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, 49 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'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', and 'ETHNICITY'.
4 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', and 'HISTOLOGICAL_TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 49 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	0.377 (0.489)	0.247 (0.359)	0.102 (0.172)	0.693 (0.747)	0.305 (0.401)	0.0227 (0.0475)	0.515 (0.595)	1 (1.00)
mRNA cHierClus subtypes	0.0372 (0.0743)	0.0787 (0.14)	0.15 (0.248)	0.763 (0.805)	0.243 (0.359)	0.00811 (0.0185)	0.225 (0.342)	0.43 (0.524)
Copy Number Ratio CNMF subtypes	1.89e-15 (2.26e-14)	4.56e-16 (7.29e-15)	0.264 (0.378)	1e-05 (3.69e-05)	0.000735 (0.00191)	1e-05 (3.69e-05)	0.00073 (0.00191)	1 (1.00)
METHLYATION CNMF	0 (0)	1.39e-20 (2.68e-19)	0.514 (0.595)	1e-05 (3.69e-05)	0.000561 (0.00158)	1e-05 (3.69e-05)	0.0203 (0.0432)	0.393 (0.496)
RPPA CNMF subtypes	0.000726 (0.00191)	0.0502 (0.0957)	0.00375 (0.00878)	0.383 (0.49)	0.185 (0.291)	0.00118 (0.0029)	0.48 (0.569)	0.55 (0.622)
RPPA cHierClus subtypes	0.00855 (0.0191)	0.0687 (0.124)	0.409 (0.51)	0.0509 (0.0957)	0.524 (0.599)	1e-05 (3.69e-05)	0.754 (0.804)	0.283 (0.383)
RNAseq CNMF subtypes	0 (0)	6.44e-12 (5.62e-11)	0.623 (0.687)	1e-05 (3.69e-05)	0.000118 (0.000397)	1e-05 (3.69e-05)	0.296 (0.394)	0.164 (0.266)
RNAseq cHierClus subtypes	0 (0)	1.45e-15 (1.98e-14)	0.279 (0.383)	1e-05 (3.69e-05)	0.00053 (0.00154)	1e-05 (3.69e-05)	0.177 (0.283)	0.837 (0.873)
MIRSEQ CNMF	0.0123 (0.0269)	0.917 (0.936)	0.69 (0.747)	0.00199 (0.00478)	0.0929 (0.162)	1e-05 (3.69e-05)	0.269 (0.38)	0.437 (0.525)
MIRSEQ CHIERARCHICAL	6.06e-14 (6.47e-13)	1.78e-08 (1.43e-07)	0.283 (0.383)	1e-05 (3.69e-05)	0.000222 (0.000688)	1e-05 (3.69e-05)	0.568 (0.634)	0.233 (0.35)
MIRseq Mature CNMF subtypes	0.000268 (0.000805)	0.0547 (0.101)	0.0323 (0.0659)	0.00014 (0.000448)	0.0393 (0.0769)	1e-05 (3.69e-05)	0.864 (0.892)	0.0008 (0.00202)
MIRseq Mature cHierClus subtypes	0 (0)	1.6e-12 (1.54e-11)	0.0973 (0.167)	0.00012 (0.000397)	5.43e-05 (0.000193)	1e-05 (3.69e-05)	0.203 (0.314)	0.431 (0.524)

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 = 0.377 (logrank test), Q value = 0.49

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	10	0.1 - 134.3 (50.1)
subtype1	9	4	10.6 - 130.8 (50.1)
subtype2	10	3	0.1 - 78.2 (36.5)
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 = 0.36

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 = 0.17

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 = 0.693 (Fisher's exact test), Q value = 0.75

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

nPatients	NO	YES
ALL	5	19
subtype1	2	6
subtype2	1	8
subtype3	2	5

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 = 0.305 (Kruskal-Wallis (anova)), Q value = 0.4

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

	nPatients	Mean (Std.Dev)
ALL	17	87.1 (13.1)
subtype1	7	81.4 (16.8)
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 = 0.0227 (Fisher's exact test), Q value = 0.047

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 = 0.515 (Fisher's exact test), Q value = 0.6

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 = 0.0372 (logrank test), Q value = 0.074

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	10	0.1 - 134.3 (50.1)
subtype1	7	4	10.6 - 82.0 (43.9)
subtype2	7	4	18.1 - 130.8 (47.9)
subtype3	7	1	0.1 - 78.2 (31.8)
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 = 0.15 (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 = 0.763 (Fisher's exact test), Q value = 0.81

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

nPatients	NO	YES
ALL	5	19
subtype1	1	5
subtype2	1	6
subtype3	1	5
subtype4	2	3

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 = 0.243 (Kruskal-Wallis (anova)), Q value = 0.36

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

	nPatients	Mean (Std.Dev)
ALL	17	87.1 (13.1)
subtype1	5	84.0 (11.4)
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 = 0.00811 (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 = 0.225 (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 = 0.43 (Fisher's exact test), Q value = 0.52

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	171	120	221

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

P value = 1.89e-15 (logrank test), Q value = 2.3e-14

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	509	121	0.0 - 211.2 (20.8)
subtype1	171	40	0.1 - 156.2 (25.9)
subtype2	119	54	0.1 - 211.2 (17.0)
subtype3	219	27	0.0 - 182.3 (22.6)

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 = 4.56e-16 (Kruskal-Wallis (anova)), Q value = 7.3e-15

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	171	37.7 (11.4)
subtype2	120	51.0 (12.6)
subtype3	220	42.8 (13.1)

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 = 0.264 (Fisher's exact test), Q value = 0.38

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

nPatients	FEMALE	MALE
ALL	228	284
subtype1	71	100
subtype2	61	59
subtype3	96	125

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	293
subtype1	45	116
subtype2	30	79
subtype3	109	98

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 = 0.000735 (Kruskal-Wallis (anova)), Q value = 0.0019

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

	nPatients	Mean (Std.Dev)
ALL	306	86.6 (12.6)
subtype1	106	87.9 (11.5)
subtype2	72	81.2 (15.1)
subtype3	128	88.6 (11.0)

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	101	44	26
subtype2	57	33	30
subtype3	36	52	133

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 = 0.00073 (Fisher's exact test), Q value = 0.0019

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	0	3	165
subtype2	1	3	12	104
subtype3	0	5	6	203

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

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	10	146
subtype2	8	106
subtype3	14	194

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	224	81	168	42

'METHLYATION CNMF' versus 'Time to Death'

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

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	512	121	0.0 - 211.2 (20.8)
subtype1	223	44	0.0 - 172.8 (25.4)
subtype2	81	49	0.1 - 211.2 (14.5)
subtype3	166	19	0.1 - 182.3 (21.8)
subtype4	42	9	0.1 - 146.1 (25.5)

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 = 1.39e-20 (Kruskal-Wallis (anova)), Q value = 2.7e-19

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	224	38.0 (11.0)
subtype2	81	54.2 (12.3)
subtype3	167	45.3 (12.6)
subtype4	42	38.0 (14.3)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.514 (Fisher's exact test), Q value = 0.6

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

nPatients	FEMALE	MALE
ALL	230	285
subtype1	96	128
subtype2	38	43
subtype3	73	95
subtype4	23	19

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	294
subtype1	64	149
subtype2	14	60
subtype3	91	64
subtype4	17	21

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 = 0.000561 (Kruskal-Wallis (anova)), Q value = 0.0016

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	141	87.6 (11.9)
subtype2	50	80.8 (14.3)
subtype3	95	88.8 (11.8)
subtype4	21	83.8 (11.6)

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	121	70	33
subtype2	53	12	16
subtype3	5	36	127
subtype4	15	12	15

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0203 (Fisher's exact test), Q value = 0.043

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	215
subtype2	1	3	6	71
subtype3	0	4	5	153
subtype4	0	0	4	36

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.393 (Fisher's exact test), Q value = 0.5

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	194
subtype2	2	75
subtype3	13	144
subtype4	3	36

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 = 0.000726 (logrank test), Q value = 0.0019

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	425	95	0.0 - 211.2 (20.0)
subtype1	88	16	0.0 - 107.0 (23.4)
subtype2	94	36	0.1 - 156.2 (19.1)
subtype3	126	21	0.1 - 211.2 (21.5)
subtype4	117	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 = 0.096

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 = 0.00375 (Fisher's exact test), Q value = 0.0088

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 = 0.383 (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	242
subtype1	37	45
subtype2	29	58
subtype3	42	75
subtype4	44	64

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 = 0.185 (Kruskal-Wallis (anova)), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	239	85.7 (12.7)
subtype1	56	86.8 (10.8)
subtype2	53	82.5 (14.7)
subtype3	63	88.1 (11.6)
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 = 0.00118 (Fisher's exact test), Q value = 0.0029

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 = 0.57

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 = 0.55 (Fisher's exact test), Q value = 0.62

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 = 0.00855 (logrank test), Q value = 0.019

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	425	95	0.0 - 211.2 (20.0)
subtype1	83	8	0.0 - 182.3 (23.3)
subtype2	158	30	0.1 - 154.4 (17.4)
subtype3	184	57	0.1 - 211.2 (20.0)

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 = 0.12

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 = 0.409 (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 = 0.0509 (Fisher's exact test), Q value = 0.096

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

nPatients	NO	YES
ALL	152	242
subtype1	34	43
subtype2	64	83
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 = 0.524 (Kruskal-Wallis (anova)), Q value = 0.6

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

	nPatients	Mean (Std.Dev)
ALL	239	85.7 (12.7)
subtype1	52	87.9 (10.0)
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 = 0.754 (Fisher's exact test), Q value = 0.8

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 = 0.283 (Fisher's exact test), Q value = 0.38

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	133	109	109	134	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	512	121	0.0 - 211.2 (20.8)
subtype1	133	24	0.0 - 145.1 (25.9)
subtype2	108	52	0.1 - 211.2 (17.2)
subtype3	108	13	0.1 - 182.3 (21.0)
subtype4	133	27	0.1 - 172.8 (23.3)
subtype5	30	5	2.4 - 98.2 (20.7)

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 = 6.44e-12 (Kruskal-Wallis (anova)), Q value = 5.6e-11

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	133	36.6 (10.4)
subtype2	109	49.1 (13.6)
subtype3	109	45.4 (12.8)
subtype4	133	41.8 (13.7)
subtype5	30	44.9 (12.3)

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 = 0.623 (Fisher's exact test), Q value = 0.69

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

nPatients	FEMALE	MALE
ALL	230	285
subtype1	52	81
subtype2	53	56
subtype3	51	58
subtype4	61	73
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	294
subtype1	42	84
subtype2	18	81
subtype3	59	42
subtype4	54	71
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 = 0.000118 (Kruskal-Wallis (anova)), Q value = 4e-04

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	83	89.4 (10.9)
subtype2	72	81.5 (13.8)
subtype3	65	89.7 (11.9)
subtype4	69	85.4 (12.6)
subtype5	18	87.8 (11.1)

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	69	41	23
subtype2	77	17	15
subtype3	3	21	85
subtype4	36	38	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 = 0.296 (Fisher's exact test), Q value = 0.39

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	125
subtype2	1	2	6	99
subtype3	0	4	2	99
subtype4	0	1	6	124
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 = 0.164 (Fisher's exact test), Q value = 0.27

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	113
subtype2	3	100
subtype3	11	90
subtype4	7	120
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	512	121	0.0 - 211.2 (20.8)
subtype1	134	26	0.0 - 145.1 (26.6)
subtype2	71	39	0.1 - 133.7 (14.3)
subtype3	36	4	0.1 - 169.8 (25.2)
subtype4	68	20	0.1 - 172.8 (19.8)
subtype5	90	12	0.1 - 182.3 (21.7)
subtype6	40	11	0.1 - 211.2 (23.2)
subtype7	73	9	0.1 - 154.4 (23.2)

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 = 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 = 0.279 (Fisher's exact test), Q value = 0.38

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	294
subtype1	42	83
subtype2	12	51
subtype3	20	10
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 = 0.00053 (Kruskal-Wallis (anova)), Q value = 0.0015

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	89.2 (11.5)
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 = 0.837 (Fisher's exact test), Q value = 0.87

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	137	106	185	83

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0123 (logrank test), Q value = 0.027

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	508	120	0.0 - 211.2 (20.9)
subtype1	136	30	0.0 - 145.1 (20.0)
subtype2	106	38	0.1 - 211.2 (20.8)
subtype3	184	37	0.1 - 156.2 (21.0)
subtype4	82	15	0.1 - 182.3 (22.8)

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 = 0.917 (Kruskal-Wallis (anova)), Q value = 0.94

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	137	42.4 (13.4)
subtype2	106	42.8 (13.1)
subtype3	184	43.2 (13.8)
subtype4	83	43.7 (13.1)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.69 (Fisher's exact test), Q value = 0.75

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

nPatients	FEMALE	MALE
ALL	230	281
subtype1	59	78
subtype2	44	62
subtype3	89	96
subtype4	38	45

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.00199 (Fisher's exact test), Q value = 0.0048

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

nPatients	NO	YES
ALL	184	292
subtype1	44	81
subtype2	27	76
subtype3	73	99
subtype4	40	36

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 = 0.0929 (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	86.6 (12.6)
subtype1	83	86.4 (12.6)
subtype2	71	84.4 (12.7)
subtype3	104	87.1 (12.7)
subtype4	47	89.4 (11.9)

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	36	32
subtype2	65	22	19
subtype3	54	50	81
subtype4	5	19	59

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.269 (Fisher's exact test), Q value = 0.38

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	122
subtype2	0	2	3	100
subtype3	0	2	6	174
subtype4	0	3	2	75

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.437 (Fisher's exact test), Q value = 0.52

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	32	445
subtype1	8	119
subtype2	4	93
subtype3	12	165
subtype4	8	68

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 = 6.06e-14 (logrank test), Q value = 6.5e-13

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	508	120	0.0 - 211.2 (20.9)
subtype1	220	40	0.0 - 182.3 (25.0)
subtype2	185	35	0.1 - 172.8 (22.6)
subtype3	103	45	0.1 - 211.2 (16.8)

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 = 0.38

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

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

nPatients	NO	YES
ALL	184	292
subtype1	91	118
subtype2	76	96
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 = 0.000222 (Kruskal-Wallis (anova)), Q value = 0.00069

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

	nPatients	Mean (Std.Dev)
ALL	305	86.6 (12.6)
subtype1	139	88.8 (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 = 0.568 (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 = 0.233 (Fisher's exact test), Q value = 0.35

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
Number of samples	185	161	161

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

P value = 0.000268 (logrank test), Q value = 8e-04

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	504	120	0.0 - 211.2 (20.9)
subtype1	185	61	0.0 - 182.3 (19.1)
subtype2	159	24	0.1 - 169.8 (25.0)
subtype3	160	35	0.1 - 211.2 (20.2)

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 = 0.0547 (Kruskal-Wallis (anova)), Q value = 0.1

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	185	44.4 (14.3)
subtype2	161	40.7 (11.7)
subtype3	160	43.8 (13.7)

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 = 0.0323 (Fisher's exact test), Q value = 0.066

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

nPatients	FEMALE	MALE
ALL	229	278
subtype1	70	115
subtype2	76	85
subtype3	83	78

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 = 0.00014 (Fisher's exact test), Q value = 0.00045

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

nPatients	NO	YES
ALL	183	290
subtype1	45	126
subtype2	72	78
subtype3	66	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 = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.077

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	104	85.0 (13.2)
subtype2	106	89.2 (10.8)
subtype3	94	85.6 (13.3)

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	95	43	47
subtype2	50	41	70
subtype3	46	42	73

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 = 0.864 (Fisher's exact test), Q value = 0.89

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	1	2	9	169
subtype2	0	4	6	147
subtype3	0	2	6	151

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 = 8e-04 (Fisher's exact test), Q value = 0.002

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	6	165
subtype2	20	128
subtype3	6	148

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
Number of samples	90	138	190	89

'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	504	120	0.0 - 211.2 (20.9)
subtype1	89	14	0.0 - 145.1 (23.7)
subtype2	137	22	0.1 - 172.8 (26.0)
subtype3	189	37	0.1 - 211.2 (22.3)
subtype4	89	47	0.1 - 133.7 (15.7)

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 = 1.6e-12 (Kruskal-Wallis (anova)), Q value = 1.5e-11

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	90	36.9 (11.3)
subtype2	138	42.4 (12.0)
subtype3	189	42.0 (13.0)
subtype4	89	52.3 (13.8)

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 = 0.0973 (Fisher's exact test), Q value = 0.17

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

nPatients	FEMALE	MALE
ALL	229	278
subtype1	35	55
subtype2	54	84
subtype3	96	94
subtype4	44	45

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 = 0.00012 (Fisher's exact test), Q value = 4e-04

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

nPatients	NO	YES
ALL	183	290
subtype1	32	51
subtype2	60	72
subtype3	77	101
subtype4	14	66

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 = 5.43e-05 (Kruskal-Wallis (anova)), Q value = 0.00019

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	56	87.3 (11.7)
subtype2	91	90.1 (10.5)
subtype3	104	86.8 (12.6)
subtype4	53	79.8 (14.3)

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	49	26	15
subtype2	36	37	65
subtype3	45	51	94
subtype4	61	12	16

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 = 0.203 (Fisher's exact test), Q value = 0.31

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	86
subtype2	0	4	5	127
subtype3	0	2	7	177
subtype4	1	2	7	77

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 = 0.431 (Fisher's exact test), Q value = 0.52

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	7	73
subtype2	11	116
subtype3	11	172
subtype4	3	80

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/20146758/LGG-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LGG-TP/19775312/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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