Correlation between aggregated molecular cancer subtypes and selected clinical features

Brain Lower Grade Glioma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

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/C1862FB7

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 433 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'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'AGE', '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' and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'AGE', '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, 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)	0.102 (1.00)	0.506 (1.00)	0.0222 (1.00)	0.744 (1.00)	0.513 (1.00)	1 (1.00)
mRNA cHierClus subtypes	0.0499 (1.00)	0.0787 (1.00)	0.15 (1.00)	0.312 (1.00)	0.00845 (0.558)	0.487 (1.00)	0.222 (1.00)	0.428 (1.00)
Copy Number Ratio CNMF subtypes	5.79e-11 (5.21e-09)	1.15e-11 (1.05e-09)	0.156 (1.00)	0.0677 (1.00)	1e-05 (0.00083)	0.386 (1.00)	0.00767 (0.514)	1 (1.00)
METHLYATION CNMF	0 (0)	2.03e-17 (1.91e-15)	0.499 (1.00)	0.005 (0.345)	1e-05 (0.00083)	0.034 (1.00)	0.172 (1.00)	0.837 (1.00)
RPPA CNMF subtypes	6.81e-05 (0.0051)	0.00957 (0.612)	0.00889 (0.578)	0.0886 (1.00)	0.00013 (0.00936)	0.126 (1.00)	0.951 (1.00)	0.906 (1.00)
RPPA cHierClus subtypes	0.331 (1.00)	0.00512 (0.348)	0.556 (1.00)	0.379 (1.00)	0.0953 (1.00)	0.407 (1.00)	0.276 (1.00)	0.931 (1.00)
RNAseq CNMF subtypes	1.42e-13 (1.32e-11)	3.03e-10 (2.7e-08)	0.72 (1.00)	2.13e-05 (0.00162)	1e-05 (0.00083)	0.0898 (1.00)	0.226 (1.00)	0.767 (1.00)
RNAseq cHierClus subtypes	7.78e-12 (7.16e-10)	9.6e-10 (8.26e-08)	0.554 (1.00)	8.58e-06 (0.00072)	1e-05 (0.00083)	0.0401 (1.00)	0.315 (1.00)	0.881 (1.00)
MIRSEQ CNMF	0.079 (1.00)	0.4 (1.00)	0.719 (1.00)	0.0532 (1.00)	1e-05 (0.00083)	0.37 (1.00)	0.033 (1.00)	0.693 (1.00)
MIRSEQ CHIERARCHICAL	3.05e-10 (2.7e-08)	2.33e-07 (1.98e-05)	0.193 (1.00)	0.00029 (0.0206)	1e-05 (0.00083)	0.425 (1.00)	0.563 (1.00)	0.945 (1.00)
MIRseq Mature CNMF subtypes	0.00245 (0.171)	0.359 (1.00)	0.0566 (1.00)	0.0287 (1.00)	0.00011 (0.00814)	0.0181 (1.00)	0.842 (1.00)	0.385 (1.00)
MIRseq Mature cHierClus subtypes	0 (0)	3.25e-10 (2.83e-08)	0.052 (1.00)	0.000125 (0.00916)	1e-05 (0.00083)	0.779 (1.00)	0.363 (1.00)	0.333 (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 = 0.102 (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 = 0.0222 (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 = 0.744 (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 = 0.513 (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 = 0.00845 (Fisher's exact test), Q value = 0.56

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 = 0.487 (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	141	97	192

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

P value = 5.79e-11 (logrank test), Q value = 5.2e-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	427	75	0.0 - 211.2 (15.9)
subtype1	140	22	0.1 - 156.2 (17.5)
subtype2	96	36	0.1 - 211.2 (12.4)
subtype3	191	17	0.0 - 182.3 (16.0)

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 = 1.15e-11 (Kruskal-Wallis (anova)), Q value = 1e-09

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

	nPatients	Mean (Std.Dev)
ALL	429	43.0 (13.2)
subtype1	141	38.1 (11.5)
subtype2	97	50.5 (12.8)
subtype3	191	42.7 (13.0)

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 = 0.156 (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	190	240
subtype1	57	84
subtype2	51	46
subtype3	82	110

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 = 0.0677 (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	249	87.8 (12.0)
subtype1	84	88.5 (11.7)
subtype2	60	84.5 (14.0)
subtype3	105	89.0 (10.9)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	159	107	164
subtype1	79	38	24
subtype2	48	26	23
subtype3	32	43	117

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 = 0.386 (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	341
subtype1	33	108
subtype2	22	75
subtype3	34	158

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 = 0.00767 (Fisher's exact test), Q value = 0.51

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	6	14	400
subtype1	0	0	2	136
subtype2	1	2	8	86
subtype3	0	4	4	178

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	16	387
subtype1	5	128
subtype2	4	88
subtype3	7	171

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	185	64	145	39

'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	430	75	0.0 - 211.2 (15.8)
subtype1	183	28	0.0 - 156.2 (17.6)
subtype2	64	30	0.1 - 211.2 (11.6)
subtype3	144	12	0.1 - 182.3 (15.3)
subtype4	39	5	0.1 - 122.7 (13.7)

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

'METHLYATION CNMF' versus 'AGE'

P value = 2.03e-17 (Kruskal-Wallis (anova)), Q value = 1.9e-15

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

	nPatients	Mean (Std.Dev)
ALL	432	42.9 (13.3)
subtype1	185	38.1 (11.1)
subtype2	64	54.3 (12.3)
subtype3	144	45.5 (12.4)
subtype4	39	37.7 (14.0)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.499 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	192	241
subtype1	79	106
subtype2	31	33
subtype3	61	84
subtype4	21	18

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

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

P value = 0.005 (Kruskal-Wallis (anova)), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	250	87.7 (12.0)
subtype1	116	88.7 (11.8)
subtype2	38	83.4 (12.1)
subtype3	79	89.2 (11.6)
subtype4	17	83.5 (12.7)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	159	108	166
subtype1	98	58	29
subtype2	43	10	11
subtype3	4	29	112
subtype4	14	11	14

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 = 0.034 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	89	344
subtype1	49	136
subtype2	14	50
subtype3	21	124
subtype4	5	34

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.172 (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	6	14	403
subtype1	0	1	5	177
subtype2	1	2	4	57
subtype3	0	3	3	134
subtype4	0	0	2	35

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.837 (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	16	390
subtype1	6	171
subtype2	2	58
subtype3	6	127
subtype4	2	34

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	52	62	69	75

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 6.81e-05 (logrank test), Q value = 0.0051

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	256	57	0.0 - 211.2 (17.1)
subtype1	51	6	0.1 - 82.0 (15.0)
subtype2	61	30	0.1 - 156.2 (16.8)
subtype3	69	10	0.0 - 211.2 (20.4)
subtype4	75	11	0.1 - 138.3 (15.7)

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 = 0.00957 (Kruskal-Wallis (anova)), Q value = 0.61

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

	nPatients	Mean (Std.Dev)
ALL	258	42.4 (13.3)
subtype1	52	37.7 (11.3)
subtype2	62	46.3 (13.6)
subtype3	69	43.3 (13.1)
subtype4	75	41.6 (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 = 0.00889 (Fisher's exact test), Q value = 0.58

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

nPatients	FEMALE	MALE
ALL	111	147
subtype1	19	33
subtype2	33	29
subtype3	20	49
subtype4	39	36

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 = 0.0886 (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	115	86.7 (11.8)
subtype1	27	88.9 (8.9)
subtype2	30	83.7 (14.7)
subtype3	26	90.0 (12.3)
subtype4	32	85.0 (9.5)

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 = 0.00013 (Fisher's exact test), Q value = 0.0094

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	86	70	102
subtype1	20	21	11
subtype2	31	11	20
subtype3	20	21	28
subtype4	15	17	43

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 = 0.126 (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	174
subtype1	15	37
subtype2	20	42
subtype3	30	39
subtype4	19	56

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	246
subtype1	3	49
subtype2	2	60
subtype3	3	65
subtype4	3	72

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.906 (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	233
subtype1	3	48
subtype2	2	56
subtype3	4	60
subtype4	4	69

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	132	59

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.331 (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	256	57	0.0 - 211.2 (17.1)
subtype1	66	9	0.0 - 138.3 (15.4)
subtype2	131	39	0.1 - 211.2 (18.1)
subtype3	59	9	0.1 - 134.3 (15.5)

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 = 0.00512 (Kruskal-Wallis (anova)), Q value = 0.35

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

	nPatients	Mean (Std.Dev)
ALL	258	42.4 (13.3)
subtype1	67	38.0 (10.5)
subtype2	132	44.7 (13.4)
subtype3	59	42.4 (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 = 0.556 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	111	147
subtype1	28	39
subtype2	54	78
subtype3	29	30

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 = 0.379 (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	115	86.7 (11.8)
subtype1	29	89.0 (10.1)
subtype2	57	86.3 (13.2)
subtype3	29	85.2 (10.2)

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 = 0.0953 (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	86	70	102
subtype1	18	23	26
subtype2	53	33	46
subtype3	15	14	30

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 = 0.407 (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	174
subtype1	20	47
subtype2	48	84
subtype3	16	43

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 = 0.276 (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	246
subtype1	5	62
subtype2	5	126
subtype3	1	58

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.931 (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	233
subtype1	4	61
subtype2	6	117
subtype3	3	55

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	110	94	90	116	20

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.42e-13 (logrank test), Q value = 1.3e-11

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	427	75	0.0 - 211.2 (15.9)
subtype1	109	17	0.0 - 130.8 (18.9)
subtype2	93	36	0.1 - 211.2 (12.2)
subtype3	89	7	0.1 - 182.3 (16.4)
subtype4	116	15	0.1 - 164.4 (14.7)
subtype5	20	0	2.5 - 31.4 (14.2)

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 = 3.03e-10 (Kruskal-Wallis (anova)), Q value = 2.7e-08

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

	nPatients	Mean (Std.Dev)
ALL	429	43.0 (13.3)
subtype1	110	36.6 (10.3)
subtype2	94	49.5 (13.6)
subtype3	90	45.1 (12.0)
subtype4	115	42.0 (13.9)
subtype5	20	43.5 (11.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.72 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	190	240
subtype1	43	67
subtype2	46	48
subtype3	41	49
subtype4	51	65
subtype5	9	11

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 = 2.13e-05 (Kruskal-Wallis (anova)), Q value = 0.0016

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

	nPatients	Mean (Std.Dev)
ALL	247	87.8 (12.1)
subtype1	70	92.1 (8.8)
subtype2	59	83.4 (12.7)
subtype3	51	90.2 (11.7)
subtype4	56	84.8 (13.3)
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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	158	108	164
subtype1	55	36	19
subtype2	66	13	15
subtype3	3	17	70
subtype4	31	32	53
subtype5	3	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 = 0.0898 (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	341
subtype1	29	81
subtype2	22	72
subtype3	11	79
subtype4	25	91
subtype5	2	18

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 = 0.226 (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	6	14	400
subtype1	0	0	5	103
subtype2	1	2	4	86
subtype3	0	3	0	84
subtype4	0	1	5	107
subtype5	0	0	0	20

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.767 (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	16	387
subtype1	4	99
subtype2	2	86
subtype3	4	77
subtype4	6	105
subtype5	0	20

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	111	94	104	121

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 7.78e-12 (logrank test), Q value = 7.2e-10

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	427	75	0.0 - 211.2 (15.9)
subtype1	110	17	0.0 - 130.8 (18.8)
subtype2	93	33	0.1 - 211.2 (12.2)
subtype3	104	10	0.1 - 182.3 (16.5)
subtype4	120	15	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 = 9.6e-10 (Kruskal-Wallis (anova)), Q value = 8.3e-08

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

	nPatients	Mean (Std.Dev)
ALL	429	43.0 (13.3)
subtype1	111	37.0 (10.5)
subtype2	94	48.7 (13.8)
subtype3	104	45.8 (12.3)
subtype4	120	41.5 (13.5)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.554 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	190	240
subtype1	43	68
subtype2	45	49
subtype3	46	58
subtype4	56	65

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 = 8.58e-06 (Kruskal-Wallis (anova)), Q value = 0.00072

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

	nPatients	Mean (Std.Dev)
ALL	247	87.8 (12.1)
subtype1	68	91.9 (9.7)
subtype2	58	83.1 (12.6)
subtype3	62	89.5 (12.2)
subtype4	59	85.8 (12.2)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	158	108	164
subtype1	52	40	19
subtype2	67	13	14
subtype3	3	22	79
subtype4	36	33	52

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 = 0.0401 (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	341
subtype1	29	82
subtype2	22	72
subtype3	12	92
subtype4	26	95

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 = 0.315 (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	6	14	400
subtype1	0	0	5	104
subtype2	1	2	3	87
subtype3	0	3	1	96
subtype4	0	1	5	113

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.881 (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	16	387
subtype1	3	100
subtype2	3	87
subtype3	4	90
subtype4	6	110

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	114	89	161	66

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.079 (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	428	74	0.0 - 211.2 (15.8)
subtype1	113	19	0.0 - 117.4 (16.0)
subtype2	89	25	0.1 - 211.2 (17.5)
subtype3	160	21	0.1 - 156.2 (14.1)
subtype4	66	9	0.1 - 182.3 (17.8)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.4 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	429	42.9 (13.3)
subtype1	114	41.4 (13.1)
subtype2	89	42.9 (12.9)
subtype3	160	43.4 (13.6)
subtype4	66	44.7 (13.3)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.719 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	192	238
subtype1	47	67
subtype2	38	51
subtype3	77	84
subtype4	30	36

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

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

P value = 0.0532 (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	248	87.7 (12.1)
subtype1	67	89.6 (10.5)
subtype2	60	85.0 (12.7)
subtype3	87	87.5 (12.3)
subtype4	34	89.7 (12.7)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	158	106	166
subtype1	58	31	25
subtype2	57	16	16
subtype3	41	45	75
subtype4	2	14	50

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 = 0.37 (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	342
subtype1	25	89
subtype2	23	66
subtype3	30	131
subtype4	10	56

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.033 (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	6	14	400
subtype1	1	1	8	102
subtype2	0	1	1	86
subtype3	0	1	5	152
subtype4	0	3	0	60

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.693 (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	16	387
subtype1	5	103
subtype2	2	81
subtype3	8	147
subtype4	1	56

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	182	162	86

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.05e-10 (logrank test), Q value = 2.7e-08

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	428	74	0.0 - 211.2 (15.8)
subtype1	181	28	0.0 - 182.3 (17.8)
subtype2	161	16	0.1 - 164.4 (14.6)
subtype3	86	30	0.1 - 211.2 (12.3)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 2.33e-07 (Kruskal-Wallis (anova)), Q value = 2e-05

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

	nPatients	Mean (Std.Dev)
ALL	429	42.9 (13.3)
subtype1	182	39.7 (11.4)
subtype2	161	42.9 (13.6)
subtype3	86	49.8 (13.8)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.193 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	192	238
subtype1	72	110
subtype2	78	84
subtype3	42	44

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

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

P value = 0.00029 (Kruskal-Wallis (anova)), Q value = 0.021

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

	nPatients	Mean (Std.Dev)
ALL	248	87.7 (12.1)
subtype1	112	90.3 (10.6)
subtype2	86	87.3 (12.6)
subtype3	50	82.8 (12.8)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	158	106	166
subtype1	63	54	65
subtype2	37	39	86
subtype3	58	13	15

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 = 0.425 (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	342
subtype1	42	140
subtype2	28	134
subtype3	18	68

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.563 (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	6	14	400
subtype1	0	2	5	172
subtype2	0	2	5	150
subtype3	1	2	4	78

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.945 (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	16	387
subtype1	6	163
subtype2	7	146
subtype3	3	78

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	157	130	140

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

P value = 0.00245 (logrank test), Q value = 0.17

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	425	74	0.0 - 211.2 (15.7)
subtype1	157	41	0.0 - 182.3 (16.1)
subtype2	129	14	0.1 - 164.4 (17.2)
subtype3	139	19	0.1 - 211.2 (14.1)

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 = 0.359 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	426	43.0 (13.3)
subtype1	157	43.7 (14.1)
subtype2	130	41.3 (11.7)
subtype3	139	43.8 (13.7)

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 = 0.0566 (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	192	235
subtype1	59	98
subtype2	62	68
subtype3	71	69

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 = 0.0287 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	247	87.8 (12.0)
subtype1	83	87.8 (11.0)
subtype2	87	90.1 (11.0)
subtype3	77	85.2 (13.6)

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 = 0.00011 (Fisher's exact test), Q value = 0.0081

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	157	105	165
subtype1	78	39	40
subtype2	42	31	57
subtype3	37	35	68

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 = 0.0181 (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	339
subtype1	43	114
subtype2	18	112
subtype3	27	113

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 = 0.842 (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	6	14	397
subtype1	1	2	6	145
subtype2	0	3	3	120
subtype3	0	1	5	132

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 = 0.385 (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	16	384
subtype1	4	144
subtype2	7	109
subtype3	5	131

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	72	115	167	73

'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	425	74	0.0 - 211.2 (15.7)
subtype1	71	9	0.0 - 117.4 (16.0)
subtype2	115	13	0.1 - 154.2 (17.8)
subtype3	166	21	0.1 - 211.2 (16.7)
subtype4	73	31	0.1 - 133.7 (11.5)

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 = 3.25e-10 (Kruskal-Wallis (anova)), Q value = 2.8e-08

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

	nPatients	Mean (Std.Dev)
ALL	426	43.0 (13.3)
subtype1	72	36.0 (10.6)
subtype2	115	43.1 (11.8)
subtype3	166	42.2 (13.1)
subtype4	73	51.5 (13.9)

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 = 0.052 (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	192	235
subtype1	28	44
subtype2	42	73
subtype3	85	82
subtype4	37	36

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 = 0.000125 (Kruskal-Wallis (anova)), Q value = 0.0092

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

	nPatients	Mean (Std.Dev)
ALL	247	87.8 (12.0)
subtype1	43	90.5 (10.2)
subtype2	77	90.8 (10.4)
subtype3	86	86.5 (13.1)
subtype4	41	82.2 (12.4)

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 = 0.00083

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

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	157	105	165
subtype1	40	21	11
subtype2	30	31	54
subtype3	35	44	88
subtype4	52	9	12

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 = 0.779 (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	339
subtype1	16	56
subtype2	20	95
subtype3	37	130
subtype4	15	58

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 = 0.363 (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	6	14	397
subtype1	0	0	2	69
subtype2	0	3	3	107
subtype3	0	1	5	157
subtype4	1	2	4	64

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 = 0.333 (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	16	384
subtype1	5	62
subtype2	2	102
subtype3	7	155
subtype4	2	65

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 = 433
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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