Correlation between aggregated molecular cancer subtypes and selected clinical features

Brain Lower Grade Glioma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1CJ8C7Q

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 403 patients, 27 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'AGE', 'KARNOFSKY.PERFORMANCE.SCORE', and 'HISTOLOGICAL.TYPE'.
7 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'AGE', 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, 27 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test	Fisher's exact test
mRNA CNMF subtypes	0.487 (1.00)	0.247 (1.00)	0.101 (1.00)	0.506 (1.00)	0.0229 (1.00)	0.746 (1.00)	0.515 (1.00)	1 (1.00)
mRNA cHierClus subtypes	0.0499 (1.00)	0.0787 (1.00)	0.15 (1.00)	0.312 (1.00)	0.00857 (0.574)	0.483 (1.00)	0.222 (1.00)	0.428 (1.00)
Copy Number Ratio CNMF subtypes	2.18e-11 (2.05e-09)	7.36e-10 (6.62e-08)	0.201 (1.00)	0.149 (1.00)	1e-05 (0.00082)	0.332 (1.00)	0.012 (0.753)	0.892 (1.00)
METHLYATION CNMF	1.22e-15 (1.17e-13)	5.13e-14 (4.88e-12)	0.641 (1.00)	0.025 (1.00)	1e-05 (0.00082)	0.0108 (0.701)	0.0239 (1.00)	0.691 (1.00)
RPPA CNMF subtypes	0.000104 (0.00742)	0.012 (0.753)	0.0094 (0.62)	0.0951 (1.00)	6e-05 (0.00438)	0.169 (1.00)	0.951 (1.00)	0.889 (1.00)
RPPA cHierClus subtypes	0.429 (1.00)	0.00467 (0.317)	0.617 (1.00)	0.459 (1.00)	0.0831 (1.00)	0.475 (1.00)	0.3 (1.00)	0.934 (1.00)
RNAseq CNMF subtypes	8.18e-11 (7.61e-09)	1.93e-09 (1.7e-07)	0.75 (1.00)	6.87e-05 (0.00495)	1e-05 (0.00082)	0.0886 (1.00)	0.304 (1.00)	0.682 (1.00)
RNAseq cHierClus subtypes	3.18e-09 (2.77e-07)	1.15e-08 (9.9e-07)	0.641 (1.00)	2.55e-05 (0.00188)	1e-05 (0.00082)	0.0413 (1.00)	0.436 (1.00)	0.635 (1.00)
MIRSEQ CNMF	0.162 (1.00)	0.245 (1.00)	0.629 (1.00)	0.149 (1.00)	1e-05 (0.00082)	0.456 (1.00)	0.05 (1.00)	0.419 (1.00)
MIRSEQ CHIERARCHICAL	1.48e-10 (1.36e-08)	4.87e-08 (4.14e-06)	0.191 (1.00)	0.000164 (0.0115)	1e-05 (0.00082)	0.0178 (1.00)	0.576 (1.00)	0.64 (1.00)
MIRseq Mature CNMF subtypes	1.23e-06 (0.000103)	1.28e-06 (0.000106)	0.777 (1.00)	0.0117 (0.751)	1e-05 (0.00082)	0.0511 (1.00)	0.317 (1.00)	0.573 (1.00)
MIRseq Mature cHierClus subtypes	2.99e-10 (2.72e-08)	1.28e-09 (1.14e-07)	0.129 (1.00)	0.00437 (0.301)	1e-05 (0.00082)	0.06 (1.00)	0.265 (1.00)	0.832 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	9	10	8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.487 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	10	0.1 - 134.3 (46.6)
subtype1	9	4	10.6 - 130.8 (43.9)
subtype2	10	3	0.1 - 78.2 (36.5)
subtype3	8	3	14.4 - 134.3 (61.5)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 1

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

	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: 'AGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.101 (Fisher's exact test), Q value = 1

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 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	7	84.3 (16.2)
subtype2	7	92.9 (7.6)
subtype3	3	90.0 (10.0)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0229 (Fisher's exact test), Q value = 1

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

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

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

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

P value = 0.746 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	20	7
subtype1	7	2
subtype2	8	2
subtype3	5	3

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.515 (Fisher's exact test), Q value = 1

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 = 0.0499 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	10	0.1 - 134.3 (46.6)
subtype1	7	4	10.6 - 82.0 (43.9)
subtype2	7	4	18.1 - 130.8 (41.1)
subtype3	7	1	0.1 - 78.2 (31.8)
subtype4	6	1	14.4 - 134.3 (75.6)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0787 (Kruskal-Wallis (anova)), Q value = 1

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

	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: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.15 (Fisher's exact test), Q value = 1

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 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.312 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	5	90.0 (7.1)
subtype2	5	82.0 (17.9)
subtype3	5	94.0 (8.9)
subtype4	2	90.0 (14.1)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00857 (Fisher's exact test), Q value = 0.57

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: '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 S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.483 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	20	7
subtype1	6	1
subtype2	6	1
subtype3	5	2
subtype4	3	3

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

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.222 (Fisher's exact test), Q value = 1

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 = 0.428 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	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	126	91	183

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

P value = 2.18e-11 (logrank test), Q value = 2e-09

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	395	71	0.0 - 211.2 (15.0)
subtype1	125	24	0.1 - 156.2 (17.3)
subtype2	89	33	0.1 - 211.2 (12.2)
subtype3	181	14	0.0 - 182.3 (14.6)

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 'AGE'

P value = 7.36e-10 (Kruskal-Wallis (anova)), Q value = 6.6e-08

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

	nPatients	Mean (Std.Dev)
ALL	400	43.2 (13.3)
subtype1	126	38.8 (11.5)
subtype2	91	50.7 (12.9)
subtype3	183	42.6 (13.2)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.201 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	176	224
subtype1	50	76
subtype2	47	44
subtype3	79	104

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 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.149 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	234	87.9 (12.2)
subtype1	76	88.0 (12.1)
subtype2	55	84.9 (14.5)
subtype3	103	89.4 (10.7)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	143	104	153
subtype1	69	36	21
subtype2	43	26	22
subtype3	31	42	110

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

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

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

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

nPatients	NO	YES
ALL	89	311
subtype1	33	93
subtype2	21	70
subtype3	35	148

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.012 (Fisher's exact test), Q value = 0.75

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	2	14	376
subtype1	0	0	2	123
subtype2	1	1	8	81
subtype3	0	1	4	172

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 = 0.892 (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	15	359
subtype1	4	117
subtype2	4	82
subtype3	7	160

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	168	54	52	124

'METHLYATION CNMF' versus 'Time to Death'

P value = 1.22e-15 (logrank test), Q value = 1.2e-13

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	67	0.0 - 211.2 (14.9)
subtype1	166	27	0.0 - 156.2 (17.4)
subtype2	54	26	0.2 - 211.2 (11.1)
subtype3	51	4	0.1 - 122.7 (11.9)
subtype4	122	10	0.1 - 182.3 (14.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 5.13e-14 (Kruskal-Wallis (anova)), Q value = 4.9e-12

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

	nPatients	Mean (Std.Dev)
ALL	398	43.1 (13.4)
subtype1	168	38.2 (11.3)
subtype2	54	54.4 (12.1)
subtype3	52	41.1 (14.7)
subtype4	124	45.6 (12.4)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.641 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	178	220
subtype1	71	97
subtype2	27	27
subtype3	26	26
subtype4	54	70

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

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.025 (Kruskal-Wallis (anova)), Q value = 1

Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	234	87.9 (12.2)
subtype1	106	89.1 (12.2)
subtype2	33	84.2 (12.3)
subtype3	23	84.8 (12.4)
subtype4	72	88.9 (12.1)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	103	153
subtype1	87	55	26
subtype2	34	10	10
subtype3	18	13	21
subtype4	3	25	96

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

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

P value = 0.0108 (Fisher's exact test), Q value = 0.7

Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	85	313
subtype1	48	120
subtype2	12	42
subtype3	9	43
subtype4	16	108

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0239 (Fisher's exact test), Q value = 1

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	2	13	375
subtype1	0	0	5	162
subtype2	0	1	3	50
subtype3	1	0	4	46
subtype4	0	1	1	117

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.691 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	357
subtype1	5	155
subtype2	2	50
subtype3	3	43
subtype4	5	109

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	51	62	69	72

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.000104 (logrank test), Q value = 0.0074

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	252	55	0.0 - 211.2 (16.1)
subtype1	50	5	0.1 - 82.0 (14.7)
subtype2	61	29	0.1 - 156.2 (16.2)
subtype3	69	10	0.0 - 211.2 (19.2)
subtype4	72	11	0.1 - 138.3 (16.3)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.012 (Kruskal-Wallis (anova)), Q value = 0.75

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

	nPatients	Mean (Std.Dev)
ALL	254	42.6 (13.3)
subtype1	51	37.7 (11.4)
subtype2	62	46.3 (13.6)
subtype3	69	43.3 (13.1)
subtype4	72	42.1 (13.6)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.0094 (Fisher's exact test), Q value = 0.62

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

nPatients	FEMALE	MALE
ALL	108	146
subtype1	18	33
subtype2	33	29
subtype3	20	49
subtype4	37	35

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

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

P value = 0.0951 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	112	86.9 (11.8)
subtype1	26	89.2 (8.9)
subtype2	30	83.7 (14.7)
subtype3	26	90.0 (12.3)
subtype4	30	85.3 (9.4)

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 6e-05 (Fisher's exact test), Q value = 0.0044

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	83	70	101
subtype1	19	21	11
subtype2	31	11	20
subtype3	20	21	28
subtype4	13	17	42

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

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

P value = 0.169 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	84	170
subtype1	15	36
subtype2	20	42
subtype3	30	39
subtype4	19	53

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.951 (Fisher's exact test), Q value = 1

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

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	242
subtype1	3	48
subtype2	2	60
subtype3	3	65
subtype4	3	69

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.889 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	229
subtype1	3	47
subtype2	2	56
subtype3	4	60
subtype4	4	66

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	67	131	56

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.429 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	252	55	0.0 - 211.2 (16.1)
subtype1	66	9	0.0 - 138.3 (15.2)
subtype2	130	37	0.1 - 211.2 (17.1)
subtype3	56	9	0.1 - 134.3 (15.6)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.00467 (Kruskal-Wallis (anova)), Q value = 0.32

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

	nPatients	Mean (Std.Dev)
ALL	254	42.6 (13.3)
subtype1	67	38.0 (10.5)
subtype2	131	44.7 (13.5)
subtype3	56	43.1 (14.7)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.617 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	108	146
subtype1	28	39
subtype2	53	78
subtype3	27	29

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

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

P value = 0.459 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	112	86.9 (11.8)
subtype1	29	89.0 (10.1)
subtype2	56	86.4 (13.3)
subtype3	27	85.6 (10.1)

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0831 (Fisher's exact test), Q value = 1

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	83	70	101
subtype1	18	23	26
subtype2	52	33	46
subtype3	13	14	29

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

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

P value = 0.475 (Fisher's exact test), Q value = 1

Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	84	170
subtype1	20	47
subtype2	48	83
subtype3	16	40

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.3 (Fisher's exact test), Q value = 1

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

nPatients	BLACK OR AFRICAN AMERICAN	WHITE
ALL	11	242
subtype1	5	62
subtype2	5	125
subtype3	1	55

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.934 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	229
subtype1	4	61
subtype2	6	116
subtype3	3	52

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	102	91	83	105	19

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 8.18e-11 (logrank test), Q value = 7.6e-09

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	395	71	0.0 - 211.2 (15.0)
subtype1	101	17	0.0 - 130.8 (17.9)
subtype2	89	33	0.1 - 211.2 (11.6)
subtype3	82	7	0.1 - 182.3 (15.5)
subtype4	105	14	0.1 - 156.2 (14.6)
subtype5	18	0	2.5 - 31.4 (13.4)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 1.93e-09 (Kruskal-Wallis (anova)), Q value = 1.7e-07

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

	nPatients	Mean (Std.Dev)
ALL	400	43.2 (13.4)
subtype1	102	36.5 (10.4)
subtype2	91	49.3 (13.6)
subtype3	83	45.1 (12.1)
subtype4	105	42.8 (14.2)
subtype5	19	44.2 (11.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients	FEMALE	MALE
ALL	176	224
subtype1	40	62
subtype2	44	47
subtype3	38	45
subtype4	45	60
subtype5	9	10

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

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

P value = 6.87e-05 (Kruskal-Wallis (anova)), Q value = 0.0049

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

	nPatients	Mean (Std.Dev)
ALL	232	87.9 (12.3)
subtype1	66	92.4 (9.0)
subtype2	57	83.5 (12.9)
subtype3	49	90.0 (11.9)
subtype4	49	85.1 (13.9)
subtype5	11	87.3 (11.0)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	105	153
subtype1	49	35	18
subtype2	63	13	15
subtype3	3	15	65
subtype4	25	32	48
subtype5	2	10	7

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

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

P value = 0.0886 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	89	311
subtype1	29	73
subtype2	22	69
subtype3	11	72
subtype4	25	80
subtype5	2	17

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.304 (Fisher's exact test), Q value = 1

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	2	14	376
subtype1	0	0	5	96
subtype2	1	1	4	84
subtype3	0	1	0	79
subtype4	0	0	5	98
subtype5	0	0	0	19

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.682 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	359
subtype1	3	92
subtype2	2	83
subtype3	4	70
subtype4	6	95
subtype5	0	19

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
Number of samples	103	90	96	111

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 3.18e-09 (logrank test), Q value = 2.8e-07

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	395	71	0.0 - 211.2 (15.0)
subtype1	102	17	0.0 - 130.8 (17.5)
subtype2	88	30	0.1 - 211.2 (12.1)
subtype3	95	10	0.1 - 182.3 (15.6)
subtype4	110	14	0.1 - 156.2 (14.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 1.15e-08 (Kruskal-Wallis (anova)), Q value = 9.9e-07

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

	nPatients	Mean (Std.Dev)
ALL	400	43.2 (13.4)
subtype1	103	37.2 (10.6)
subtype2	90	48.6 (13.8)
subtype3	96	46.0 (12.4)
subtype4	111	42.1 (13.8)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.641 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	176	224
subtype1	40	63
subtype2	43	47
subtype3	43	53
subtype4	50	61

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

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

P value = 2.55e-05 (Kruskal-Wallis (anova)), Q value = 0.0019

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

	nPatients	Mean (Std.Dev)
ALL	232	87.9 (12.3)
subtype1	65	92.2 (9.8)
subtype2	55	83.1 (12.9)
subtype3	59	89.3 (12.4)
subtype4	53	86.2 (12.4)

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	105	153
subtype1	46	39	18
subtype2	63	13	14
subtype3	3	20	73
subtype4	30	33	48

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

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

P value = 0.0413 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	89	311
subtype1	29	74
subtype2	22	68
subtype3	12	84
subtype4	26	85

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.436 (Fisher's exact test), Q value = 1

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	2	14	376
subtype1	0	0	5	97
subtype2	1	1	3	84
subtype3	0	1	1	90
subtype4	0	0	5	105

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.635 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	359
subtype1	2	93
subtype2	3	83
subtype3	4	82
subtype4	6	101

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	108	83	153	53

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.162 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	70	0.0 - 211.2 (15.0)
subtype1	106	20	0.0 - 117.4 (15.6)
subtype2	82	23	0.1 - 211.2 (15.3)
subtype3	152	19	0.1 - 156.2 (13.6)
subtype4	53	8	0.1 - 182.3 (16.5)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.245 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	397	43.3 (13.3)
subtype1	108	41.9 (13.0)
subtype2	83	42.3 (12.8)
subtype3	153	43.8 (13.6)
subtype4	53	46.1 (13.6)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.629 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	178	219
subtype1	43	65
subtype2	37	46
subtype3	73	80
subtype4	25	28

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

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

P value = 0.149 (Kruskal-Wallis (anova)), Q value = 1

Table S77. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	229	88.0 (12.3)
subtype1	63	89.7 (10.8)
subtype2	55	85.3 (13.0)
subtype3	83	88.3 (12.4)
subtype4	28	88.6 (13.5)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	102	153
subtype1	52	28	28
subtype2	54	16	13
subtype3	35	46	72
subtype4	1	12	40

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

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

P value = 0.456 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	88	309
subtype1	25	83
subtype2	23	60
subtype3	31	122
subtype4	9	44

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.05 (Fisher's exact test), Q value = 1

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	2	14	373
subtype1	1	1	8	97
subtype2	0	0	1	81
subtype3	0	0	5	146
subtype4	0	1	0	49

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.419 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	357
subtype1	5	97
subtype2	2	75
subtype3	8	140
subtype4	0	45

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	4
Number of samples	127	79	111	80

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 1.48e-10 (logrank test), Q value = 1.4e-08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	70	0.0 - 211.2 (15.0)
subtype1	125	23	0.0 - 182.3 (17.4)
subtype2	79	8	0.1 - 156.2 (15.2)
subtype3	110	10	0.1 - 138.3 (14.5)
subtype4	79	29	0.1 - 211.2 (11.4)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 4.87e-08 (Kruskal-Wallis (anova)), Q value = 4.1e-06

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

	nPatients	Mean (Std.Dev)
ALL	397	43.3 (13.3)
subtype1	127	38.6 (11.5)
subtype2	79	44.6 (12.7)
subtype3	111	42.8 (13.5)
subtype4	80	50.1 (13.5)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.191 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	178	219
subtype1	47	80
subtype2	38	41
subtype3	53	58
subtype4	40	40

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

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

P value = 0.000164 (Kruskal-Wallis (anova)), Q value = 0.011

Table S86. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	229	88.0 (12.3)
subtype1	76	89.9 (10.4)
subtype2	51	91.4 (12.5)
subtype3	56	86.4 (12.7)
subtype4	46	83.0 (13.0)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	102	153
subtype1	52	38	37
subtype2	9	19	51
subtype3	25	33	53
subtype4	56	12	12

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

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

P value = 0.0178 (Fisher's exact test), Q value = 1

Table S88. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	88	309
subtype1	38	89
subtype2	9	70
subtype3	24	87
subtype4	17	63

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.576 (Fisher's exact test), Q value = 1

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	2	14	373
subtype1	0	0	4	122
subtype2	0	1	2	72
subtype3	0	0	4	106
subtype4	1	1	4	73

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.64 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	357
subtype1	5	115
subtype2	1	69
subtype3	6	102
subtype4	3	71

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	5	6	7
Number of samples	88	74	50	117	34	14	20

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

P value = 1.23e-06 (logrank test), Q value = 1e-04

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	70	0.0 - 211.2 (15.0)
subtype1	88	15	0.0 - 130.8 (17.6)
subtype2	73	24	0.1 - 124.5 (10.6)
subtype3	50	5	0.1 - 182.3 (15.0)
subtype4	116	12	0.1 - 156.2 (13.2)
subtype5	34	8	0.1 - 154.2 (18.3)
subtype6	14	5	7.8 - 211.2 (16.5)
subtype7	18	1	2.7 - 88.8 (14.5)

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 'AGE'

P value = 1.28e-06 (Kruskal-Wallis (anova)), Q value = 0.00011

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

	nPatients	Mean (Std.Dev)
ALL	397	43.3 (13.3)
subtype1	88	37.6 (11.3)
subtype2	74	48.6 (12.8)
subtype3	50	45.1 (12.5)
subtype4	117	43.7 (14.0)
subtype5	34	42.9 (13.8)
subtype6	14	35.5 (10.3)
subtype7	20	47.1 (11.9)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.777 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	178	219
subtype1	35	53
subtype2	34	40
subtype3	20	30
subtype4	56	61
subtype5	17	17
subtype6	8	6
subtype7	8	12

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

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

P value = 0.0117 (Kruskal-Wallis (anova)), Q value = 0.75

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

	nPatients	Mean (Std.Dev)
ALL	229	88.0 (12.3)
subtype1	54	91.3 (10.5)
subtype2	41	82.7 (13.8)
subtype3	33	89.4 (12.7)
subtype4	60	87.0 (12.8)
subtype5	17	88.8 (13.2)
subtype6	11	86.4 (10.3)
subtype7	13	92.3 (4.4)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	102	153
subtype1	48	26	14
subtype2	45	16	13
subtype3	3	9	38
subtype4	27	30	60
subtype5	7	11	16
subtype6	10	2	2
subtype7	2	8	10

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

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

P value = 0.0511 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	88	309
subtype1	27	61
subtype2	12	62
subtype3	7	43
subtype4	24	93
subtype5	11	23
subtype6	5	9
subtype7	2	18

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.317 (Fisher's exact test), Q value = 1

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	2	14	373
subtype1	0	0	4	83
subtype2	1	1	2	69
subtype3	0	0	0	48
subtype4	0	0	4	111
subtype5	0	0	2	31
subtype6	0	0	1	13
subtype7	0	1	1	18

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

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	357
subtype1	1	80
subtype2	2	66
subtype3	2	42
subtype4	7	106
subtype5	2	31
subtype6	0	14
subtype7	1	18

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	122	108	89	78

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

P value = 2.99e-10 (logrank test), Q value = 2.7e-08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	393	70	0.0 - 211.2 (15.0)
subtype1	120	24	0.0 - 182.3 (18.9)
subtype2	107	12	0.1 - 103.8 (14.4)
subtype3	89	6	0.1 - 156.2 (14.3)
subtype4	77	28	0.1 - 211.2 (10.9)

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 'AGE'

P value = 1.28e-09 (Kruskal-Wallis (anova)), Q value = 1.1e-07

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

	nPatients	Mean (Std.Dev)
ALL	397	43.3 (13.3)
subtype1	122	38.0 (11.1)
subtype2	108	44.2 (13.1)
subtype3	89	42.6 (13.0)
subtype4	78	51.1 (13.4)

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.129 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	178	219
subtype1	44	78
subtype2	51	57
subtype3	44	45
subtype4	39	39

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

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

P value = 0.00437 (Kruskal-Wallis (anova)), Q value = 0.3

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

	nPatients	Mean (Std.Dev)
ALL	229	88.0 (12.3)
subtype1	72	89.7 (10.9)
subtype2	69	90.3 (11.6)
subtype3	43	86.0 (13.3)
subtype4	45	83.6 (13.3)

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

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

P value = 1e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	142	102	153
subtype1	56	39	27
subtype2	11	28	69
subtype3	19	24	46
subtype4	56	11	11

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

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

P value = 0.06 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	88	309
subtype1	36	86
subtype2	16	92
subtype3	18	71
subtype4	18	60

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

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

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	2	14	373
subtype1	0	0	5	115
subtype2	0	1	1	102
subtype3	0	0	4	85
subtype4	1	1	4	71

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 = 0.832 (Fisher's exact test), Q value = 1

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	15	357
subtype1	4	109
subtype2	4	95
subtype3	5	82
subtype4	2	71

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

Methods & Data

Input

Cluster data file = LGG-TP.mergedcluster.txt
Clinical data file = LGG-TP.merged_data.txt
Number of patients = 403
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.

Download Results

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

References

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

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

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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