 - 169.8 (26.1)
subtype4	68	20	0.1 - 172.8 (19.8)
subtype5	90	12	0.1 - 182.3 (22.8)
subtype6	40	11	0.1 - 211.2 (24.2)
subtype7	73	9	0.1 - 154.4 (27.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.45e-15 (Kruskal-Wallis (anova)), Q value = 3.2e-14

Table S66. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	514	42.9 (13.4)
subtype1	134	37.2 (10.6)
subtype2	71	53.9 (12.3)
subtype3	36	49.0 (13.9)
subtype4	68	41.0 (14.2)
subtype5	91	44.4 (12.3)
subtype6	41	39.9 (11.4)
subtype7	73	41.5 (12.9)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.282 (Fisher's exact test), Q value = 0.39

Table S67. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	285
subtype1	53	81
subtype2	34	37
subtype3	22	14
subtype4	32	36
subtype5	35	56
subtype6	19	22
subtype7	35	39

Figure S59. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S68. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	186	296
subtype1	42	83
subtype2	12	51
subtype3	20	12
subtype4	22	42
subtype5	51	37
subtype6	5	35
subtype7	34	36

Figure S60. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000445 (Kruskal-Wallis (anova)), Q value = 0.0013

Table S69. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	307	86.6 (12.6)
subtype1	84	89.3 (11.6)
subtype2	41	79.8 (15.1)
subtype3	23	89.1 (14.1)
subtype4	36	85.3 (11.8)
subtype5	53	89.2 (11.4)
subtype6	31	83.9 (11.5)
subtype7	39	86.7 (11.3)

Figure S61. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S70. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	194	130	191
subtype1	65	46	23
subtype2	46	12	13
subtype3	1	3	32
subtype4	37	22	9
subtype5	3	25	63
subtype6	33	5	3
subtype7	9	17	48

Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.177 (Fisher's exact test), Q value = 0.28

Table S71. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	475
subtype1	0	0	6	126
subtype2	1	1	6	62
subtype3	0	1	1	31
subtype4	0	1	3	62
subtype5	0	4	2	83
subtype6	0	1	0	40
subtype7	0	0	3	71

Figure S63. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.834 (Fisher's exact test), Q value = 0.85

Table S72. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	449
subtype1	9	112
subtype2	3	64
subtype3	3	29
subtype4	4	58
subtype5	8	78
subtype6	2	39
subtype7	3	69

Figure S64. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S73. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	140	106	83	182

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0253 (logrank test), Q value = 0.054

Table S74. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	507	124	0.0 - 211.2 (22.4)
subtype1	139	32	0.0 - 145.1 (23.5)
subtype2	106	38	0.1 - 211.2 (21.1)
subtype3	81	18	0.1 - 182.3 (24.9)
subtype4	181	36	0.1 - 156.2 (21.4)

Figure S65. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.804 (Kruskal-Wallis (anova)), Q value = 0.84

Table S75. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	140	42.5 (13.4)
subtype2	106	42.4 (13.2)
subtype3	83	44.1 (13.2)
subtype4	181	43.1 (13.7)

Figure S66. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.621 (Fisher's exact test), Q value = 0.68

Table S76. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	61	79
subtype2	43	63
subtype3	38	45
subtype4	88	94

Figure S67. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00551 (Fisher's exact test), Q value = 0.013

Table S77. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	294
subtype1	45	84
subtype2	27	74
subtype3	39	39
subtype4	73	97

Figure S68. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0898 (Kruskal-Wallis (anova)), Q value = 0.16

Table S78. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.7 (12.6)
subtype1	86	86.7 (12.6)
subtype2	69	84.2 (12.9)
subtype3	47	89.4 (11.9)
subtype4	103	87.0 (12.7)

Figure S69. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S79. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	69	35	36
subtype2	66	22	18
subtype3	5	20	58
subtype4	53	50	79

Figure S70. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.302 (Fisher's exact test), Q value = 0.41

Table S80. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	1	1	10	125
subtype2	0	2	3	100
subtype3	0	3	2	75
subtype4	0	2	6	171

Figure S71. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.259 (Fisher's exact test), Q value = 0.38

Table S81. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	9	121
subtype2	3	94
subtype3	8	67
subtype4	12	163

Figure S72. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S82. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	222	186	103

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.47e-14 (logrank test), Q value = 4.8e-13

Table S83. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	507	124	0.0 - 211.2 (22.4)
subtype1	219	42	0.0 - 182.3 (27.4)
subtype2	185	36	0.1 - 172.8 (23.3)
subtype3	103	46	0.1 - 211.2 (17.5)

Figure S73. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 1.78e-08 (Kruskal-Wallis (anova)), Q value = 1.4e-07

Table S84. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	510	43.0 (13.4)
subtype1	222	40.0 (11.5)
subtype2	185	42.6 (13.8)
subtype3	103	50.0 (13.8)

Figure S74. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.283 (Fisher's exact test), Q value = 0.39

Table S85. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	230	281
subtype1	91	131
subtype2	90	96
subtype3	49	54

Figure S75. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 2e-05 (Fisher's exact test), Q value = 7.1e-05

Table S86. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	184	294
subtype1	91	119
subtype2	76	97
subtype3	17	78

Figure S76. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000189 (Kruskal-Wallis (anova)), Q value = 0.00061

Table S87. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	305	86.7 (12.6)
subtype1	139	88.9 (11.1)
subtype2	105	87.0 (12.5)
subtype3	61	80.8 (14.3)

Figure S77. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S88. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	193	127	191
subtype1	76	67	79
subtype2	48	44	94
subtype3	69	16	18

Figure S78. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.562 (Fisher's exact test), Q value = 0.63

Table S89. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	471
subtype1	0	3	7	208
subtype2	0	3	8	170
subtype3	1	2	6	93

Figure S79. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.235 (Fisher's exact test), Q value = 0.36

Table S90. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	17	188
subtype2	12	164
subtype3	3	93

Figure S80. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S91. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	126	119	106	156

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

P value = 0.000159 (logrank test), Q value = 0.00053

Table S92. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	503	124	0.0 - 211.2 (22.4)
subtype1	126	36	0.0 - 182.3 (21.2)
subtype2	118	44	0.1 - 145.1 (21.9)
subtype3	104	12	0.1 - 169.8 (25.4)
subtype4	155	32	0.1 - 211.2 (21.3)

Figure S81. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 0.19

Table S93. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	126	41.0 (14.1)
subtype2	119	44.5 (12.9)
subtype3	106	43.0 (12.0)
subtype4	155	43.5 (14.0)

Figure S82. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.473 (Fisher's exact test), Q value = 0.56

Table S94. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	53	73
subtype2	49	70
subtype3	50	56
subtype4	77	79

Figure S83. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 5e-05 (Fisher's exact test), Q value = 0.00017

Table S95. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	292
subtype1	38	81
subtype2	27	81
subtype3	57	44
subtype4	61	86

Figure S84. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0239 (Kruskal-Wallis (anova)), Q value = 0.052

Table S96. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	66	86.7 (12.4)
subtype2	83	84.3 (13.4)
subtype3	68	90.1 (11.0)
subtype4	87	86.3 (12.8)

Figure S85. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S97. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	58	36	32
subtype2	74	23	22
subtype3	13	26	67
subtype4	46	41	69

Figure S86. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.348 (Fisher's exact test), Q value = 0.46

Table S98. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	0	0	5	119
subtype2	1	2	7	107
subtype3	0	4	3	95
subtype4	0	2	6	146

Figure S87. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.0636 (Fisher's exact test), Q value = 0.12

Table S99. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	5	114
subtype2	6	102
subtype3	13	86
subtype4	8	139

Figure S88. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S100. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	98	77	120	57	63	92

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

P value = 0 (logrank test), Q value = 0

Table S101. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	503	124	0.0 - 211.2 (22.4)
subtype1	97	13	0.0 - 172.8 (27.4)
subtype2	76	21	0.1 - 134.3 (26.0)
subtype3	119	24	0.1 - 169.8 (21.6)
subtype4	56	7	0.1 - 123.7 (24.7)
subtype5	63	37	0.1 - 133.7 (15.7)
subtype6	92	22	0.1 - 211.2 (22.4)

Figure S89. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.88e-13 (Kruskal-Wallis (anova)), Q value = 2.3e-12

Table S102. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	506	43.0 (13.4)
subtype1	98	36.6 (11.2)
subtype2	77	41.4 (11.5)
subtype3	119	43.0 (13.9)
subtype4	57	44.1 (12.6)
subtype5	63	54.6 (11.7)
subtype6	92	42.6 (13.1)

Figure S90. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.217 (Fisher's exact test), Q value = 0.34

Table S103. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	229	278
subtype1	34	64
subtype2	36	41
subtype3	59	61
subtype4	23	34
subtype5	31	32
subtype6	46	46

Figure S91. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S104. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	183	292
subtype1	34	58
subtype2	19	55
subtype3	47	66
subtype4	37	17
subtype5	11	45
subtype6	35	51

Figure S92. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00414 (Kruskal-Wallis (anova)), Q value = 0.0099

Table S105. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	304	86.7 (12.6)
subtype1	60	87.3 (11.8)
subtype2	53	86.8 (11.2)
subtype3	65	85.7 (12.5)
subtype4	36	91.4 (9.9)
subtype5	37	79.7 (15.9)
subtype6	53	88.9 (12.4)

Figure S93. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3.7e-05

Table S106. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	191	126	190
subtype1	55	27	16
subtype2	35	20	22
subtype3	35	28	57
subtype4	2	11	44
subtype5	41	11	11
subtype6	23	29	40

Figure S94. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0453 (Fisher's exact test), Q value = 0.091

Table S107. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	8	21	467
subtype1	0	0	2	94
subtype2	0	2	3	72
subtype3	0	2	5	112
subtype4	0	3	2	50
subtype5	1	1	7	53
subtype6	0	0	2	86

Figure S95. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.259 (Fisher's exact test), Q value = 0.38

Table S108. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	441
subtype1	6	81
subtype2	4	69
subtype3	5	110
subtype4	8	44
subtype5	3	55
subtype6	6	82

Figure S96. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LGG-TP/22553887/LGG-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LGG-TP/22506744/LGG-TP.merged_data.txt
Number of patients = 515
Number of clustering approaches = 12
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.

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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