Correlation between molecular cancer subtypes and selected clinical features
Overview
Introduction

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

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 6 clinical features across 578 patients, 13 significant findings detected with P value < 0.05.

  • CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death',  'AGE', and 'TUMOR.STAGE'.

  • Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

  • 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 6 subtypes that correlate to 'AGE' and 'TUMOR.STAGE'.

  • 3 subtypes identified in current cancer cohort by 'CN CNMF'. 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 correlate to 'Time to Death'.

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

  • 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 correlate to 'AGE'.

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 12 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE PRIMARY
SITE
OF
DISEASE 		KARNOFSKY
PERFORMANCE
SCORE 		TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION
Statistical Tests logrank test ANOVA Chi-square test ANOVA Chi-square test Fisher's exact test
mRNA CNMF subtypes 0.0275 0.0107 0.568 0.151 0.0364 0.793
mRNA cHierClus subtypes 0.802 0.00529 0.224 0.172 0.266 0.611
miR CNMF subtypes 0.0543 0.622 0.198 0.737 0.126 0.266
miR cHierClus subtypes 0.607 0.00948 0.837 0.0822 0.00314 0.525
CN CNMF 0.423 7.91e-09 0.288 0.544 0.131 0.395
METHLYATION CNMF 0.198 3.66e-10 0.387 0.637 0.29 1
RPPA CNMF subtypes 0.0056 0.58 0.885 0.61 0.333 0.129
RPPA cHierClus subtypes 0.174 0.0408 0.676 0.68 0.181 0.823
RNAseq CNMF subtypes 0.247 0.00506 1 0.104 0.589 1
RNAseq cHierClus subtypes 0.73 0.0371 0.592 0.104 0.456 0.675
MIRseq CNMF subtypes 0.0431 0.594 0.0583 0.207 0.193 0.476
MIRseq cHierClus subtypes 0.563 0.757 0.812 0.577 0.49 0.281
Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 219 211 132
'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.0275 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 557 290 0.3 - 180.2 (28.3)
subtype1 216 125 0.3 - 152.0 (29.6)
subtype2 209 90 0.4 - 180.2 (27.0)
subtype3 132 75 0.3 - 119.1 (26.7)

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 = 0.0107 (ANOVA)

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 (11.5)
subtype2 207 58.0 (11.4)
subtype3 131 59.7 (11.8)

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 = 0.568 (Chi-square test)

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 1 209 1
subtype3 1 130 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 = 0.151 (ANOVA)

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 30 78.0 (13.2)
subtype2 28 76.4 (12.2)
subtype3 20 71.0 (12.1)

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

'mRNA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0364 (Chi-square test)

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

nPatients I II III IV
ALL 16 26 431 85
subtype1 7 9 173 28
subtype2 9 15 153 33
subtype3 0 2 105 24

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

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

P value = 0.793 (Fisher's exact test)

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

nPatients NO YES
ALL 3 559
subtype1 2 217
subtype2 1 210
subtype3 0 132

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8.  Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 233 202 127
'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.802 (logrank test)

Table S9.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 557 290 0.3 - 180.2 (28.3)
subtype1 232 111 0.3 - 119.1 (24.2)
subtype2 199 113 0.3 - 152.0 (30.0)
subtype3 126 66 0.3 - 180.2 (31.5)

Figure S7.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.00529 (ANOVA)

Table S10.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 551 59.7 (11.6)
subtype1 231 58.5 (11.6)
subtype2 195 61.9 (11.8)
subtype3 125 58.7 (11.0)

Figure S8.  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 = 0.224 (Chi-square test)

Table S11.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 558 2
subtype1 2 229 2
subtype2 0 202 0
subtype3 0 127 0

Figure S9.  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 = 0.172 (ANOVA)

Table S12.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 29 73.1 (12.3)
subtype2 20 80.0 (14.5)
subtype3 29 75.2 (11.5)

Figure S10.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'mRNA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.266 (Chi-square test)

Table S13.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 26 431 85
subtype1 4 13 179 36
subtype2 7 9 160 23
subtype3 5 4 92 26

Figure S11.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.611 (Fisher's exact test)

Table S14.  Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 559
subtype1 1 232
subtype2 2 200
subtype3 0 127

Figure S12.  Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15.  Get Full Table 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 = 0.0543 (logrank test)

Table S16.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 555 290 0.3 - 180.2 (28.2)
subtype1 156 86 0.3 - 130.0 (26.4)
subtype2 162 91 0.3 - 115.9 (24.7)
subtype3 237 113 0.3 - 180.2 (30.1)

Figure S13.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.622 (ANOVA)

Table S17.  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 S14.  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 = 0.198 (Chi-square test)

Table S18.  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 S15.  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 = 0.737 (ANOVA)

Table S19.  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 S16.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.126 (Chi-square test)

Table S20.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 25 430 85
subtype1 3 6 114 32
subtype2 7 5 135 17
subtype3 6 14 181 36

Figure S17.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.266 (Fisher's exact test)

Table S21.  Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

Figure S18.  Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22.  Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 66 120 122 102 56 94
'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.607 (logrank test)

Table S23.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 555 290 0.3 - 180.2 (28.2)
subtype1 65 31 0.8 - 115.9 (26.9)
subtype2 119 65 1.0 - 180.2 (28.7)
subtype3 122 65 0.3 - 130.0 (25.5)
subtype4 102 46 0.3 - 125.7 (29.6)
subtype5 54 31 1.8 - 106.0 (22.4)
subtype6 93 52 0.5 - 152.0 (30.4)

Figure S19.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.00948 (ANOVA)

Table S24.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 549 59.7 (11.6)
subtype1 66 56.5 (11.2)
subtype2 119 57.9 (11.2)
subtype3 118 59.9 (12.3)
subtype4 100 60.1 (10.8)
subtype5 56 62.2 (13.2)
subtype6 90 62.2 (10.6)

Figure S20.  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 = 0.837 (Chi-square test)

Table S25.  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 66 0
subtype2 1 118 1
subtype3 0 121 1
subtype4 1 101 0
subtype5 0 56 0
subtype6 0 94 0

Figure S21.  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 = 0.0822 (ANOVA)

Table S26.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 9 75.6 (13.3)
subtype2 19 75.8 (12.6)
subtype3 17 76.5 (14.6)
subtype4 17 76.5 (10.6)
subtype5 5 88.0 (11.0)
subtype6 11 67.3 (10.1)

Figure S22.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'miR cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00314 (Chi-square test)

Table S27.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 25 430 85
subtype1 5 1 48 12
subtype2 0 0 98 21
subtype3 2 7 91 21
subtype4 5 7 73 17
subtype5 1 3 40 11
subtype6 3 7 80 3

Figure S23.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.525 (Chi-square test)

Table S28.  Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 557
subtype1 0 66
subtype2 0 120
subtype3 0 122
subtype4 1 101
subtype5 1 55
subtype6 1 93

Figure S24.  Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'CN CNMF'

Table S29.  Get Full Table Description of clustering approach #5: 'CN CNMF'

Cluster Labels 1 2 3
Number of samples 162 204 186
'CN CNMF' versus 'Time to Death'

P value = 0.423 (logrank test)

Table S30.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 546 283 0.3 - 180.2 (28.3)
subtype1 160 90 0.8 - 119.1 (29.3)
subtype2 202 98 0.3 - 130.0 (30.0)
subtype3 184 95 0.3 - 180.2 (22.3)

Figure S25.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 7.91e-09 (ANOVA)

Table S31.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 541 59.8 (11.6)
subtype1 158 59.5 (12.4)
subtype2 203 56.6 (10.9)
subtype3 180 63.7 (10.5)

Figure S26.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.288 (Chi-square test)

Table S32.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 548 2
subtype1 1 161 0
subtype2 1 203 0
subtype3 0 184 2

Figure S27.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.544 (ANOVA)

Table S33.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 76 75.5 (12.9)
subtype1 18 76.7 (10.3)
subtype2 31 73.5 (13.1)
subtype3 27 77.0 (14.4)

Figure S28.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.131 (Chi-square test)

Table S34.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 26 423 82
subtype1 7 7 123 23
subtype2 4 10 149 41
subtype3 5 9 151 18

Figure S29.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.395 (Fisher's exact test)

Table S35.  Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 549
subtype1 1 161
subtype2 0 204
subtype3 2 184

Figure S30.  Get High-res Image Clustering Approach #5: 'CN CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'METHLYATION CNMF'

Table S36.  Get Full Table Description of clustering approach #6: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 174 181 219
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.198 (logrank test)

Table S37.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 557 291 0.3 - 180.2 (28.3)
subtype1 168 100 0.3 - 152.0 (26.9)
subtype2 173 86 0.8 - 180.2 (28.6)
subtype3 216 105 0.3 - 130.0 (28.7)

Figure S31.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 3.66e-10 (ANOVA)

Table S38.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 553 59.8 (11.6)
subtype1 167 64.6 (10.6)
subtype2 171 57.6 (12.0)
subtype3 215 57.8 (10.9)

Figure S32.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

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

P value = 0.387 (Chi-square test)

Table S39.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 560 2
subtype1 0 169 1
subtype2 0 176 0
subtype3 2 215 1

Figure S33.  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 = 0.637 (ANOVA)

Table S40.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 78 75.6 (12.8)
subtype1 23 74.8 (13.8)
subtype2 20 74.0 (13.1)
subtype3 35 77.1 (12.0)

Figure S34.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.29 (Chi-square test)

Table S41.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 16 26 430 86
subtype1 4 7 135 21
subtype2 3 5 134 32
subtype3 9 14 161 33

Figure S35.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S42.  Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 571
subtype1 1 173
subtype2 1 180
subtype3 1 218

Figure S36.  Get High-res Image Clustering Approach #6: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'RPPA CNMF subtypes'

Table S43.  Get Full Table Description of clustering approach #7: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 128 207 72
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0056 (logrank test)

Table S44.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 400 209 0.3 - 180.2 (28.6)
subtype1 125 71 0.4 - 125.7 (31.1)
subtype2 204 92 0.3 - 180.2 (25.2)
subtype3 71 46 0.5 - 89.3 (25.9)

Figure S37.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.58 (ANOVA)

Table S45.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 400 59.7 (11.8)
subtype1 126 60.0 (12.3)
subtype2 204 59.1 (11.7)
subtype3 70 60.7 (11.3)

Figure S38.  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 = 0.885 (Chi-square test)

Table S46.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 403 2
subtype1 1 126 1
subtype2 1 205 1
subtype3 0 72 0

Figure S39.  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 = 0.61 (ANOVA)

Table S47.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 51 74.9 (11.9)
subtype1 19 76.8 (10.0)
subtype2 27 73.3 (13.6)
subtype3 5 76.0 (8.9)

Figure S40.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.333 (Chi-square test)

Table S48.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 14 19 317 52
subtype1 4 7 96 20
subtype2 9 11 164 20
subtype3 1 1 57 12

Figure S41.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.129 (Fisher's exact test)

Table S49.  Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 405
subtype1 2 126
subtype2 0 207
subtype3 0 72

Figure S42.  Get High-res Image Clustering Approach #7: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'RPPA cHierClus subtypes'

Table S50.  Get Full Table Description of clustering approach #8: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 101 68 145 93
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.174 (logrank test)

Table S51.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 400 209 0.3 - 180.2 (28.6)
subtype1 97 45 0.3 - 115.9 (28.5)
subtype2 68 42 0.8 - 180.2 (35.5)
subtype3 143 84 0.4 - 125.7 (27.5)
subtype4 92 38 0.5 - 152.0 (22.6)

Figure S43.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0408 (ANOVA)

Table S52.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 400 59.7 (11.8)
subtype1 100 57.7 (11.8)
subtype2 67 61.5 (11.1)
subtype3 142 61.2 (12.1)
subtype4 91 58.2 (11.6)

Figure S44.  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 = 0.676 (Chi-square test)

Table S53.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 403 2
subtype1 0 101 0
subtype2 1 67 0
subtype3 1 143 1
subtype4 0 92 1

Figure S45.  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 = 0.68 (ANOVA)

Table S54.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 51 74.9 (11.9)
subtype1 5 80.0 (0.0)
subtype2 9 75.6 (16.7)
subtype3 13 72.3 (10.1)
subtype4 24 75.0 (12.2)

Figure S46.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.181 (Chi-square test)

Table S55.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients I II III IV
ALL 14 19 317 52
subtype1 8 5 76 9
subtype2 2 4 53 9
subtype3 3 5 111 24
subtype4 1 5 77 10

Figure S47.  Get High-res Image Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.823 (Fisher's exact test)

Table S56.  Clustering Approach #8: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 405
subtype1 0 101
subtype2 0 68
subtype3 1 144
subtype4 1 92

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

Clustering Approach #9: 'RNAseq CNMF subtypes'

Table S57.  Get Full Table Description of clustering approach #9: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 100 70 92
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.247 (logrank test)

Table S58.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 260 147 0.3 - 180.2 (28.0)
subtype1 100 55 0.4 - 180.2 (27.7)
subtype2 69 37 1.0 - 116.1 (35.1)
subtype3 91 55 0.3 - 152.0 (25.0)

Figure S49.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00506 (ANOVA)

Table S59.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 255 59.0 (10.8)
subtype1 98 59.7 (10.6)
subtype2 68 55.5 (10.9)
subtype3 89 60.9 (10.6)

Figure S50.  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)

Table S60.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 261
subtype1 1 99
subtype2 0 70
subtype3 0 92

Figure S51.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.104 (ANOVA)

Table S61.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 14 74.3 (12.2)
subtype1 10 72.0 (14.0)
subtype2 1 80.0 (NA)
subtype3 3 80.0 (0.0)

Figure S52.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.589 (Chi-square test)

Table S62.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients II III IV
ALL 18 210 33
subtype1 7 79 14
subtype2 6 53 11
subtype3 5 78 8

Figure S53.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 1 (Fisher's exact test)

Table S63.  Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 260
subtype1 1 99
subtype2 0 70
subtype3 1 91

Figure S54.  Get High-res Image Clustering Approach #9: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'RNAseq cHierClus subtypes'

Table S64.  Get Full Table Description of clustering approach #10: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 104 107 51
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.73 (logrank test)

Table S65.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 260 147 0.3 - 180.2 (28.0)
subtype1 103 54 0.4 - 180.2 (27.9)
subtype2 107 65 0.4 - 152.0 (28.3)
subtype3 50 28 0.3 - 115.9 (27.8)

Figure S55.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0371 (ANOVA)

Table S66.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 255 59.0 (10.8)
subtype1 101 58.8 (10.2)
subtype2 104 60.7 (11.3)
subtype3 50 56.0 (10.6)

Figure S56.  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 = 0.592 (Fisher's exact test)

Table S67.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY
ALL 1 261
subtype1 1 103
subtype2 0 107
subtype3 0 51

Figure S57.  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 = 0.104 (ANOVA)

Table S68.  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 S58.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.456 (Chi-square test)

Table S69.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients II III IV
ALL 18 210 33
subtype1 6 85 13
subtype2 8 88 10
subtype3 4 37 10

Figure S59.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.675 (Fisher's exact test)

Table S70.  Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 2 260
subtype1 0 104
subtype2 2 105
subtype3 0 51

Figure S60.  Get High-res Image Clustering Approach #10: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #11: 'MIRseq CNMF subtypes'

Table S71.  Get Full Table Description of clustering approach #11: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 113 195 146
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.0431 (logrank test)

Table S72.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 451 252 0.3 - 180.2 (30.1)
subtype1 113 74 0.3 - 152.0 (27.0)
subtype2 194 101 0.3 - 180.2 (32.5)
subtype3 144 77 0.3 - 130.0 (28.7)

Figure S61.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.594 (ANOVA)

Table S73.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 446 59.8 (11.5)
subtype1 112 60.8 (11.7)
subtype2 192 59.5 (11.3)
subtype3 142 59.5 (11.6)

Figure S62.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.0583 (Chi-square test)

Table S74.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 451 1
subtype1 2 110 1
subtype2 0 195 0
subtype3 0 146 0

Figure S63.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.207 (ANOVA)

Table S75.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype1 10 82.0 (14.8)
subtype2 46 74.3 (12.4)
subtype3 8 72.5 (14.9)

Figure S64.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.193 (Chi-square test)

Table S76.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients II III IV
ALL 24 352 74
subtype1 4 91 17
subtype2 8 147 38
subtype3 12 114 19

Figure S65.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.476 (Fisher's exact test)

Table S77.  Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 451
subtype1 0 113
subtype2 1 194
subtype3 2 144

Figure S66.  Get High-res Image Clustering Approach #11: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #12: 'MIRseq cHierClus subtypes'

Table S78.  Get Full Table Description of clustering approach #12: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 3 153 298
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.563 (logrank test)

Table S79.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 451 252 0.3 - 180.2 (30.1)
subtype1 3 3 4.5 - 60.6 (33.7)
subtype2 151 82 0.3 - 130.0 (28.6)
subtype3 297 167 0.3 - 180.2 (31.0)

Figure S67.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.757 (ANOVA)

Table S80.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 446 59.8 (11.5)
subtype1 3 62.7 (1.5)
subtype2 149 59.3 (11.9)
subtype3 294 60.0 (11.4)

Figure S68.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

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

P value = 0.812 (Chi-square test)

Table S81.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

nPatients OMENTUM OVARY PERITONEUM (OVARY)
ALL 2 451 1
subtype1 0 3 0
subtype2 0 153 0
subtype3 2 295 1

Figure S69.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'PRIMARY.SITE.OF.DISEASE'

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

P value = 0.577 (ANOVA)

Table S82.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 64 75.3 (13.2)
subtype2 8 72.5 (14.9)
subtype3 56 75.7 (13.1)

Figure S70.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.49 (Chi-square test)

Table S83.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

nPatients II III IV
ALL 24 352 74
subtype1 0 3 0
subtype2 11 120 21
subtype3 13 229 53

Figure S71.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'TUMOR.STAGE'

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

P value = 0.281 (Fisher's exact test)

Table S84.  Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients NO YES
ALL 3 451
subtype1 0 3
subtype2 2 151
subtype3 1 297

Figure S72.  Get High-res Image Clustering Approach #12: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data
Input

  • Cluster data file = OV-TP.mergedcluster.txt

  • Clinical data file = OV-TP.clin.merged.picked.txt

  • Number of patients = 578

  • Number of clustering approaches = 12

  • Number of selected clinical features = 6

  • 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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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

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] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] 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)
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