Correlation between aggregated molecular cancer subtypes and selected clinical features
Cholangiocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1805208
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1805208
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 10 different clustering approaches and 10 clinical features across 36 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • 4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.

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

  • 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 do not correlate to any clinical features.

  • CNMF clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that do not correlate to any clinical features.

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

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

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

  • 5 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 10 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.

Clinical
Features 		Time
to
Death 		YEARS
TO
BIRTH 		PATHOLOGIC
STAGE 		PATHOLOGY
T
STAGE 		PATHOLOGY
N
STAGE 		PATHOLOGY
M
STAGE 		GENDER HISTOLOGICAL
TYPE 		RESIDUAL
TUMOR 		RACE
Statistical Tests logrank test Kruskal-Wallis (anova) Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test Fisher's exact test
Copy Number Ratio CNMF subtypes 0.996
(1.00) 		0.307
(0.922) 		0.836
(0.996) 		0.285
(0.922) 		0.263
(0.922) 		0.212
(0.922) 		0.73
(0.988) 		0.622
(0.943) 		0.37
(0.922) 		0.361
(0.922)
METHLYATION CNMF 0.997
(1.00) 		0.101
(0.922) 		0.359
(0.922) 		0.942
(1.00) 		0.409
(0.922) 		0.836
(0.996) 		0.231
(0.922) 		0.234
(0.922) 		0.0821
(0.922) 		0.8
(0.996)
RPPA CNMF subtypes 0.156
(0.922) 		0.0209
(0.922) 		0.916
(1.00) 		0.774
(0.992) 		0.516
(0.922) 		0.344
(0.922) 		0.808
(0.996) 		0.512
(0.922) 		0.516
(0.922) 		0.329
(0.922)
RPPA cHierClus subtypes 0.493
(0.922) 		0.0524
(0.922) 		0.468
(0.922) 		0.399
(0.922) 		0.286
(0.922) 		0.148
(0.922) 		0.689
(0.988) 		0.512
(0.922) 		0.592
(0.941) 		0.602
(0.941)
RNAseq CNMF subtypes 0.641
(0.943) 		0.0428
(0.922) 		0.225
(0.922) 		0.504
(0.922) 		0.14
(0.922) 		0.387
(0.922) 		0.171
(0.922) 		0.255
(0.922) 		0.636
(0.943) 		0.458
(0.922)
RNAseq cHierClus subtypes 0.12
(0.922) 		0.372
(0.922) 		0.409
(0.922) 		0.722
(0.988) 		0.0143
(0.922) 		0.241
(0.922) 		0.145
(0.922) 		0.558
(0.941) 		0.408
(0.922) 		0.359
(0.922)
MIRSEQ CNMF 0.291
(0.922) 		0.594
(0.941) 		0.48
(0.922) 		0.731
(0.988) 		0.592
(0.941) 		0.351
(0.922) 		1
(1.00) 		0.0611
(0.922) 		0.923
(1.00) 		0.767
(0.992)
MIRSEQ CHIERARCHICAL 0.986
(1.00) 		0.988
(1.00) 		0.703
(0.988) 		0.904
(1.00) 		1
(1.00) 		0.869
(1.00) 		0.05
(0.922) 		0.501
(0.922) 		0.213
(0.922) 		0.466
(0.922)
MIRseq Mature CNMF subtypes 0.593
(0.941) 		0.218
(0.922) 		0.927
(1.00) 		0.585
(0.941) 		0.885
(1.00) 		1
(1.00) 		0.527
(0.924) 		0.292
(0.922) 		0.638
(0.943) 		0.746
(0.992)
MIRseq Mature cHierClus subtypes 0.514
(0.922) 		0.0723
(0.922) 		0.999
(1.00) 		0.825
(0.996) 		1
(1.00) 		0.789
(0.996) 		0.511
(0.922) 		0.142
(0.922) 		0.692
(0.988) 		0.756
(0.992)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1.  Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 14 9 7 6
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.996 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 14 5 0.3 - 48.4 (12.9)
subtype2 9 5 5.6 - 50.7 (30.4)
subtype3 7 5 13.8 - 63.8 (24.4)
subtype4 6 3 0.7 - 65.0 (21.8)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.307 (Kruskal-Wallis (anova)), Q value = 0.92

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

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 14 63.6 (10.3)
subtype2 9 60.3 (16.6)
subtype3 7 58.7 (14.4)
subtype4 6 70.8 (8.9)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.836 (Fisher's exact test), Q value = 1

Table S4.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 7 3 1 1 1 1
subtype2 2 3 0 1 1 2
subtype3 5 2 0 0 0 0
subtype4 5 1 0 0 0 0

Figure S3.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.285 (Fisher's exact test), Q value = 0.92

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 7 4 3
subtype2 2 5 2
subtype3 5 2 0
subtype4 5 1 0

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.263 (Fisher's exact test), Q value = 0.92

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 10 2
subtype2 5 3
subtype3 7 0
subtype4 4 0

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.212 (Fisher's exact test), Q value = 0.92

Table S7.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 11 2
subtype2 5 3
subtype3 7 0
subtype4 5 0

Figure S6.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.73 (Fisher's exact test), Q value = 0.99

Table S8.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 7 7
subtype2 6 3
subtype3 3 4
subtype4 4 2

Figure S7.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.622 (Fisher's exact test), Q value = 0.94

Table S9.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 2 2 10
subtype2 0 2 7
subtype3 0 0 7
subtype4 0 0 6

Figure S8.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.37 (Fisher's exact test), Q value = 0.92

Table S10.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 12 2 0
subtype2 6 1 2
subtype3 5 2 0
subtype4 5 0 1

Figure S9.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.361 (Fisher's exact test), Q value = 0.92

Table S11.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 3 1 10
subtype2 0 0 9
subtype3 0 0 7
subtype4 0 1 5

Figure S10.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RACE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S12.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 14 10 12
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.997 (logrank test), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 14 5 0.3 - 50.7 (15.1)
subtype2 10 6 0.8 - 65.0 (24.8)
subtype3 12 7 1.6 - 63.8 (27.4)

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.92

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 14 66.9 (10.5)
subtype2 10 57.8 (9.3)
subtype3 12 62.8 (16.7)

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.359 (Fisher's exact test), Q value = 0.92

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 8 3 1 0 0 2
subtype2 6 2 0 2 0 0
subtype3 5 4 0 0 2 1

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.942 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 8 4 2
subtype2 6 3 1
subtype3 5 5 2

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.409 (Fisher's exact test), Q value = 0.92

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 10 2
subtype2 7 0
subtype3 9 3

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.836 (Fisher's exact test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 10 2
subtype2 7 2
subtype3 11 1

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.231 (Fisher's exact test), Q value = 0.92

Table S19.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 6 8
subtype2 8 2
subtype3 6 6

Figure S17.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.234 (Fisher's exact test), Q value = 0.92

Table S20.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 2 2 10
subtype2 0 2 8
subtype3 0 0 12

Figure S18.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.0821 (Fisher's exact test), Q value = 0.92

Table S21.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 11 0 3
subtype2 8 2 0
subtype3 9 3 0

Figure S19.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.8 (Fisher's exact test), Q value = 1

Table S22.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 2 1 11
subtype2 0 1 9
subtype3 1 0 11

Figure S20.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RACE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S23.  Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 10 8 12
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.156 (logrank test), Q value = 0.92

Table S24.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 30 15 0.3 - 65.0 (20.8)
subtype1 10 7 0.3 - 63.8 (11.9)
subtype2 8 3 5.6 - 65.0 (20.2)
subtype3 12 5 0.8 - 53.1 (28.5)

Figure S21.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0209 (Kruskal-Wallis (anova)), Q value = 0.92

Table S25.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 30 63.1 (13.8)
subtype1 10 71.1 (2.8)
subtype2 8 65.2 (14.5)
subtype3 12 55.1 (15.1)

Figure S22.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.916 (Fisher's exact test), Q value = 1

Table S26.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 17 6 1 2 2 2
subtype1 5 2 1 0 1 1
subtype2 6 2 0 0 0 0
subtype3 6 2 0 2 1 1

Figure S23.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.774 (Fisher's exact test), Q value = 0.99

Table S27.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 17 9 4
subtype1 5 3 2
subtype2 6 2 0
subtype3 6 4 2

Figure S24.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.516 (Fisher's exact test), Q value = 0.92

Table S28.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 22 4
subtype1 7 2
subtype2 7 0
subtype3 8 2

Figure S25.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.344 (Fisher's exact test), Q value = 0.92

Table S29.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 24 4
subtype1 8 1
subtype2 8 0
subtype3 8 3

Figure S26.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.808 (Fisher's exact test), Q value = 1

Table S30.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 17 13
subtype1 6 4
subtype2 5 3
subtype3 6 6

Figure S27.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.512 (Fisher's exact test), Q value = 0.92

Table S31.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 1 4 25
subtype1 1 2 7
subtype2 0 0 8
subtype3 0 2 10

Figure S28.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.516 (Fisher's exact test), Q value = 0.92

Table S32.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 24 3 3
subtype1 7 1 2
subtype2 7 0 1
subtype3 10 2 0

Figure S29.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RPPA CNMF subtypes' versus 'RACE'

P value = 0.329 (Fisher's exact test), Q value = 0.92

Table S33.  Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 2 26
subtype1 2 0 8
subtype2 0 1 7
subtype3 0 1 11

Figure S30.  Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RACE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S34.  Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels 1 2 3 4
Number of samples 6 7 8 9
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.493 (logrank test), Q value = 0.92

Table S35.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 30 15 0.3 - 65.0 (20.8)
subtype1 6 5 8.9 - 63.8 (15.5)
subtype2 7 2 5.6 - 65.0 (19.8)
subtype3 8 4 0.7 - 48.4 (21.8)
subtype4 9 4 0.3 - 53.1 (26.6)

Figure S31.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0524 (Kruskal-Wallis (anova)), Q value = 0.92

Table S36.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 30 63.1 (13.8)
subtype1 6 70.7 (3.7)
subtype2 7 63.4 (14.6)
subtype3 8 69.0 (9.4)
subtype4 9 52.7 (15.5)

Figure S32.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.468 (Fisher's exact test), Q value = 0.92

Table S37.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 17 6 1 2 2 2
subtype1 3 0 1 0 1 1
subtype2 5 2 0 0 0 0
subtype3 6 2 0 0 0 0
subtype4 3 2 0 2 1 1

Figure S33.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.399 (Fisher's exact test), Q value = 0.92

Table S38.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 17 9 4
subtype1 3 1 2
subtype2 5 2 0
subtype3 6 2 0
subtype4 3 4 2

Figure S34.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.286 (Fisher's exact test), Q value = 0.92

Table S39.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 22 4
subtype1 4 2
subtype2 6 0
subtype3 6 0
subtype4 6 2

Figure S35.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.148 (Fisher's exact test), Q value = 0.92

Table S40.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 24 4
subtype1 5 1
subtype2 7 0
subtype3 7 0
subtype4 5 3

Figure S36.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.689 (Fisher's exact test), Q value = 0.99

Table S41.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 17 13
subtype1 3 3
subtype2 4 3
subtype3 6 2
subtype4 4 5

Figure S37.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.512 (Fisher's exact test), Q value = 0.92

Table S42.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 1 4 25
subtype1 1 1 4
subtype2 0 0 7
subtype3 0 1 7
subtype4 0 2 7

Figure S38.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RPPA cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.592 (Fisher's exact test), Q value = 0.94

Table S43.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 24 3 3
subtype1 4 1 1
subtype2 6 0 1
subtype3 7 0 1
subtype4 7 2 0

Figure S39.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 0.602 (Fisher's exact test), Q value = 0.94

Table S44.  Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 2 2 26
subtype1 1 0 5
subtype2 0 1 6
subtype3 1 1 6
subtype4 0 0 9

Figure S40.  Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RACE'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S45.  Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4 5 6
Number of samples 6 4 5 6 9 6
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.641 (logrank test), Q value = 0.94

Table S46.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 6 3 3.2 - 23.3 (12.2)
subtype2 4 3 14.6 - 46.1 (28.5)
subtype3 5 1 20.6 - 50.7 (35.4)
subtype4 6 3 5.6 - 63.8 (30.9)
subtype5 9 5 0.3 - 65.0 (19.8)
subtype6 6 3 1.6 - 53.1 (29.2)

Figure S41.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0428 (Kruskal-Wallis (anova)), Q value = 0.92

Table S47.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 6 62.3 (12.9)
subtype2 4 44.5 (9.3)
subtype3 5 73.4 (8.6)
subtype4 6 64.3 (7.0)
subtype5 9 65.0 (9.4)
subtype6 6 63.2 (17.2)

Figure S42.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.225 (Fisher's exact test), Q value = 0.92

Table S48.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 4 0 0 0 0 2
subtype2 1 1 0 1 1 0
subtype3 3 1 1 0 0 0
subtype4 4 2 0 0 0 0
subtype5 6 2 0 1 0 0
subtype6 1 3 0 0 1 1

Figure S43.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.504 (Fisher's exact test), Q value = 0.92

Table S49.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 4 1 1
subtype2 1 2 1
subtype3 3 1 1
subtype4 4 2 0
subtype5 6 3 0
subtype6 1 3 2

Figure S44.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.14 (Fisher's exact test), Q value = 0.92

Table S50.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 3 2
subtype2 2 1
subtype3 5 0
subtype4 5 0
subtype5 7 0
subtype6 4 2

Figure S45.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.387 (Fisher's exact test), Q value = 0.92

Table S51.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 3 2
subtype2 2 1
subtype3 5 0
subtype4 6 0
subtype5 7 1
subtype6 5 1

Figure S46.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.171 (Fisher's exact test), Q value = 0.92

Table S52.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 3 3
subtype2 4 0
subtype3 3 2
subtype4 4 2
subtype5 2 7
subtype6 4 2

Figure S47.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.255 (Fisher's exact test), Q value = 0.92

Table S53.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 1 2 3
subtype2 0 1 3
subtype3 1 0 4
subtype4 0 0 6
subtype5 0 1 8
subtype6 0 0 6

Figure S48.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.636 (Fisher's exact test), Q value = 0.94

Table S54.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 5 0 1
subtype2 4 0 0
subtype3 5 0 0
subtype4 5 1 0
subtype5 5 2 2
subtype6 4 2 0

Figure S49.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.458 (Fisher's exact test), Q value = 0.92

Table S55.  Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 1 1 4
subtype2 0 0 4
subtype3 1 0 4
subtype4 1 1 4
subtype5 0 0 9
subtype6 0 0 6

Figure S50.  Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RACE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S56.  Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels 1 2 3 4 5 6 7
Number of samples 4 5 5 9 6 5 2
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 0.92

Table S57.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 34 17 0.3 - 65.0 (21.2)
subtype1 4 3 3.2 - 13.2 (10.0)
subtype2 5 1 14.6 - 50.7 (26.6)
subtype3 5 1 13.8 - 38.7 (23.3)
subtype4 9 5 0.3 - 65.0 (19.8)
subtype5 6 5 5.6 - 63.8 (43.1)
subtype6 5 2 1.6 - 53.1 (18.2)

Figure S51.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.372 (Kruskal-Wallis (anova)), Q value = 0.92

Table S58.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 34 64.6 (11.3)
subtype1 4 65.2 (9.1)
subtype2 5 60.6 (13.1)
subtype3 5 73.0 (7.6)
subtype4 9 65.0 (9.4)
subtype5 6 61.3 (8.0)
subtype6 5 63.0 (19.2)

Figure S52.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.409 (Fisher's exact test), Q value = 0.92

Table S59.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 18 9 1 2 1 3
subtype1 2 0 0 0 0 2
subtype2 2 2 1 0 0 0
subtype3 4 1 0 0 0 0
subtype4 6 2 0 1 0 0
subtype5 3 2 0 1 0 0
subtype6 1 2 0 0 1 1

Figure S53.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.722 (Fisher's exact test), Q value = 0.99

Table S60.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 18 11 5
subtype1 2 1 1
subtype2 2 2 1
subtype3 4 1 0
subtype4 6 3 0
subtype5 3 2 1
subtype6 1 2 2

Figure S54.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0143 (Fisher's exact test), Q value = 0.92

Table S61.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 25 4
subtype1 1 2
subtype2 4 0
subtype3 5 0
subtype4 7 0
subtype5 5 0
subtype6 3 2

Figure S55.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.241 (Fisher's exact test), Q value = 0.92

Table S62.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 26 5
subtype1 1 2
subtype2 4 0
subtype3 5 0
subtype4 7 1
subtype5 5 1
subtype6 4 1

Figure S56.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.145 (Fisher's exact test), Q value = 0.92

Table S63.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 18 16
subtype1 1 3
subtype2 4 1
subtype3 3 2
subtype4 2 7
subtype5 5 1
subtype6 3 2

Figure S57.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.558 (Fisher's exact test), Q value = 0.94

Table S64.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 28
subtype1 1 1 2
subtype2 1 0 4
subtype3 0 1 4
subtype4 0 1 8
subtype5 0 1 5
subtype6 0 0 5

Figure S58.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.408 (Fisher's exact test), Q value = 0.92

Table S65.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 26 5 3
subtype1 3 0 1
subtype2 5 0 0
subtype3 4 1 0
subtype4 5 2 2
subtype5 6 0 0
subtype6 3 2 0

Figure S59.  Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.359 (Fisher's exact test), Q value = 0.92

Table S66.  Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 1 30
subtype1 1 0 3
subtype2 1 0 4
subtype3 0 0 5
subtype4 0 0 9
subtype5 1 1 4
subtype6 0 0 5

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

Clustering Approach #7: 'MIRSEQ CNMF'

Table S67.  Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels 1 2 3 4
Number of samples 10 6 10 10
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.291 (logrank test), Q value = 0.92

Table S68.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 10 2 0.7 - 53.1 (31.0)
subtype2 6 5 0.8 - 65.0 (17.8)
subtype3 10 5 1.6 - 40.4 (18.8)
subtype4 10 6 0.3 - 63.8 (21.4)

Figure S61.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.594 (Kruskal-Wallis (anova)), Q value = 0.94

Table S69.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 10 63.7 (15.8)
subtype2 6 58.8 (11.8)
subtype3 10 65.3 (10.3)
subtype4 10 62.6 (13.8)

Figure S62.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.48 (Fisher's exact test), Q value = 0.92

Table S70.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 5 3 1 0 0 1
subtype2 2 2 0 2 0 0
subtype3 5 2 0 0 1 2
subtype4 7 2 0 0 1 0

Figure S63.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.731 (Fisher's exact test), Q value = 0.99

Table S71.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 5 3 2
subtype2 2 3 1
subtype3 5 3 2
subtype4 7 3 0

Figure S64.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.592 (Fisher's exact test), Q value = 0.94

Table S72.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 8 1
subtype2 4 0
subtype3 7 3
subtype4 7 1

Figure S65.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.351 (Fisher's exact test), Q value = 0.92

Table S73.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 7 1
subtype2 4 2
subtype3 8 2
subtype4 9 0

Figure S66.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

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

Table S74.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 6 4
subtype2 3 3
subtype3 5 5
subtype4 6 4

Figure S67.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0611 (Fisher's exact test), Q value = 0.92

Table S75.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 2 0 8
subtype2 0 2 4
subtype3 0 2 8
subtype4 0 0 10

Figure S68.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.923 (Fisher's exact test), Q value = 1

Table S76.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 8 1 1
subtype2 4 1 1
subtype3 7 2 1
subtype4 9 1 0

Figure S69.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.767 (Fisher's exact test), Q value = 0.99

Table S77.  Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 2 0 8
subtype2 0 0 6
subtype3 0 1 9
subtype4 1 1 8

Figure S70.  Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RACE'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S78.  Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3 4 5 6
Number of samples 8 10 4 5 5 4
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.986 (logrank test), Q value = 1

Table S79.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 8 3 3.2 - 50.7 (18.0)
subtype2 10 4 1.6 - 40.4 (22.0)
subtype3 4 3 5.6 - 63.8 (34.5)
subtype4 5 4 0.7 - 65.0 (21.0)
subtype5 5 3 12.7 - 53.1 (23.1)
subtype6 4 1 0.3 - 47.6 (10.3)

Figure S71.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.988 (Kruskal-Wallis (anova)), Q value = 1

Table S80.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 8 62.1 (11.7)
subtype2 10 66.1 (11.3)
subtype3 4 60.2 (19.7)
subtype4 5 64.4 (11.8)
subtype5 5 58.4 (18.7)
subtype6 4 64.0 (9.8)

Figure S72.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.703 (Fisher's exact test), Q value = 0.99

Table S81.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 4 2 1 0 0 1
subtype2 6 2 0 0 0 2
subtype3 1 2 0 0 1 0
subtype4 2 2 0 1 0 0
subtype5 3 1 0 0 1 0
subtype6 3 0 0 1 0 0

Figure S73.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.904 (Fisher's exact test), Q value = 1

Table S82.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 4 2 2
subtype2 6 3 1
subtype3 1 3 0
subtype4 2 2 1
subtype5 3 1 1
subtype6 3 1 0

Figure S74.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

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

Table S83.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 5 1
subtype2 8 2
subtype3 3 1
subtype4 4 0
subtype5 3 1
subtype6 3 0

Figure S75.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.869 (Fisher's exact test), Q value = 1

Table S84.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 5 1
subtype2 8 2
subtype3 4 0
subtype4 3 1
subtype5 5 0
subtype6 3 1

Figure S76.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.05 (Fisher's exact test), Q value = 0.92

Table S85.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 5 3
subtype2 4 6
subtype3 3 1
subtype4 3 2
subtype5 5 0
subtype6 0 4

Figure S77.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.501 (Fisher's exact test), Q value = 0.92

Table S86.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 2 0 6
subtype2 0 2 8
subtype3 0 0 4
subtype4 0 1 4
subtype5 0 0 5
subtype6 0 1 3

Figure S78.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.213 (Fisher's exact test), Q value = 0.92

Table S87.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 8 0 0
subtype2 8 1 1
subtype3 3 1 0
subtype4 3 0 2
subtype5 3 2 0
subtype6 3 1 0

Figure S79.  Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.466 (Fisher's exact test), Q value = 0.92

Table S88.  Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 2 0 6
subtype2 0 1 9
subtype3 1 0 3
subtype4 0 0 5
subtype5 0 1 4
subtype6 0 0 4

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

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S89.  Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 8 6 7 7 8
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.593 (logrank test), Q value = 0.94

Table S90.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 8 2 0.7 - 53.1 (36.7)
subtype2 6 4 14.6 - 46.1 (19.6)
subtype3 7 4 5.6 - 65.0 (23.3)
subtype4 7 3 1.6 - 40.4 (21.4)
subtype5 8 5 0.3 - 63.8 (12.7)

Figure S81.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.218 (Kruskal-Wallis (anova)), Q value = 0.92

Table S91.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 8 66.4 (16.4)
subtype2 6 53.3 (11.9)
subtype3 7 67.6 (9.9)
subtype4 7 65.1 (5.4)
subtype5 8 61.1 (15.0)

Figure S82.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.927 (Fisher's exact test), Q value = 1

Table S92.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 4 2 1 0 0 1
subtype2 4 1 0 1 0 0
subtype3 5 1 0 0 0 1
subtype4 2 3 0 0 1 1
subtype5 4 2 0 1 1 0

Figure S83.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.585 (Fisher's exact test), Q value = 0.94

Table S93.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 4 2 2
subtype2 4 1 1
subtype3 5 2 0
subtype4 2 3 2
subtype5 4 4 0

Figure S84.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.885 (Fisher's exact test), Q value = 1

Table S94.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 6 1
subtype2 5 0
subtype3 6 1
subtype4 4 2
subtype5 5 1

Figure S85.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

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

Table S95.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 6 1
subtype2 4 1
subtype3 6 1
subtype4 6 1
subtype5 6 1

Figure S86.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.527 (Fisher's exact test), Q value = 0.92

Table S96.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 5 3
subtype2 5 1
subtype3 4 3
subtype4 3 4
subtype5 3 5

Figure S87.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.292 (Fisher's exact test), Q value = 0.92

Table S97.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 2 0 6
subtype2 0 1 5
subtype3 0 2 5
subtype4 0 0 7
subtype5 0 1 7

Figure S88.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.638 (Fisher's exact test), Q value = 0.94

Table S98.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 6 1 1
subtype2 6 0 0
subtype3 4 1 2
subtype4 6 1 0
subtype5 6 2 0

Figure S89.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.746 (Fisher's exact test), Q value = 0.99

Table S99.  Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 1 0 7
subtype2 0 1 5
subtype3 0 0 7
subtype4 1 1 5
subtype5 1 0 7

Figure S90.  Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RACE'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S100.  Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels 1 2 3 4 5
Number of samples 6 8 9 7 6
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.514 (logrank test), Q value = 0.92

Table S101.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 36 18 0.3 - 65.0 (21.2)
subtype1 6 2 11.2 - 53.1 (15.8)
subtype2 8 6 8.9 - 63.8 (32.2)
subtype3 9 2 1.6 - 50.7 (21.4)
subtype4 7 4 0.3 - 38.7 (18.2)
subtype5 6 4 0.7 - 65.0 (13.3)

Figure S91.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0723 (Kruskal-Wallis (anova)), Q value = 0.92

Table S102.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

nPatients Mean (Std.Dev)
ALL 36 63.0 (12.8)
subtype1 6 51.3 (16.8)
subtype2 8 61.5 (6.9)
subtype3 9 71.1 (7.3)
subtype4 7 63.7 (16.4)
subtype5 6 63.8 (10.5)

Figure S92.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.999 (Fisher's exact test), Q value = 1

Table S103.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV STAGE IVA STAGE IVB
ALL 19 9 1 2 2 3
subtype1 3 2 0 0 0 1
subtype2 5 1 0 1 0 1
subtype3 4 2 1 0 1 1
subtype4 4 2 0 0 1 0
subtype5 3 2 0 1 0 0

Figure S93.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.825 (Fisher's exact test), Q value = 1

Table S104.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients T1 T2 T3
ALL 19 12 5
subtype1 3 2 1
subtype2 5 2 1
subtype3 4 2 3
subtype4 4 3 0
subtype5 3 3 0

Figure S94.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

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

Table S105.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients 0 1
ALL 26 5
subtype1 4 1
subtype2 6 1
subtype3 7 2
subtype4 5 1
subtype5 4 0

Figure S95.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.789 (Fisher's exact test), Q value = 1

Table S106.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients 0 1
ALL 28 5
subtype1 4 1
subtype2 6 2
subtype3 8 1
subtype4 6 0
subtype5 4 1

Figure S96.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.511 (Fisher's exact test), Q value = 0.92

Table S107.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 20 16
subtype1 5 1
subtype2 5 3
subtype3 4 5
subtype4 4 3
subtype5 2 4

Figure S97.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.142 (Fisher's exact test), Q value = 0.92

Table S108.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

nPatients CHOLANGIOCARCINOMA; DISTAL CHOLANGIOCARCINOMA; HILAR/PERIHILAR CHOLANGIOCARCINOMA; INTRAHEPATIC
ALL 2 4 30
subtype1 1 0 5
subtype2 0 3 5
subtype3 1 0 8
subtype4 0 0 7
subtype5 0 1 5

Figure S98.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.692 (Fisher's exact test), Q value = 0.99

Table S109.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

nPatients R0 R1 RX
ALL 28 5 3
subtype1 5 1 0
subtype2 6 1 1
subtype3 8 1 0
subtype4 6 1 0
subtype5 3 1 2

Figure S99.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.756 (Fisher's exact test), Q value = 0.99

Table S110.  Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

nPatients ASIAN BLACK OR AFRICAN AMERICAN WHITE
ALL 3 2 31
subtype1 1 1 4
subtype2 0 1 7
subtype3 1 0 8
subtype4 1 0 6
subtype5 0 0 6

Figure S100.  Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RACE'

Methods & Data
Input

  • Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/CHOL-TP/22541054/CHOL-TP.mergedcluster.txt

  • Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/CHOL-TP/22489349/CHOL-TP.merged_data.txt

  • Number of patients = 36

  • Number of clustering approaches = 10

  • Number of selected clinical features = 10

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

Fisher's exact test

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

Q value calculation

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

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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