Correlation between aggregated molecular cancer subtypes and selected clinical features
Brain Lower Grade Glioma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
doi:10.7908/C15B01QW
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C15B01QW
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 8 clinical features across 515 patients, 49 significant findings detected with P value < 0.05 and Q value < 0.25.

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

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

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

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

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

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

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

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

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

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

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

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

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 12 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 49 significant findings detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		GENDER RADIATION
THERAPY 		KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.377
(0.489) 		0.247
(0.359) 		0.102
(0.172) 		0.693
(0.747) 		0.305
(0.401) 		0.0227
(0.0475) 		0.515
(0.595) 		1
(1.00)
mRNA cHierClus subtypes 0.0372
(0.0743) 		0.0787
(0.14) 		0.15
(0.248) 		0.763
(0.805) 		0.243
(0.359) 		0.00811
(0.0185) 		0.225
(0.342) 		0.43
(0.524)
Copy Number Ratio CNMF subtypes 1.89e-15
(2.26e-14) 		4.56e-16
(7.29e-15) 		0.264
(0.378) 		1e-05
(3.69e-05) 		0.000735
(0.00191) 		1e-05
(3.69e-05) 		0.00073
(0.00191) 		1
(1.00)
METHLYATION CNMF 0
(0) 		1.39e-20
(2.68e-19) 		0.514
(0.595) 		1e-05
(3.69e-05) 		0.000561
(0.00158) 		1e-05
(3.69e-05) 		0.0203
(0.0432) 		0.393
(0.496)
RPPA CNMF subtypes 0.000726
(0.00191) 		0.0502
(0.0957) 		0.00375
(0.00878) 		0.383
(0.49) 		0.185
(0.291) 		0.00118
(0.0029) 		0.48
(0.569) 		0.55
(0.622)
RPPA cHierClus subtypes 0.00855
(0.0191) 		0.0687
(0.124) 		0.409
(0.51) 		0.0509
(0.0957) 		0.524
(0.599) 		1e-05
(3.69e-05) 		0.754
(0.804) 		0.283
(0.383)
RNAseq CNMF subtypes 0
(0) 		6.44e-12
(5.62e-11) 		0.623
(0.687) 		1e-05
(3.69e-05) 		0.000118
(0.000397) 		1e-05
(3.69e-05) 		0.296
(0.394) 		0.164
(0.266)
RNAseq cHierClus subtypes 0
(0) 		1.45e-15
(1.98e-14) 		0.279
(0.383) 		1e-05
(3.69e-05) 		0.00053
(0.00154) 		1e-05
(3.69e-05) 		0.177
(0.283) 		0.837
(0.873)
MIRSEQ CNMF 0.0123
(0.0269) 		0.917
(0.936) 		0.69
(0.747) 		0.00199
(0.00478) 		0.0929
(0.162) 		1e-05
(3.69e-05) 		0.269
(0.38) 		0.437
(0.525)
MIRSEQ CHIERARCHICAL 6.06e-14
(6.47e-13) 		1.78e-08
(1.43e-07) 		0.283
(0.383) 		1e-05
(3.69e-05) 		0.000222
(0.000688) 		1e-05
(3.69e-05) 		0.568
(0.634) 		0.233
(0.35)
MIRseq Mature CNMF subtypes 0.000268
(0.000805) 		0.0547
(0.101) 		0.0323
(0.0659) 		0.00014
(0.000448) 		0.0393
(0.0769) 		1e-05
(3.69e-05) 		0.864
(0.892) 		0.0008
(0.00202)
MIRseq Mature cHierClus subtypes 0
(0) 		1.6e-12
(1.54e-11) 		0.0973
(0.167) 		0.00012
(0.000397) 		5.43e-05
(0.000193) 		1e-05
(3.69e-05) 		0.203
(0.314) 		0.431
(0.524)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

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

P value = 0.377 (logrank test), Q value = 0.49

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

nPatients nDeath Duration Range (Median), Month
ALL 27 10 0.1 - 134.3 (50.1)
subtype1 9 4 10.6 - 130.8 (50.1)
subtype2 10 3 0.1 - 78.2 (36.5)
subtype3 8 3 21.0 - 134.3 (75.6)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.247 (Kruskal-Wallis (anova)), Q value = 0.36

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

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

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.102 (Fisher's exact test), Q value = 0.17

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

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

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

'mRNA CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'mRNA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 17 87.1 (13.1)
subtype1 7 81.4 (16.8)
subtype2 7 92.9 (7.6)
subtype3 3 86.7 (11.5)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0227 (Fisher's exact test), Q value = 0.047

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

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

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

'mRNA CNMF subtypes' versus 'RACE'

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

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

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

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

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

P value = 0.0372 (logrank test), Q value = 0.074

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

nPatients nDeath Duration Range (Median), Month
ALL 27 10 0.1 - 134.3 (50.1)
subtype1 7 4 10.6 - 82.0 (43.9)
subtype2 7 4 18.1 - 130.8 (47.9)
subtype3 7 1 0.1 - 78.2 (31.8)
subtype4 6 1 46.1 - 134.3 (84.7)

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

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

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

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.15 (Fisher's exact test), Q value = 0.25

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

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

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

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.763 (Fisher's exact test), Q value = 0.81

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

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

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

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 17 87.1 (13.1)
subtype1 5 84.0 (11.4)
subtype2 5 82.0 (17.9)
subtype3 5 94.0 (8.9)
subtype4 2 90.0 (14.1)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00811 (Fisher's exact test), Q value = 0.019

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

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

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

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.225 (Fisher's exact test), Q value = 0.34

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

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

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.43 (Fisher's exact test), Q value = 0.52

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

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

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

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

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

Cluster Labels 1 2 3
Number of samples 171 120 221
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 511 43.0 (13.3)
subtype1 171 37.7 (11.4)
subtype2 120 51.0 (12.6)
subtype3 220 42.8 (13.1)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 184 293
subtype1 45 116
subtype2 30 79
subtype3 109 98

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

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

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 194 129 189
subtype1 101 44 26
subtype2 57 33 30
subtype3 36 52 133

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 472
subtype1 0 0 3 165
subtype2 1 3 12 104
subtype3 0 5 6 203

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 446
subtype1 10 146
subtype2 8 106
subtype3 14 194

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

Clustering Approach #4: 'METHLYATION CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 224 81 168 42
'METHLYATION CNMF' versus 'Time to Death'

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

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

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

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

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 514 42.9 (13.4)
subtype1 224 38.0 (11.0)
subtype2 81 54.2 (12.3)
subtype3 167 45.3 (12.6)
subtype4 42 38.0 (14.3)

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

'METHLYATION CNMF' versus 'GENDER'

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

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

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

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 186 294
subtype1 64 149
subtype2 14 60
subtype3 91 64
subtype4 17 21

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

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 307 86.6 (12.6)
subtype1 141 87.6 (11.9)
subtype2 50 80.8 (14.3)
subtype3 95 88.8 (11.8)
subtype4 21 83.8 (11.6)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 194 130 191
subtype1 121 70 33
subtype2 53 12 16
subtype3 5 36 127
subtype4 15 12 15

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

'METHLYATION CNMF' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 475
subtype1 0 1 6 215
subtype2 1 3 6 71
subtype3 0 4 5 153
subtype4 0 0 4 36

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

'METHLYATION CNMF' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 449
subtype1 14 194
subtype2 2 75
subtype3 13 144
subtype4 3 36

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

Clustering Approach #5: 'RPPA CNMF subtypes'

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

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

P value = 0.000726 (logrank test), Q value = 0.0019

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

nPatients nDeath Duration Range (Median), Month
ALL 425 95 0.0 - 211.2 (20.0)
subtype1 88 16 0.0 - 107.0 (23.4)
subtype2 94 36 0.1 - 156.2 (19.1)
subtype3 126 21 0.1 - 211.2 (21.5)
subtype4 117 22 0.1 - 154.4 (18.6)

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

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0502 (Kruskal-Wallis (anova)), Q value = 0.096

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

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

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

'RPPA CNMF subtypes' versus 'GENDER'

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

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

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

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

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.383 (Fisher's exact test), Q value = 0.49

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

nPatients NO YES
ALL 152 242
subtype1 37 45
subtype2 29 58
subtype3 42 75
subtype4 44 64

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

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 239 85.7 (12.7)
subtype1 56 86.8 (10.8)
subtype2 53 82.5 (14.7)
subtype3 63 88.1 (11.6)
subtype4 67 85.1 (13.3)

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

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00118 (Fisher's exact test), Q value = 0.0029

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

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

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

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.48 (Fisher's exact test), Q value = 0.57

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

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

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #6: 'RPPA cHierClus subtypes'

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

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

P value = 0.00855 (logrank test), Q value = 0.019

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

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

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

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0687 (Kruskal-Wallis (anova)), Q value = 0.12

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

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

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.409 (Fisher's exact test), Q value = 0.51

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

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

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

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 152 242
subtype1 34 43
subtype2 64 83
subtype3 54 116

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

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.524 (Kruskal-Wallis (anova)), Q value = 0.6

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

nPatients Mean (Std.Dev)
ALL 239 85.7 (12.7)
subtype1 52 87.9 (10.0)
subtype2 88 85.7 (13.2)
subtype3 99 84.5 (13.6)

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

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.754 (Fisher's exact test), Q value = 0.8

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

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

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #7: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5
Number of samples 133 109 109 134 30
'RNAseq CNMF subtypes' versus 'Time to Death'

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

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

nPatients nDeath Duration Range (Median), Month
ALL 512 121 0.0 - 211.2 (20.8)
subtype1 133 24 0.0 - 145.1 (25.9)
subtype2 108 52 0.1 - 211.2 (17.2)
subtype3 108 13 0.1 - 182.3 (21.0)
subtype4 133 27 0.1 - 172.8 (23.3)
subtype5 30 5 2.4 - 98.2 (20.7)

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

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 514 42.9 (13.4)
subtype1 133 36.6 (10.4)
subtype2 109 49.1 (13.6)
subtype3 109 45.4 (12.8)
subtype4 133 41.8 (13.7)
subtype5 30 44.9 (12.3)

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

'RNAseq CNMF subtypes' versus 'GENDER'

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

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

nPatients FEMALE MALE
ALL 230 285
subtype1 52 81
subtype2 53 56
subtype3 51 58
subtype4 61 73
subtype5 13 17

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

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 186 294
subtype1 42 84
subtype2 18 81
subtype3 59 42
subtype4 54 71
subtype5 13 16

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

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000118 (Kruskal-Wallis (anova)), Q value = 4e-04

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

nPatients Mean (Std.Dev)
ALL 307 86.6 (12.6)
subtype1 83 89.4 (10.9)
subtype2 72 81.5 (13.8)
subtype3 65 89.7 (11.9)
subtype4 69 85.4 (12.6)
subtype5 18 87.8 (11.1)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 194 130 191
subtype1 69 41 23
subtype2 77 17 15
subtype3 3 21 85
subtype4 36 38 60
subtype5 9 13 8

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.296 (Fisher's exact test), Q value = 0.39

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 475
subtype1 0 0 6 125
subtype2 1 2 6 99
subtype3 0 4 2 99
subtype4 0 1 6 124
subtype5 0 1 1 28

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.164 (Fisher's exact test), Q value = 0.27

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 449
subtype1 8 113
subtype2 3 100
subtype3 11 90
subtype4 7 120
subtype5 3 26

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

Clustering Approach #8: 'RNAseq cHierClus subtypes'

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

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

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

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

nPatients nDeath Duration Range (Median), Month
ALL 512 121 0.0 - 211.2 (20.8)
subtype1 134 26 0.0 - 145.1 (26.6)
subtype2 71 39 0.1 - 133.7 (14.3)
subtype3 36 4 0.1 - 169.8 (25.2)
subtype4 68 20 0.1 - 172.8 (19.8)
subtype5 90 12 0.1 - 182.3 (21.7)
subtype6 40 11 0.1 - 211.2 (23.2)
subtype7 73 9 0.1 - 154.4 (23.2)

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

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.45e-15 (Kruskal-Wallis (anova)), Q value = 2e-14

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

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

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

'RNAseq cHierClus subtypes' versus 'GENDER'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 186 294
subtype1 42 83
subtype2 12 51
subtype3 20 10
subtype4 22 42
subtype5 51 37
subtype6 5 35
subtype7 34 36

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

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00053 (Kruskal-Wallis (anova)), Q value = 0.0015

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

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

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

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'RACE'

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

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

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

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #9: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3 4
Number of samples 137 106 185 83
'MIRSEQ CNMF' versus 'Time to Death'

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

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

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

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

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 510 43.0 (13.4)
subtype1 137 42.4 (13.4)
subtype2 106 42.8 (13.1)
subtype3 184 43.2 (13.8)
subtype4 83 43.7 (13.1)

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

'MIRSEQ CNMF' versus 'GENDER'

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

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

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

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0929 (Kruskal-Wallis (anova)), Q value = 0.16

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

nPatients Mean (Std.Dev)
ALL 305 86.6 (12.6)
subtype1 83 86.4 (12.6)
subtype2 71 84.4 (12.7)
subtype3 104 87.1 (12.7)
subtype4 47 89.4 (11.9)

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 193 127 191
subtype1 69 36 32
subtype2 65 22 19
subtype3 54 50 81
subtype4 5 19 59

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

'MIRSEQ CNMF' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 471
subtype1 1 1 10 122
subtype2 0 2 3 100
subtype3 0 2 6 174
subtype4 0 3 2 75

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

'MIRSEQ CNMF' versus 'ETHNICITY'

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

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

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

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

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

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

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

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 184 292
subtype1 91 118
subtype2 76 96
subtype3 17 78

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

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000222 (Kruskal-Wallis (anova)), Q value = 0.00069

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

nPatients Mean (Std.Dev)
ALL 305 86.6 (12.6)
subtype1 139 88.8 (11.1)
subtype2 105 87.0 (12.5)
subtype3 61 80.8 (14.3)

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

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

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

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.568 (Fisher's exact test), Q value = 0.63

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

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

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.233 (Fisher's exact test), Q value = 0.35

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

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

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

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 185 161 161
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 506 43.0 (13.4)
subtype1 185 44.4 (14.3)
subtype2 161 40.7 (11.7)
subtype3 160 43.8 (13.7)

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

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

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

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

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

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

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

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

nPatients NO YES
ALL 183 290
subtype1 45 126
subtype2 72 78
subtype3 66 86

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

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0393 (Kruskal-Wallis (anova)), Q value = 0.077

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

nPatients Mean (Std.Dev)
ALL 304 86.7 (12.6)
subtype1 104 85.0 (13.2)
subtype2 106 89.2 (10.8)
subtype3 94 85.6 (13.3)

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

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 191 126 190
subtype1 95 43 47
subtype2 50 41 70
subtype3 46 42 73

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

'MIRseq Mature CNMF subtypes' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 467
subtype1 1 2 9 169
subtype2 0 4 6 147
subtype3 0 2 6 151

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

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 441
subtype1 6 165
subtype2 20 128
subtype3 6 148

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

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 90 138 190 89
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

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

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

nPatients nDeath Duration Range (Median), Month
ALL 504 120 0.0 - 211.2 (20.9)
subtype1 89 14 0.0 - 145.1 (23.7)
subtype2 137 22 0.1 - 172.8 (26.0)
subtype3 189 37 0.1 - 211.2 (22.3)
subtype4 89 47 0.1 - 133.7 (15.7)

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

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

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

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

nPatients Mean (Std.Dev)
ALL 506 43.0 (13.4)
subtype1 90 36.9 (11.3)
subtype2 138 42.4 (12.0)
subtype3 189 42.0 (13.0)
subtype4 89 52.3 (13.8)

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

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

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

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

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

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

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

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

nPatients Mean (Std.Dev)
ALL 304 86.7 (12.6)
subtype1 56 87.3 (11.7)
subtype2 91 90.1 (10.5)
subtype3 104 86.8 (12.6)
subtype4 53 79.8 (14.3)

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

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

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

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

nPatients ASTROCYTOMA OLIGOASTROCYTOMA OLIGODENDROGLIOMA
ALL 191 126 190
subtype1 49 26 15
subtype2 36 37 65
subtype3 45 51 94
subtype4 61 12 16

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

'MIRseq Mature cHierClus subtypes' versus 'RACE'

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

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 1 8 21 467
subtype1 0 0 2 86
subtype2 0 4 5 127
subtype3 0 2 7 177
subtype4 1 2 7 77

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

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

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

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 32 441
subtype1 7 73
subtype2 11 116
subtype3 11 172
subtype4 3 80

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

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LGG-TP/20146758/LGG-TP.mergedcluster.txt

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

  • Number of patients = 515

  • Number of clustering approaches = 12

  • Number of selected clinical features = 8

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

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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