Correlation between aggregated molecular cancer subtypes and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1JM292M
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1JM292M
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 8 clinical features across 515 patients, 48 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

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

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

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

  • Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death' and 'HISTOLOGICAL_TYPE'.

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

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

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

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

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

  • 6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death',  'YEARS_TO_BIRTH',  'RADIATION_THERAPY',  'KARNOFSKY_PERFORMANCE_SCORE',  'HISTOLOGICAL_TYPE', and 'RACE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 48 significant findings detected.

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

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

Cluster Labels 1 2 3
Number of samples 9 10 8
'mRNA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

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

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

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

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'mRNA CNMF subtypes' versus 'RACE'

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

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

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

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

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

Table S9.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 1 20
subtype1 0 6
subtype2 1 7
subtype3 0 7

Figure S8.  Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S10.  Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 7 7 7 6
'mRNA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

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

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

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

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'RACE'

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

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

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

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

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

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

Cluster Labels 1 2 3
Number of samples 226 111 175
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 208 136 158 13
'METHLYATION CNMF' versus 'Time to Death'

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

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

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

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

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

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

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

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

'METHLYATION CNMF' versus 'GENDER'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

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

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

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

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'METHLYATION CNMF' versus 'RACE'

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

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

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

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 90 95 126 117
'RPPA CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RPPA CNMF subtypes' versus 'GENDER'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RPPA CNMF subtypes' versus 'RACE'

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

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

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

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 83 160 185
'RPPA cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RPPA cHierClus subtypes' versus 'GENDER'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RACE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 129 115 106 135 30
'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

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

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'RACE'

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

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

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

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6 7
Number of samples 134 71 36 68 91 41 74
'RNAseq cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'RACE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 140 106 83 182
'MIRSEQ CNMF' versus 'Time to Death'

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

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

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

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

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

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

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

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

'MIRSEQ CNMF' versus 'GENDER'

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

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

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

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'MIRSEQ CNMF' versus 'RACE'

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

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

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

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

'MIRSEQ CNMF' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels 1 2 3
Number of samples 222 186 103
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 126 119 106 156
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels 1 2 3 4 5 6
Number of samples 98 77 120 57 63 92
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LGG-TP/22553887/LGG-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LGG-TP/22506744/LGG-TP.merged_data.txt

  • Number of patients = 515

  • Number of clustering approaches = 12

  • Number of selected clinical features = 8

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

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)
[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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