Correlation between aggregated molecular cancer subtypes and selected clinical features
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 8 clinical features across 475 patients, 43 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RACE'.

  • 4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE',  'RADIATIONS_RADIATION_REGIMENINDICATION', and 'RACE'.

  • CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'GENDER', and 'HISTOLOGICAL_TYPE'.

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'YEARS_TO_BIRTH'.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'KARNOFSKY_PERFORMANCE_SCORE', and 'HISTOLOGICAL_TYPE'.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death',  'GENDER',  'HISTOLOGICAL_TYPE', and 'RADIATIONS_RADIATION_REGIMENINDICATION'.

  • 4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  '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, 43 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		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 0.46
(0.6) 		0.247
(0.376) 		0.0993
(0.187) 		0.506
(0.64) 		0.0224
(0.0616) 		0.746
(0.832) 		0.515
(0.642) 		1
(1.00)
mRNA cHierClus subtypes 0.0486
(0.108) 		0.0787
(0.157) 		0.149
(0.265) 		0.312
(0.454) 		0.00826
(0.0248) 		0.485
(0.621) 		0.225
(0.36) 		0.431
(0.575)
Copy Number Ratio CNMF subtypes 6.55e-12
(7.86e-11) 		1.43e-13
(2.29e-12) 		0.184
(0.315) 		0.0377
(0.0953) 		1e-05
(5.05e-05) 		0.348
(0.492) 		0.00217
(0.00744) 		0.868
(0.936)
METHLYATION CNMF 0
(0) 		1.6e-19
(3.85e-18) 		0.556
(0.662) 		0.0129
(0.0363) 		1e-05
(5.05e-05) 		0.0432
(0.101) 		0.042
(0.101) 		0.911
(0.961)
RPPA CNMF subtypes 4.71e-05
(0.000216) 		0.00957
(0.0278) 		0.00812
(0.0248) 		0.0886
(0.174) 		8e-05
(0.000349) 		0.134
(0.243) 		0.951
(0.981) 		0.906
(0.961)
RPPA cHierClus subtypes 0.23
(0.36) 		0.00512
(0.017) 		0.553
(0.662) 		0.379
(0.527) 		0.0952
(0.183) 		0.231
(0.36) 		0.272
(0.408) 		0.933
(0.973)
RNAseq CNMF subtypes 2.32e-13
(3.19e-12) 		6.29e-11
(5.49e-10) 		0.462
(0.6) 		1.77e-05
(8.48e-05) 		1e-05
(5.05e-05) 		0.0451
(0.103) 		0.066
(0.135) 		0.843
(0.919)
RNAseq cHierClus subtypes 0
(0) 		2.26e-14
(4.34e-13) 		0.222
(0.36) 		0.000111
(0.000442) 		1e-05
(5.05e-05) 		0.0547
(0.117) 		0.194
(0.326) 		0.997
(1.00)
MIRSEQ CNMF 0.042
(0.101) 		0.652
(0.764) 		0.71
(0.811) 		0.0232
(0.0619) 		1e-05
(5.05e-05) 		0.317
(0.454) 		0.109
(0.201) 		0.827
(0.913)
MIRSEQ CHIERARCHICAL 1.14e-11
(1.22e-10) 		8.83e-08
(7.07e-07) 		0.232
(0.36) 		0.000327
(0.00121) 		1e-05
(5.05e-05) 		0.415
(0.561) 		0.559
(0.662) 		1
(1.00)
MIRseq Mature CNMF subtypes 0.000731
(0.0026) 		0.163
(0.285) 		0.031
(0.0804) 		0.0535
(0.117) 		9e-05
(0.000376) 		0.00677
(0.0217) 		0.74
(0.832) 		0.407
(0.558)
MIRseq Mature cHierClus subtypes 0
(0) 		1.51e-11
(1.45e-10) 		0.0623
(0.13) 		0.000172
(0.00066) 		1e-05
(5.05e-05) 		0.702
(0.811) 		0.301
(0.444) 		0.525
(0.646)
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 = 0.46 (logrank test), Q value = 0.6

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 (47.9)
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 (61.9)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 0.38

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.0993 (Fisher's exact test), Q value = 0.19

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 = 0.64

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 = 0.0224 (Fisher's exact test), Q value = 0.062

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 = 0.83

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 = 0.64

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 = 0.0486 (logrank test), Q value = 0.11

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 (47.9)
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 'YEARS_TO_BIRTH'

P value = 0.0787 (Kruskal-Wallis (anova)), Q value = 0.16

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.149 (Fisher's exact test), Q value = 0.27

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 = 0.45

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 = 0.00826 (Fisher's exact test), Q value = 0.025

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 = 0.485 (Fisher's exact test), Q value = 0.62

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 = 0.225 (Fisher's exact test), Q value = 0.36

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 = 0.431 (Fisher's exact test), Q value = 0.57

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 156 110 206
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 6.55e-12 (logrank test), Q value = 7.9e-11

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

nPatients nDeath Duration Range (Median), Month
ALL 470 94 0.0 - 211.2 (18.7)
subtype1 156 28 0.1 - 156.2 (20.5)
subtype2 109 43 0.1 - 211.2 (16.0)
subtype3 205 23 0.0 - 182.3 (20.4)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.43e-13 (Kruskal-Wallis (anova)), Q value = 2.3e-12

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

nPatients Mean (Std.Dev)
ALL 471 43.1 (13.4)
subtype1 156 37.8 (11.7)
subtype2 110 50.7 (12.8)
subtype3 205 43.0 (13.1)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.184 (Fisher's exact test), Q value = 0.32

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

nPatients FEMALE MALE
ALL 206 266
subtype1 62 94
subtype2 56 54
subtype3 88 118

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 = 0.0377 (Kruskal-Wallis (anova)), Q value = 0.095

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

nPatients Mean (Std.Dev)
ALL 269 87.7 (12.0)
subtype1 91 88.7 (11.4)
subtype2 64 84.2 (14.0)
subtype3 114 88.9 (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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 176 118 178
subtype1 90 41 25
subtype2 52 31 27
subtype3 34 46 126

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 = 0.348 (Fisher's exact test), Q value = 0.49

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

nPatients NO YES
ALL 92 380
subtype1 34 122
subtype2 24 86
subtype3 34 172

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 = 0.00217 (Fisher's exact test), Q value = 0.0074

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 437
subtype1 0 0 2 151
subtype2 1 3 9 96
subtype3 0 5 4 190

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 = 0.868 (Fisher's exact test), Q value = 0.94

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 419
subtype1 6 136
subtype2 5 99
subtype3 7 184

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 208 72 156 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 473 94 0.0 - 211.2 (18.6)
subtype1 207 34 0.0 - 172.8 (20.7)
subtype2 72 36 0.1 - 211.2 (12.5)
subtype3 155 17 0.1 - 182.3 (20.1)
subtype4 39 7 0.1 - 122.7 (20.1)

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 1.6e-19 (Kruskal-Wallis (anova)), Q value = 3.9e-18

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

nPatients Mean (Std.Dev)
ALL 474 43.0 (13.5)
subtype1 208 38.1 (11.2)
subtype2 72 54.5 (12.4)
subtype3 155 45.6 (12.5)
subtype4 39 37.7 (14.0)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.556 (Fisher's exact test), Q value = 0.66

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

nPatients FEMALE MALE
ALL 208 267
subtype1 87 121
subtype2 33 39
subtype3 67 89
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 = 0.0129 (Kruskal-Wallis (anova)), Q value = 0.036

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

nPatients Mean (Std.Dev)
ALL 270 87.7 (12.0)
subtype1 126 88.7 (11.5)
subtype2 42 83.8 (12.7)
subtype3 85 88.9 (11.8)
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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 176 119 180
subtype1 112 65 31
subtype2 46 12 14
subtype3 4 31 121
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 = 0.0432 (Fisher's exact test), Q value = 0.1

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

nPatients NO YES
ALL 92 383
subtype1 51 157
subtype2 15 57
subtype3 21 135
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 = 0.042 (Fisher's exact test), Q value = 0.1

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 8 15 440
subtype1 0 1 5 199
subtype2 1 3 5 63
subtype3 0 4 3 143
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 = 0.911 (Fisher's exact test), Q value = 0.96

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 422
subtype1 8 185
subtype2 2 66
subtype3 6 137
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 = 4.71e-05 (logrank test), Q value = 0.00022

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

nPatients nDeath Duration Range (Median), Month
ALL 256 59 0.0 - 211.2 (18.7)
subtype1 51 6 0.1 - 107.0 (17.5)
subtype2 61 30 0.1 - 156.2 (16.8)
subtype3 69 10 0.0 - 211.2 (20.4)
subtype4 75 13 0.1 - 138.3 (20.1)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00957 (Kruskal-Wallis (anova)), Q value = 0.028

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

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: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.00812 (Fisher's exact test), Q value = 0.025

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 = 0.17

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 = 8e-05 (Fisher's exact test), Q value = 0.00035

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 = 0.134 (Fisher's exact test), Q value = 0.24

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

nPatients NO YES
ALL 87 171
subtype1 16 36
subtype2 22 40
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 = 0.98

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 = 0.96

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 = 0.23 (logrank test), Q value = 0.36

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

nPatients nDeath Duration Range (Median), Month
ALL 256 59 0.0 - 211.2 (18.7)
subtype1 66 9 0.0 - 138.3 (18.3)
subtype2 131 39 0.1 - 211.2 (18.8)
subtype3 59 11 0.1 - 134.3 (17.9)

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00512 (Kruskal-Wallis (anova)), Q value = 0.017

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

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: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.553 (Fisher's exact test), Q value = 0.66

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 = 0.53

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 = 0.0952 (Fisher's exact test), Q value = 0.18

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 = 0.231 (Fisher's exact test), Q value = 0.36

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

nPatients NO YES
ALL 87 171
subtype1 20 47
subtype2 51 81
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 = 0.272 (Fisher's exact test), Q value = 0.41

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 = 0.933 (Fisher's exact test), Q value = 0.97

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 124 100 99 128 24
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 2.32e-13 (logrank test), Q value = 3.2e-12

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

nPatients nDeath Duration Range (Median), Month
ALL 473 94 0.0 - 211.2 (18.6)
subtype1 124 20 0.0 - 130.8 (21.2)
subtype2 99 40 0.1 - 211.2 (14.4)
subtype3 99 12 0.1 - 182.3 (17.8)
subtype4 127 20 0.1 - 172.8 (20.6)
subtype5 24 2 2.4 - 98.2 (18.2)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 6.29e-11 (Kruskal-Wallis (anova)), Q value = 5.5e-10

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

nPatients Mean (Std.Dev)
ALL 474 43.0 (13.5)
subtype1 124 36.7 (10.6)
subtype2 100 49.2 (13.8)
subtype3 99 45.9 (12.7)
subtype4 127 41.8 (13.8)
subtype5 24 44.0 (12.2)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.462 (Fisher's exact test), Q value = 0.6

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

nPatients FEMALE MALE
ALL 208 267
subtype1 46 78
subtype2 48 52
subtype3 47 52
subtype4 57 71
subtype5 10 14

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 = 1.77e-05 (Kruskal-Wallis (anova)), Q value = 8.5e-05

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

nPatients Mean (Std.Dev)
ALL 270 87.7 (12.0)
subtype1 75 91.7 (8.8)
subtype2 63 83.2 (12.7)
subtype3 56 90.0 (12.1)
subtype4 63 85.1 (12.8)
subtype5 13 89.2 (11.2)

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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 176 119 180
subtype1 64 39 21
subtype2 70 16 14
subtype3 3 17 79
subtype4 34 36 58
subtype5 5 11 8

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 = 0.0451 (Fisher's exact test), Q value = 0.1

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

nPatients NO YES
ALL 92 383
subtype1 31 93
subtype2 23 77
subtype3 11 88
subtype4 25 103
subtype5 2 22

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 = 0.066 (Fisher's exact test), Q value = 0.13

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 440
subtype1 0 0 5 116
subtype2 1 2 5 91
subtype3 0 4 0 91
subtype4 0 1 5 119
subtype5 0 1 0 23

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.843 (Fisher's exact test), Q value = 0.92

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 422
subtype1 5 107
subtype2 2 92
subtype3 4 85
subtype4 6 115
subtype5 1 23

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6 7
Number of samples 124 65 33 61 83 37 72
'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

nPatients nDeath Duration Range (Median), Month
ALL 473 94 0.0 - 211.2 (18.6)
subtype1 124 21 0.0 - 130.8 (23.5)
subtype2 65 31 0.1 - 133.7 (12.2)
subtype3 33 4 0.1 - 169.8 (16.4)
subtype4 61 14 0.1 - 172.8 (17.9)
subtype5 83 11 0.1 - 182.3 (22.1)
subtype6 36 8 0.1 - 211.2 (19.5)
subtype7 71 5 0.1 - 138.3 (20.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 2.26e-14 (Kruskal-Wallis (anova)), Q value = 4.3e-13

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

nPatients Mean (Std.Dev)
ALL 474 43.0 (13.5)
subtype1 124 37.2 (10.8)
subtype2 65 53.6 (12.6)
subtype3 33 50.3 (13.7)
subtype4 61 41.0 (14.3)
subtype5 83 44.6 (12.1)
subtype6 37 39.9 (11.7)
subtype7 71 41.5 (12.9)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.222 (Fisher's exact test), Q value = 0.36

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

nPatients FEMALE MALE
ALL 208 267
subtype1 46 78
subtype2 32 33
subtype3 20 13
subtype4 29 32
subtype5 32 51
subtype6 16 21
subtype7 33 39

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 = 0.000111 (Kruskal-Wallis (anova)), Q value = 0.00044

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

nPatients Mean (Std.Dev)
ALL 270 87.7 (12.0)
subtype1 73 91.8 (9.5)
subtype2 36 82.8 (13.0)
subtype3 20 89.5 (14.3)
subtype4 33 85.8 (12.8)
subtype5 48 89.2 (11.6)
subtype6 26 83.1 (12.3)
subtype7 34 86.5 (11.0)

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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 176 119 180
subtype1 60 43 21
subtype2 42 11 12
subtype3 1 1 31
subtype4 32 21 8
subtype5 2 23 58
subtype6 30 4 3
subtype7 9 16 47

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 = 0.0547 (Fisher's exact test), Q value = 0.12

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

nPatients NO YES
ALL 92 383
subtype1 31 93
subtype2 11 54
subtype3 2 31
subtype4 12 49
subtype5 10 73
subtype6 11 26
subtype7 15 57

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 = 0.194 (Fisher's exact test), Q value = 0.33

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 440
subtype1 0 0 5 116
subtype2 1 1 4 58
subtype3 0 1 0 29
subtype4 0 1 2 56
subtype5 0 4 1 76
subtype6 0 1 0 36
subtype7 0 0 3 69

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.997 (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 18 422
subtype1 5 107
subtype2 2 59
subtype3 1 27
subtype4 3 52
subtype5 3 74
subtype6 1 36
subtype7 3 67

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 124 103 172 72
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.042 (logrank test), Q value = 0.1

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

nPatients nDeath Duration Range (Median), Month
ALL 469 93 0.0 - 211.2 (18.7)
subtype1 123 23 0.0 - 122.5 (18.6)
subtype2 103 32 0.1 - 211.2 (19.6)
subtype3 171 26 0.1 - 156.2 (17.4)
subtype4 72 12 0.1 - 182.3 (20.8)

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.652 (Kruskal-Wallis (anova)), Q value = 0.76

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

nPatients Mean (Std.Dev)
ALL 470 43.0 (13.5)
subtype1 124 42.0 (13.5)
subtype2 103 42.8 (13.2)
subtype3 171 43.4 (13.8)
subtype4 72 44.2 (13.2)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.71 (Fisher's exact test), Q value = 0.81

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

nPatients FEMALE MALE
ALL 208 263
subtype1 51 73
subtype2 43 60
subtype3 81 91
subtype4 33 39

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0232 (Kruskal-Wallis (anova)), Q value = 0.062

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

nPatients Mean (Std.Dev)
ALL 268 87.7 (12.0)
subtype1 70 89.9 (10.4)
subtype2 66 84.7 (12.6)
subtype3 93 87.5 (12.4)
subtype4 39 89.5 (12.1)

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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 175 116 180
subtype1 61 33 30
subtype2 62 22 19
subtype3 48 46 78
subtype4 4 15 53

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 = 0.317 (Fisher's exact test), Q value = 0.45

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

nPatients NO YES
ALL 91 380
subtype1 26 98
subtype2 25 78
subtype3 30 142
subtype4 10 62

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.109 (Fisher's exact test), Q value = 0.2

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 436
subtype1 1 1 8 111
subtype2 0 2 2 97
subtype3 0 2 5 162
subtype4 0 3 0 66

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.827 (Fisher's exact test), Q value = 0.91

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 418
subtype1 5 110
subtype2 4 90
subtype3 8 156
subtype4 1 62

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 202 172 97
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 1.14e-11 (logrank test), Q value = 1.2e-10

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

nPatients nDeath Duration Range (Median), Month
ALL 469 93 0.0 - 211.2 (18.7)
subtype1 201 34 0.0 - 182.3 (23.2)
subtype2 171 23 0.1 - 172.8 (18.9)
subtype3 97 36 0.1 - 211.2 (14.5)

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 8.83e-08 (Kruskal-Wallis (anova)), Q value = 7.1e-07

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

nPatients Mean (Std.Dev)
ALL 470 43.0 (13.5)
subtype1 202 40.0 (11.7)
subtype2 171 42.7 (13.8)
subtype3 97 49.9 (14.0)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.232 (Fisher's exact test), Q value = 0.36

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

nPatients FEMALE MALE
ALL 208 263
subtype1 80 122
subtype2 82 90
subtype3 46 51

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000327 (Kruskal-Wallis (anova)), Q value = 0.0012

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

nPatients Mean (Std.Dev)
ALL 268 87.7 (12.0)
subtype1 122 90.2 (10.3)
subtype2 92 87.4 (12.7)
subtype3 54 82.8 (12.9)

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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 175 116 180
subtype1 68 60 74
subtype2 43 40 89
subtype3 64 16 17

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 = 0.415 (Fisher's exact test), Q value = 0.56

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

nPatients NO YES
ALL 91 380
subtype1 44 158
subtype2 28 144
subtype3 19 78

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.559 (Fisher's exact test), Q value = 0.66

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 436
subtype1 0 3 5 189
subtype2 0 3 5 159
subtype3 1 2 5 88

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 1 (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 18 418
subtype1 8 177
subtype2 7 154
subtype3 3 87

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 173 143 152
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.000731 (logrank test), Q value = 0.0026

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

nPatients nDeath Duration Range (Median), Month
ALL 466 93 0.0 - 211.2 (18.7)
subtype1 173 50 0.0 - 182.3 (17.6)
subtype2 142 18 0.1 - 169.8 (21.8)
subtype3 151 25 0.1 - 211.2 (17.9)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.163 (Kruskal-Wallis (anova)), Q value = 0.29

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

nPatients Mean (Std.Dev)
ALL 467 43.1 (13.5)
subtype1 173 44.1 (14.4)
subtype2 143 41.0 (11.8)
subtype3 151 43.9 (13.8)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.031 (Fisher's exact test), Q value = 0.08

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

nPatients FEMALE MALE
ALL 208 260
subtype1 64 109
subtype2 66 77
subtype3 78 74

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 = 0.0535 (Kruskal-Wallis (anova)), Q value = 0.12

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

nPatients Mean (Std.Dev)
ALL 267 87.8 (12.0)
subtype1 89 87.9 (11.2)
subtype2 93 89.9 (10.9)
subtype3 85 85.4 (13.5)

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 = 9e-05 (Fisher's exact test), Q value = 0.00038

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 174 115 179
subtype1 86 42 45
subtype2 46 34 63
subtype3 42 39 71

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 = 0.00677 (Fisher's exact test), Q value = 0.022

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

nPatients NO YES
ALL 91 377
subtype1 46 127
subtype2 18 125
subtype3 27 125

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 = 0.74 (Fisher's exact test), Q value = 0.83

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 433
subtype1 1 2 7 159
subtype2 0 4 3 131
subtype3 0 2 5 143

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 = 0.407 (Fisher's exact test), Q value = 0.56

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 415
subtype1 5 155
subtype2 8 120
subtype3 5 140

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 83 125 178 82
'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 466 93 0.0 - 211.2 (18.7)
subtype1 82 12 0.0 - 122.5 (22.1)
subtype2 125 16 0.1 - 172.8 (21.4)
subtype3 177 27 0.1 - 211.2 (19.6)
subtype4 82 38 0.1 - 133.7 (13.2)

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

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.51e-11 (Kruskal-Wallis (anova)), Q value = 1.5e-10

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

nPatients Mean (Std.Dev)
ALL 467 43.1 (13.5)
subtype1 83 36.6 (11.4)
subtype2 125 42.7 (12.0)
subtype3 177 42.1 (13.0)
subtype4 82 52.2 (14.1)

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0623 (Fisher's exact test), Q value = 0.13

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

nPatients FEMALE MALE
ALL 208 260
subtype1 31 52
subtype2 47 78
subtype3 89 89
subtype4 41 41

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 = 0.000172 (Kruskal-Wallis (anova)), Q value = 0.00066

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

nPatients Mean (Std.Dev)
ALL 267 87.8 (12.0)
subtype1 48 90.4 (9.9)
subtype2 81 90.5 (10.2)
subtype3 93 87.0 (12.8)
subtype4 45 81.8 (13.0)

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 = 5.1e-05

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 174 115 179
subtype1 45 24 14
subtype2 33 33 59
subtype3 40 47 91
subtype4 56 11 15

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 = 0.702 (Fisher's exact test), Q value = 0.81

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

nPatients NO YES
ALL 91 377
subtype1 18 65
subtype2 20 105
subtype3 37 141
subtype4 16 66

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 = 0.301 (Fisher's exact test), Q value = 0.44

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 15 433
subtype1 0 0 2 79
subtype2 0 4 3 116
subtype3 0 2 5 166
subtype4 1 2 5 72

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 = 0.525 (Fisher's exact test), Q value = 0.65

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 18 415
subtype1 5 69
subtype2 3 109
subtype3 8 163
subtype4 2 74

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LGG-TP/15115118/LGG-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LGG-TP/15082624/LGG-TP.merged_data.txt

  • Number of patients = 475

  • Number of clustering approaches = 12

  • Number of selected clinical features = 8

  • Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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