Correlation between aggregated molecular cancer subtypes and selected clinical features
Ovarian Serous Cystadenocarcinoma (Primary solid tumor)
17 October 2014  |  analyses__2014_10_17
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1Q81C08
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 14 different clustering approaches and 8 clinical features across 587 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • CNMF clustering analysis on array-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on array-based miR expression data identified 10 subtypes that correlate to 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'AGE'.

  • 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.

  • CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.

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

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.

  • 4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.

  • 2 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 14 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, 6 significant findings detected.

Clinical
Features
Time
to
Death
AGE PRIMARY
SITE
OF
DISEASE
KARNOFSKY
PERFORMANCE
SCORE
RADIATIONS
RADIATION
REGIMENINDICATION
COMPLETENESS
OF
RESECTION
RACE ETHNICITY
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
mRNA CNMF subtypes 0.0159
(1.00)
0.00831
(0.773)
0.432
(1.00)
0.14
(1.00)
0.795
(1.00)
0.153
(1.00)
0.0582
(1.00)
0.152
(1.00)
mRNA cHierClus subtypes 0.253
(1.00)
0.0223
(1.00)
0.24
(1.00)
0.407
(1.00)
0.131
(1.00)
0.154
(1.00)
0.76
(1.00)
0.0637
(1.00)
miR CNMF subtypes 0.0391
(1.00)
0.691
(1.00)
0.217
(1.00)
0.669
(1.00)
0.267
(1.00)
0.212
(1.00)
0.285
(1.00)
0.232
(1.00)
miR cHierClus subtypes 0.106
(1.00)
0.00044
(0.0423)
0.802
(1.00)
0.903
(1.00)
0.36
(1.00)
0.268
(1.00)
0.908
(1.00)
0.445
(1.00)
Copy Number Ratio CNMF subtypes 0.493
(1.00)
1.4e-09
(1.38e-07)
0.279
(1.00)
0.769
(1.00)
0.408
(1.00)
0.272
(1.00)
0.401
(1.00)
0.367
(1.00)
METHLYATION CNMF 0.227
(1.00)
4.02e-09
(3.94e-07)
0.487
(1.00)
0.728
(1.00)
1
(1.00)
0.481
(1.00)
0.555
(1.00)
0.553
(1.00)
RPPA CNMF subtypes 0.0245
(1.00)
0.476
(1.00)
0.769
(1.00)
0.743
(1.00)
0.133
(1.00)
0.342
(1.00)
0.886
(1.00)
0.899
(1.00)
RPPA cHierClus subtypes 0.00165
(0.155)
0.0367
(1.00)
0.693
(1.00)
0.253
(1.00)
1
(1.00)
0.879
(1.00)
0.0754
(1.00)
0.0924
(1.00)
RNAseq CNMF subtypes 0.416
(1.00)
0.00134
(0.127)
1
(1.00)
1
(1.00)
0.681
(1.00)
0.469
(1.00)
RNAseq cHierClus subtypes 0.645
(1.00)
0.0889
(1.00)
0.554
(1.00)
0.33
(1.00)
0.265
(1.00)
0.702
(1.00)
0.778
(1.00)
MIRSEQ CNMF 0.301
(1.00)
0.506
(1.00)
0.0208
(1.00)
0.235
(1.00)
0.489
(1.00)
0.552
(1.00)
0.0478
(1.00)
MIRSEQ CHIERARCHICAL 0.28
(1.00)
0.000116
(0.0113)
0.87
(1.00)
0.677
(1.00)
0.685
(1.00)
0.813
(1.00)
0.77
(1.00)
MIRseq Mature CNMF subtypes 0.186
(1.00)
0.434
(1.00)
1
(1.00)
0.0782
(1.00)
MIRseq Mature cHierClus subtypes 0.0195
(1.00)
0.308
(1.00)
0.249
(1.00)
1
(1.00)
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 219 214 129
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0159 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 557 293 0.3 - 180.2 (28.6)
subtype1 216 124 0.3 - 152.0 (30.2)
subtype2 212 93 0.4 - 180.2 (28.2)
subtype3 129 76 0.3 - 119.1 (25.9)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.00831 (Kruskal-Wallis (anova)), Q value = 0.77

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

nPatients Mean (Std.Dev)
ALL 551 59.7 (11.6)
subtype1 213 61.4 (11.6)
subtype2 210 57.9 (11.4)
subtype3 128 60.0 (11.7)

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

'mRNA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.432 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 558 2
subtype1 0 219 0
subtype2 1 212 1
subtype3 1 127 1

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

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

P value = 0.14 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 31 78.1 (13.0)
subtype2 28 76.4 (12.2)
subtype3 19 70.5 (12.2)

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

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

P value = 0.795 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 3 559
subtype1 2 217
subtype2 1 213
subtype3 0 129

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

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.153 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2
ALL 14 27 1
subtype1 6 9 0
subtype2 7 9 0
subtype3 1 9 1

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

'mRNA CNMF subtypes' versus 'RACE'

P value = 0.0582 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 19 24 1 485
subtype1 0 7 12 0 186
subtype2 0 11 9 1 181
subtype3 2 1 3 0 118

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

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.152 (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 11 328
subtype1 6 130
subtype2 5 120
subtype3 0 78

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
Number of samples 313 160 89
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.253 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 557 293 0.3 - 180.2 (28.6)
subtype1 309 155 0.3 - 130.0 (28.6)
subtype2 160 87 0.3 - 180.2 (24.7)
subtype3 88 51 1.0 - 152.0 (32.1)

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

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0223 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 551 59.7 (11.6)
subtype1 307 59.5 (11.4)
subtype2 157 58.4 (12.0)
subtype3 87 62.9 (11.3)

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

'mRNA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.24 (Fisher's exact test), Q value = 1

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 558 2
subtype1 1 312 0
subtype2 1 157 2
subtype3 0 89 0

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

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

P value = 0.407 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 43 76.7 (11.5)
subtype2 21 72.4 (13.4)
subtype3 14 77.1 (15.4)

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

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

P value = 0.131 (Fisher's exact test), Q value = 1

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

nPatients NO YES
ALL 3 559
subtype1 1 312
subtype2 0 160
subtype3 2 87

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

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.154 (Fisher's exact test), Q value = 1

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

nPatients R0 R1 R2
ALL 14 27 1
subtype1 8 11 0
subtype2 2 12 1
subtype3 4 4 0

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

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.76 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 19 24 1 485
subtype1 0 10 14 1 273
subtype2 2 6 6 0 136
subtype3 0 3 4 0 76

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

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0637 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 328
subtype1 8 178
subtype2 0 93
subtype3 3 57

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

Clustering Approach #3: 'miR CNMF subtypes'

Table S19.  Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 157 165 238
'miR CNMF subtypes' versus 'Time to Death'

P value = 0.0391 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 555 293 0.3 - 180.2 (28.5)
subtype1 156 88 0.3 - 130.0 (27.3)
subtype2 162 92 0.3 - 115.9 (24.7)
subtype3 237 113 0.3 - 180.2 (30.4)

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

'miR CNMF subtypes' versus 'AGE'

P value = 0.691 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 549 59.7 (11.6)
subtype1 153 59.3 (12.5)
subtype2 162 59.3 (11.6)
subtype3 234 60.3 (11.1)

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

'miR CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.217 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 556 2
subtype1 0 155 2
subtype2 1 164 0
subtype3 1 237 0

Figure S19.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.669 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 24 76.7 (12.7)
subtype2 23 73.9 (15.3)
subtype3 31 76.1 (10.9)

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

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

P value = 0.267 (Fisher's exact test), Q value = 1

Table S24.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 557
subtype1 0 157
subtype2 0 165
subtype3 3 235

Figure S21.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.212 (Fisher's exact test), Q value = 1

Table S25.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 4 12 0
subtype2 3 9 1
subtype3 7 6 0

Figure S22.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'miR CNMF subtypes' versus 'RACE'

P value = 0.285 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 19 23 1 484
subtype1 0 2 8 0 136
subtype2 2 6 5 0 143
subtype3 0 11 10 1 205

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

'miR CNMF subtypes' versus 'ETHNICITY'

P value = 0.232 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 327
subtype1 5 86
subtype2 1 97
subtype3 5 144

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

Clustering Approach #4: 'miR cHierClus subtypes'

Table S28.  Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels 1 10 2 3 4 5 6 7 8 9
Number of samples 82 57 58 86 35 75 36 36 51 44
'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.106 (logrank test), Q value = 1

Table S29.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 555 293 0.3 - 180.2 (28.5)
subtype1 82 46 0.3 - 130.0 (25.4)
subtype10 56 28 0.8 - 115.9 (25.6)
subtype2 56 35 0.3 - 106.0 (26.0)
subtype3 86 42 0.3 - 125.8 (30.6)
subtype4 35 25 1.2 - 101.8 (22.2)
subtype5 74 36 1.7 - 180.2 (34.3)
subtype6 36 19 1.0 - 73.5 (32.0)
subtype7 36 13 0.5 - 98.0 (30.0)
subtype8 50 25 0.5 - 152.0 (21.6)
subtype9 44 24 1.0 - 107.2 (29.7)

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

'miR cHierClus subtypes' versus 'AGE'

P value = 0.00044 (Kruskal-Wallis (anova)), Q value = 0.042

Table S30.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 549 59.7 (11.6)
subtype1 79 58.6 (12.2)
subtype10 56 55.6 (11.2)
subtype2 58 61.7 (12.3)
subtype3 85 59.3 (11.2)
subtype4 35 60.3 (11.7)
subtype5 74 60.0 (10.0)
subtype6 35 63.3 (10.9)
subtype7 35 62.9 (12.0)
subtype8 49 63.6 (11.4)
subtype9 43 54.3 (11.3)

Figure S26.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.802 (Fisher's exact test), Q value = 1

Table S31.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 556 2
subtype1 0 81 1
subtype10 0 57 0
subtype2 0 58 0
subtype3 1 85 0
subtype4 0 34 1
subtype5 1 74 0
subtype6 0 36 0
subtype7 0 36 0
subtype8 0 51 0
subtype9 0 44 0

Figure S27.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.903 (Kruskal-Wallis (anova)), Q value = 1

Table S32.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 13 73.8 (15.0)
subtype10 6 73.3 (16.3)
subtype2 4 75.0 (10.0)
subtype3 13 80.0 (11.5)
subtype4 3 73.3 (11.5)
subtype5 12 76.7 (16.7)
subtype6 7 77.1 (13.8)
subtype7 6 76.7 (8.2)
subtype8 5 68.0 (11.0)
subtype9 9 75.6 (8.8)

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

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

P value = 0.36 (Fisher's exact test), Q value = 1

Table S33.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 557
subtype1 0 82
subtype10 0 57
subtype2 0 58
subtype3 0 86
subtype4 0 35
subtype5 1 74
subtype6 0 36
subtype7 1 35
subtype8 1 50
subtype9 0 44

Figure S29.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.268 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 14 27 1
subtype1 2 7 0
subtype10 2 1 0
subtype2 1 2 0
subtype3 2 4 0
subtype4 0 5 0
subtype5 1 6 0
subtype6 1 1 0
subtype7 2 1 0
subtype8 2 0 0
subtype9 1 0 1

Figure S30.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'miR cHierClus subtypes' versus 'RACE'

P value = 0.908 (Fisher's exact test), Q value = 1

Table S35.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 2 19 23 1 484
subtype1 0 1 3 0 71
subtype10 1 2 4 0 47
subtype2 0 2 5 0 47
subtype3 0 4 4 1 73
subtype4 0 0 1 0 33
subtype5 1 3 2 0 66
subtype6 0 1 0 0 34
subtype7 0 1 1 0 32
subtype8 0 4 1 0 42
subtype9 0 1 2 0 39

Figure S31.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'miR cHierClus subtypes' versus 'ETHNICITY'

P value = 0.445 (Fisher's exact test), Q value = 1

Table S36.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 327
subtype1 3 45
subtype10 0 36
subtype2 0 24
subtype3 3 56
subtype4 1 18
subtype5 1 44
subtype6 2 23
subtype7 1 19
subtype8 0 34
subtype9 0 28

Figure S32.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #5: 'Copy Number Ratio CNMF subtypes'

Table S37.  Description of clustering approach #5: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 170 201 200
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.493 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 556 290 0.3 - 180.2 (28.0)
subtype1 165 94 0.8 - 119.1 (29.4)
subtype2 196 95 0.3 - 130.0 (30.0)
subtype3 195 101 0.3 - 180.2 (22.5)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 1.4e-09 (Kruskal-Wallis (anova)), Q value = 1.4e-07

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

nPatients Mean (Std.Dev)
ALL 550 59.8 (11.6)
subtype1 163 60.2 (12.2)
subtype2 197 56.1 (10.9)
subtype3 190 63.3 (10.6)

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

'Copy Number Ratio CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.279 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 557 2
subtype1 1 166 0
subtype2 0 198 0
subtype3 1 193 2

Figure S35.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.769 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 81 75.4 (13.7)
subtype1 22 77.3 (11.2)
subtype2 31 74.8 (12.9)
subtype3 28 74.6 (16.4)

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

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

P value = 0.408 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 568
subtype1 1 169
subtype2 0 201
subtype3 2 198

Figure S37.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.272 (Fisher's exact test), Q value = 1

Table S43.  Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 14 26 5 3
subtype1 2 11 0 1
subtype2 3 6 2 1
subtype3 9 9 3 1

Figure S38.  Get High-res Image Clustering Approach #5: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.401 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 18 32 1 475
subtype1 1 3 7 0 145
subtype2 0 9 15 1 163
subtype3 2 6 10 0 167

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.367 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 10 325
subtype1 1 101
subtype2 4 111
subtype3 5 113

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

Clustering Approach #6: 'METHLYATION CNMF'

Table S46.  Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 175 180 219
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.227 (logrank test), Q value = 1

Table S47.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 558 295 0.3 - 180.2 (28.5)
subtype1 170 101 0.3 - 152.0 (26.9)
subtype2 172 86 0.8 - 180.2 (30.1)
subtype3 216 108 0.3 - 130.0 (28.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 4.02e-09 (Kruskal-Wallis (anova)), Q value = 3.9e-07

Table S48.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 553 59.8 (11.6)
subtype1 168 64.5 (10.4)
subtype2 170 57.8 (12.3)
subtype3 215 57.7 (10.9)

Figure S42.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.487 (Fisher's exact test), Q value = 1

Table S49.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 560 2
subtype1 0 170 1
subtype2 0 175 0
subtype3 2 215 1

Figure S43.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.728 (Kruskal-Wallis (anova)), Q value = 1

Table S50.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 22 74.5 (12.6)
subtype2 21 75.2 (14.0)
subtype3 35 76.6 (12.4)

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

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

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

Table S51.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 571
subtype1 1 174
subtype2 1 179
subtype3 1 218

Figure S45.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.481 (Fisher's exact test), Q value = 1

Table S52.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 14 26 2
subtype1 4 8 1
subtype2 4 12 0
subtype3 6 6 1

Figure S46.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'METHLYATION CNMF' versus 'RACE'

P value = 0.555 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 3 18 24 1 486
subtype1 1 3 9 0 151
subtype2 1 8 4 0 151
subtype3 1 7 11 1 184

Figure S47.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #7: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.553 (Fisher's exact test), Q value = 1

Table S54.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 11 329
subtype1 5 102
subtype2 2 104
subtype3 4 123

Figure S48.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #7: 'RPPA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 123 199 85
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0245 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 401 213 0.3 - 180.2 (28.7)
subtype1 120 66 0.4 - 125.8 (31.1)
subtype2 197 92 0.3 - 180.2 (25.1)
subtype3 84 55 0.5 - 89.3 (27.2)

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

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.476 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 400 59.7 (11.8)
subtype1 121 60.0 (12.4)
subtype2 196 59.2 (11.7)
subtype3 83 60.4 (11.4)

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

'RPPA CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.769 (Fisher's exact test), Q value = 1

Table S58.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 403 2
subtype1 1 122 0
subtype2 1 197 1
subtype3 0 84 1

Figure S51.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.743 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 51 74.9 (11.9)
subtype1 21 76.2 (10.2)
subtype2 24 75.0 (12.2)
subtype3 6 70.0 (16.7)

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

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

P value = 0.133 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 405
subtype1 2 121
subtype2 0 199
subtype3 0 85

Figure S53.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.342 (Fisher's exact test), Q value = 1

Table S61.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 13 28 2
subtype1 2 10 0
subtype2 8 9 1
subtype3 3 9 1

Figure S54.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.886 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 16 19 342
subtype1 2 5 6 100
subtype2 1 9 10 169
subtype3 0 2 3 73

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

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.899 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 207
subtype1 2 65
subtype2 5 102
subtype3 1 40

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

Clustering Approach #8: 'RPPA cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 120 98 130 59
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.00165 (logrank test), Q value = 0.16

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

nPatients nDeath Duration Range (Median), Month
ALL 401 213 0.3 - 180.2 (28.7)
subtype1 119 77 0.4 - 125.8 (25.9)
subtype2 95 43 0.3 - 115.9 (28.4)
subtype3 128 59 0.5 - 152.0 (26.4)
subtype4 59 34 0.8 - 180.2 (36.2)

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

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0367 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 400 59.7 (11.8)
subtype1 119 60.7 (12.0)
subtype2 96 58.3 (12.7)
subtype3 127 58.5 (11.1)
subtype4 58 62.5 (11.2)

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

'RPPA cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.693 (Fisher's exact test), Q value = 1

Table S67.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 403 2
subtype1 1 118 1
subtype2 0 98 0
subtype3 0 129 1
subtype4 1 58 0

Figure S59.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.253 (Kruskal-Wallis (anova)), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 51 74.9 (11.9)
subtype1 9 75.6 (8.8)
subtype2 6 83.3 (8.2)
subtype3 28 74.3 (12.0)
subtype4 8 70.0 (15.1)

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

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

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

Table S69.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 405
subtype1 1 119
subtype2 0 98
subtype3 1 129
subtype4 0 59

Figure S61.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.879 (Fisher's exact test), Q value = 1

Table S70.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2
ALL 13 28 2
subtype1 3 11 1
subtype2 4 4 0
subtype3 5 10 1
subtype4 1 3 0

Figure S62.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'COMPLETENESS.OF.RESECTION'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.0754 (Fisher's exact test), Q value = 1

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

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 16 19 342
subtype1 1 2 5 105
subtype2 0 4 1 85
subtype3 2 9 7 103
subtype4 0 1 6 49

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

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0924 (Fisher's exact test), Q value = 1

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

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 207
subtype1 0 64
subtype2 1 44
subtype3 5 75
subtype4 2 24

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

Clustering Approach #9: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 102 67 92
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.416 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 259 147 0.3 - 180.2 (28.2)
subtype1 102 55 0.4 - 180.2 (28.5)
subtype2 66 36 1.0 - 106.0 (34.6)
subtype3 91 56 0.3 - 152.0 (25.0)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00134 (Kruskal-Wallis (anova)), Q value = 0.13

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

nPatients Mean (Std.Dev)
ALL 254 59.0 (10.8)
subtype1 99 59.2 (10.3)
subtype2 66 55.2 (10.6)
subtype3 89 61.5 (10.9)

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

'RNAseq CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

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

Table S76.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 260
subtype1 1 101
subtype2 0 67
subtype3 0 92

Figure S67.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

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

Table S77.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 259
subtype1 1 101
subtype2 0 67
subtype3 1 91

Figure S68.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.681 (Fisher's exact test), Q value = 1

Table S78.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 1 12 16 1 222
subtype1 1 6 5 0 86
subtype2 0 3 3 1 58
subtype3 0 3 8 0 78

Figure S69.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.469 (Fisher's exact test), Q value = 1

Table S79.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 137
subtype1 2 53
subtype2 1 34
subtype3 0 50

Figure S70.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S80.  Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 107 117 37
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.645 (logrank test), Q value = 1

Table S81.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 259 147 0.3 - 180.2 (28.2)
subtype1 106 55 0.4 - 180.2 (27.7)
subtype2 116 68 0.3 - 125.8 (30.3)
subtype3 37 24 1.0 - 152.0 (25.7)

Figure S71.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0889 (Kruskal-Wallis (anova)), Q value = 1

Table S82.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 254 59.0 (10.8)
subtype1 105 59.0 (10.6)
subtype2 113 57.6 (10.5)
subtype3 36 62.9 (11.9)

Figure S72.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

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

Table S83.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 260
subtype1 1 106
subtype2 0 117
subtype3 0 37

Figure S73.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.33 (Kruskal-Wallis (anova)), Q value = 1

Table S84.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 14 74.3 (12.2)
subtype1 11 72.7 (13.5)
subtype2 3 80.0 (0.0)

Figure S74.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

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

Table S85.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 259
subtype1 0 107
subtype2 1 116
subtype3 1 36

Figure S75.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.702 (Fisher's exact test), Q value = 1

Table S86.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER WHITE
ALL 1 12 16 1 222
subtype1 1 6 5 0 90
subtype2 0 4 7 1 102
subtype3 0 2 4 0 30

Figure S76.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.778 (Fisher's exact test), Q value = 1

Table S87.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 3 137
subtype1 2 54
subtype2 1 59
subtype3 0 24

Figure S77.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #11: 'MIRSEQ CNMF'

Table S88.  Description of clustering approach #11: 'MIRSEQ CNMF'

Cluster Labels 1 2 3
Number of samples 124 185 144
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.301 (logrank test), Q value = 1

Table S89.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 450 252 0.3 - 180.2 (30.1)
subtype1 124 78 0.3 - 152.0 (28.6)
subtype2 184 98 0.3 - 180.2 (32.1)
subtype3 142 76 0.3 - 130.0 (28.7)

Figure S78.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

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

Table S90.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 445 59.8 (11.5)
subtype1 123 60.7 (11.7)
subtype2 182 59.4 (11.3)
subtype3 140 59.4 (11.6)

Figure S79.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.0208 (Fisher's exact test), Q value = 1

Table S91.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 450 1
subtype1 2 121 1
subtype2 0 185 0
subtype3 0 144 0

Figure S80.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.235 (Kruskal-Wallis (anova)), Q value = 1

Table S92.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype1 11 81.8 (14.0)
subtype2 45 74.2 (12.5)
subtype3 8 72.5 (14.9)

Figure S81.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.489 (Fisher's exact test), Q value = 1

Table S93.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 450
subtype1 0 124
subtype2 1 184
subtype3 2 142

Figure S82.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.552 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15 21 400
subtype1 2 3 6 106
subtype2 0 6 10 166
subtype3 0 6 5 128

Figure S83.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.0478 (Fisher's exact test), Q value = 1

Table S95.  Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 262
subtype1 0 66
subtype2 7 113
subtype3 1 83

Figure S84.  Get High-res Image Clustering Approach #11: 'MIRSEQ CNMF' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #12: 'MIRSEQ CHIERARCHICAL'

Table S96.  Description of clustering approach #12: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4
Number of samples 105 73 169 106
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.28 (logrank test), Q value = 1

Table S97.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 450 252 0.3 - 180.2 (30.1)
subtype1 105 53 0.3 - 107.2 (32.8)
subtype2 72 45 1.0 - 89.3 (28.0)
subtype3 168 99 0.3 - 180.2 (28.3)
subtype4 105 55 0.3 - 130.0 (30.5)

Figure S85.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.000116 (Kruskal-Wallis (anova)), Q value = 0.011

Table S98.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 445 59.8 (11.5)
subtype1 104 56.9 (11.5)
subtype2 73 62.5 (11.8)
subtype3 166 61.8 (11.1)
subtype4 102 57.4 (10.9)

Figure S86.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.87 (Fisher's exact test), Q value = 1

Table S99.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM OVARY
ALL 2 450 1
subtype1 0 105 0
subtype2 0 73 0
subtype3 2 166 1
subtype4 0 106 0

Figure S87.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.677 (Kruskal-Wallis (anova)), Q value = 1

Table S100.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype1 28 76.4 (11.0)
subtype2 6 70.0 (21.0)
subtype3 26 74.6 (14.5)
subtype4 4 80.0 (0.0)

Figure S88.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.685 (Fisher's exact test), Q value = 1

Table S101.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 450
subtype1 0 105
subtype2 1 72
subtype3 1 168
subtype4 1 105

Figure S89.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.813 (Fisher's exact test), Q value = 1

Table S102.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

nPatients AMERICAN INDIAN OR ALASKA NATIVE ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 15 21 400
subtype1 0 6 4 94
subtype2 0 3 4 61
subtype3 1 3 8 151
subtype4 1 3 5 94

Figure S90.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.77 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

nPatients HISPANIC OR LATINO NOT HISPANIC OR LATINO
ALL 8 262
subtype1 3 70
subtype2 2 45
subtype3 2 90
subtype4 1 57

Figure S91.  Get High-res Image Clustering Approach #12: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'ETHNICITY'

Clustering Approach #13: 'MIRseq Mature CNMF subtypes'

Table S104.  Description of clustering approach #13: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2
Number of samples 12 10
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.186 (logrank test), Q value = 1

Table S105.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 22 14 0.8 - 125.8 (30.4)
subtype1 12 8 0.8 - 125.8 (22.9)
subtype2 10 6 5.4 - 115.9 (59.9)

Figure S92.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.434 (Wilcoxon-test), Q value = 1

Table S106.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 21 60.5 (12.6)
subtype1 12 62.6 (13.1)
subtype2 9 57.7 (12.1)

Figure S93.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

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

Table S107.  Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 21
subtype1 1 11
subtype2 0 10

Figure S94.  Get High-res Image Clustering Approach #13: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

Clustering Approach #14: 'MIRseq Mature cHierClus subtypes'

Table S108.  Description of clustering approach #14: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 6 2 2 6 4 2
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.0195 (logrank test), Q value = 1

Table S109.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 16 10 4.8 - 125.8 (34.0)
subtype1 6 5 4.8 - 42.0 (24.9)
subtype4 6 2 5.4 - 115.9 (59.9)
subtype5 4 3 11.1 - 125.8 (51.8)

Figure S95.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.308 (Kruskal-Wallis (anova)), Q value = 1

Table S110.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 15 57.6 (12.9)
subtype1 6 54.3 (17.4)
subtype4 5 55.2 (7.7)
subtype5 4 65.5 (9.1)

Figure S96.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'PRIMARY.SITE.OF.DISEASE'

P value = 0.249 (Fisher's exact test), Q value = 1

Table S111.  Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 15
subtype1 0 6
subtype4 0 6
subtype5 1 3

Figure S97.  Get High-res Image Clustering Approach #14: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

Methods & Data
Input
  • Cluster data file = OV-TP.mergedcluster.txt

  • Clinical data file = OV-TP.merged_data.txt

  • Number of patients = 587

  • Number of clustering approaches = 14

  • 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)