Correlation between aggregated molecular cancer subtypes and selected clinical features
Liver Hepatocellular Carcinoma (Primary solid tumor)
15 January 2014  |  analyses__2014_01_15
Maintainer Information
Citation Information
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1K9362Z
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 8 different clustering approaches and 8 clinical features across 128 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.

  • 3 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 sequencing-based mRNA expression data identified 9 subtypes that correlate to 'GENDER'.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'GENDER'.

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

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

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

  • 3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.

Clinical
Features
Time
to
Death
AGE NEOPLASM
DISEASESTAGE
PATHOLOGY
T
STAGE
PATHOLOGY
N
STAGE
PATHOLOGY
M
STAGE
GENDER COMPLETENESS
OF
RESECTION
Statistical Tests logrank test ANOVA Chi-square test Chi-square test Fisher's exact test Chi-square test Fisher's exact test Chi-square test
Copy Number Ratio CNMF subtypes 0.13
(1.00)
0.569
(1.00)
0.262
(1.00)
0.789
(1.00)
1
(1.00)
0.492
(1.00)
0.453
(1.00)
0.103
(1.00)
METHLYATION CNMF 0.34
(1.00)
0.0289
(1.00)
0.655
(1.00)
0.856
(1.00)
0.775
(1.00)
0.178
(1.00)
0.133
(1.00)
0.794
(1.00)
RNAseq CNMF subtypes 0.578
(1.00)
0.00633
(0.373)
0.106
(1.00)
0.0564
(1.00)
0.677
(1.00)
0.646
(1.00)
0.00043
(0.0267)
0.13
(1.00)
RNAseq cHierClus subtypes 0.566
(1.00)
0.00187
(0.114)
0.134
(1.00)
0.00917
(0.532)
0.862
(1.00)
0.667
(1.00)
0.000184
(0.0116)
0.291
(1.00)
MIRSEQ CNMF 0.151
(1.00)
0.0338
(1.00)
0.346
(1.00)
0.394
(1.00)
0.488
(1.00)
0.275
(1.00)
0.475
(1.00)
0.306
(1.00)
MIRSEQ CHIERARCHICAL 0.0634
(1.00)
0.00284
(0.171)
0.947
(1.00)
0.968
(1.00)
0.717
(1.00)
0.469
(1.00)
0.295
(1.00)
0.0339
(1.00)
MIRseq Mature CNMF subtypes 0.808
(1.00)
0.0505
(1.00)
0.149
(1.00)
0.454
(1.00)
0.116
(1.00)
0.241
(1.00)
0.523
(1.00)
0.522
(1.00)
MIRseq Mature cHierClus subtypes 0.12
(1.00)
8.51e-05
(0.00545)
0.917
(1.00)
0.919
(1.00)
1
(1.00)
0.293
(1.00)
0.213
(1.00)
0.34
(1.00)
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
Number of samples 35 44 44
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.13 (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 119 54 0.0 - 113.0 (14.9)
subtype1 33 18 0.1 - 90.7 (14.4)
subtype2 43 17 0.1 - 107.1 (25.3)
subtype3 43 19 0.0 - 113.0 (13.6)

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 'AGE'

P value = 0.569 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 121 61.6 (13.7)
subtype1 34 63.7 (13.1)
subtype2 44 60.5 (13.5)
subtype3 43 61.1 (14.6)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.262 (Chi-square test), Q value = 1

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

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 48 27 2 27 3 4 1 1 1
subtype1 15 7 0 9 0 0 1 1 0
subtype2 20 7 0 10 1 3 0 0 0
subtype3 13 13 2 8 2 1 0 0 1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.789 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 51 30 36 6
subtype1 16 8 10 1
subtype2 21 9 12 2
subtype3 14 13 14 3

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

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

nPatients 0 1
ALL 80 3
subtype1 18 1
subtype2 34 1
subtype3 28 1

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 = 0.492 (Chi-square test), Q value = 1

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

nPatients M0 M1 MX
ALL 96 2 25
subtype1 24 1 10
subtype2 36 0 8
subtype3 36 1 7

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 = 0.453 (Fisher's exact test), Q value = 1

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

nPatients FEMALE MALE
ALL 46 77
subtype1 10 25
subtype2 18 26
subtype3 18 26

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 'COMPLETENESS.OF.RESECTION'

P value = 0.103 (Chi-square test), Q value = 1

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

nPatients R0 R1 R2 RX
ALL 99 10 1 8
subtype1 28 5 1 0
subtype2 36 2 0 2
subtype3 35 3 0 6

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S10.  Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 35 37 55
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.34 (logrank test), Q value = 1

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 122 54 0.0 - 113.0 (14.6)
subtype1 34 13 0.1 - 107.1 (17.1)
subtype2 36 17 0.1 - 113.0 (14.3)
subtype3 52 24 0.0 - 102.7 (14.5)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.0289 (ANOVA), Q value = 1

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 125 61.4 (13.9)
subtype1 35 57.0 (14.5)
subtype2 37 60.5 (15.0)
subtype3 53 64.9 (12.0)

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.655 (Chi-square test), Q value = 1

Table S13.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 48 27 2 30 3 5 1 1 1
subtype1 14 7 0 8 2 3 0 0 0
subtype2 12 7 1 9 1 1 1 0 1
subtype3 22 13 1 13 0 1 0 1 0

Figure S11.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.856 (Chi-square test), Q value = 1

Table S14.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 51 30 39 7
subtype1 15 8 10 2
subtype2 12 8 14 3
subtype3 24 14 15 2

Figure S12.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.775 (Fisher's exact test), Q value = 1

Table S15.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 83 3
subtype1 29 1
subtype2 24 0
subtype3 30 2

Figure S13.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.178 (Chi-square test), Q value = 1

Table S16.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 101 2 24
subtype1 30 0 5
subtype2 29 2 6
subtype3 42 0 13

Figure S14.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

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

Table S17.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 49 78
subtype1 15 20
subtype2 18 19
subtype3 16 39

Figure S15.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.794 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 102 10 1 9
subtype1 29 2 0 2
subtype2 29 4 1 3
subtype3 44 4 0 4

Figure S16.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels 1 2 3 4 5 6 7 8 9
Number of samples 17 14 13 23 20 3 15 10 9
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.578 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 120 53 0.0 - 113.0 (14.3)
subtype1 16 7 0.2 - 60.4 (12.1)
subtype2 14 7 0.1 - 113.0 (12.5)
subtype3 12 4 0.0 - 80.8 (51.5)
subtype4 23 13 0.3 - 102.7 (21.8)
subtype5 20 8 0.4 - 93.7 (13.9)
subtype6 3 1 1.2 - 16.4 (13.8)
subtype7 15 4 0.1 - 49.1 (6.0)
subtype8 8 2 0.3 - 61.5 (9.7)
subtype9 9 7 0.1 - 79.7 (13.8)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00633 (ANOVA), Q value = 0.37

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

nPatients Mean (Std.Dev)
ALL 122 61.5 (13.7)
subtype1 17 58.0 (16.4)
subtype2 14 60.0 (13.0)
subtype3 13 61.4 (10.4)
subtype4 22 69.0 (8.7)
subtype5 20 62.1 (15.4)
subtype6 3 69.0 (5.6)
subtype7 15 51.4 (13.8)
subtype8 9 69.2 (11.4)
subtype9 9 58.3 (12.0)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.106 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 47 27 2 29 2 5 1 1 1
subtype1 2 4 0 8 0 1 0 0 0
subtype2 2 4 0 3 0 2 0 0 1
subtype3 7 2 0 2 0 0 0 0 0
subtype4 14 1 1 5 0 0 0 0 0
subtype5 8 5 0 6 0 0 0 0 0
subtype6 1 2 0 0 0 0 0 0 0
subtype7 4 5 1 3 1 1 0 0 0
subtype8 5 3 0 0 0 1 0 1 0
subtype9 4 1 0 2 1 0 1 0 0

Figure S19.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0564 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 50 30 37 7
subtype1 2 4 9 2
subtype2 2 6 4 2
subtype3 8 2 3 0
subtype4 14 1 6 2
subtype5 8 6 6 0
subtype6 1 2 0 0
subtype7 5 5 5 0
subtype8 6 3 0 1
subtype9 4 1 4 0

Figure S20.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.677 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 10 0
subtype2 9 1
subtype3 9 0
subtype4 14 0
subtype5 12 0
subtype6 2 0
subtype7 11 1
subtype8 7 1
subtype9 6 0

Figure S21.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.646 (Chi-square test), Q value = 1

Table S25.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 97 2 25
subtype1 13 0 4
subtype2 10 1 3
subtype3 10 0 3
subtype4 18 0 5
subtype5 15 0 5
subtype6 3 0 0
subtype7 14 0 1
subtype8 7 0 3
subtype9 7 1 1

Figure S22.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00043 (Chi-square test), Q value = 0.027

Table S26.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 47 77
subtype1 5 12
subtype2 11 3
subtype3 5 8
subtype4 10 13
subtype5 0 20
subtype6 2 1
subtype7 9 6
subtype8 3 7
subtype9 2 7

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

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

P value = 0.13 (Chi-square test), Q value = 1

Table S27.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 100 10 1 8
subtype1 10 4 0 2
subtype2 11 1 0 2
subtype3 13 0 0 0
subtype4 18 2 0 3
subtype5 17 1 0 0
subtype6 2 1 0 0
subtype7 14 0 0 1
subtype8 7 1 0 0
subtype9 8 0 1 0

Figure S24.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels 1 2 3 4
Number of samples 20 20 29 55
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.566 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 120 53 0.0 - 113.0 (14.3)
subtype1 20 6 0.4 - 93.7 (13.9)
subtype2 17 6 0.0 - 80.8 (19.8)
subtype3 29 15 0.3 - 102.7 (21.8)
subtype4 54 26 0.1 - 113.0 (12.1)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00187 (ANOVA), Q value = 0.11

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

nPatients Mean (Std.Dev)
ALL 122 61.5 (13.7)
subtype1 20 62.5 (14.7)
subtype2 19 57.2 (13.9)
subtype3 28 69.6 (8.6)
subtype4 55 58.6 (13.8)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.134 (Chi-square test), Q value = 1

Table S31.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 47 27 2 29 2 5 1 1 1
subtype1 10 5 0 5 0 0 0 0 0
subtype2 10 5 0 2 0 1 0 1 0
subtype3 17 2 1 6 0 0 0 0 0
subtype4 10 15 1 16 2 4 1 0 1

Figure S27.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00917 (Chi-square test), Q value = 0.53

Table S32.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 50 30 37 7
subtype1 10 5 5 0
subtype2 12 5 2 1
subtype3 17 2 8 2
subtype4 11 18 22 4

Figure S28.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.862 (Fisher's exact test), Q value = 1

Table S33.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 13 0
subtype2 13 1
subtype3 18 0
subtype4 36 2

Figure S29.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.667 (Chi-square test), Q value = 1

Table S34.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 97 2 25
subtype1 16 0 4
subtype2 14 0 6
subtype3 23 0 6
subtype4 44 2 9

Figure S30.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000184 (Fisher's exact test), Q value = 0.012

Table S35.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 47 77
subtype1 0 20
subtype2 8 12
subtype3 11 18
subtype4 28 27

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

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

P value = 0.291 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 100 10 1 8
subtype1 18 0 0 0
subtype2 18 0 0 0
subtype3 22 4 0 3
subtype4 42 6 1 5

Figure S32.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels 1 2 3
Number of samples 38 32 53
'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.151 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 118 52 0.0 - 113.0 (14.6)
subtype1 36 13 0.0 - 80.8 (20.1)
subtype2 30 19 0.1 - 102.7 (16.4)
subtype3 52 20 0.2 - 113.0 (14.2)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0338 (ANOVA), Q value = 1

Table S39.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 121 61.4 (13.9)
subtype1 38 56.7 (13.9)
subtype2 30 64.8 (13.7)
subtype3 53 62.9 (13.4)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.346 (Chi-square test), Q value = 1

Table S40.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 46 26 2 29 3 5 1 1 1
subtype1 12 9 1 10 0 2 1 0 0
subtype2 13 2 1 7 2 2 0 1 0
subtype3 21 15 0 12 1 1 0 0 1

Figure S35.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.394 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 49 29 38 7
subtype1 13 10 13 2
subtype2 15 3 11 3
subtype3 21 16 14 2

Figure S36.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.488 (Fisher's exact test), Q value = 1

Table S42.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 25 0
subtype2 23 2
subtype3 32 1

Figure S37.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.275 (Chi-square test), Q value = 1

Table S43.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 98 2 23
subtype1 32 1 5
subtype2 22 0 10
subtype3 44 1 8

Figure S38.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.475 (Fisher's exact test), Q value = 1

Table S44.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 46 77
subtype1 15 23
subtype2 9 23
subtype3 22 31

Figure S39.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.306 (Chi-square test), Q value = 1

Table S45.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 98 10 1 9
subtype1 32 2 1 3
subtype2 20 5 0 3
subtype3 46 3 0 3

Figure S40.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S46.  Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2 3
Number of samples 53 60 10
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0634 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 118 52 0.0 - 113.0 (14.6)
subtype1 51 24 0.1 - 80.8 (17.6)
subtype2 57 21 0.1 - 113.0 (14.9)
subtype3 10 7 0.0 - 83.6 (3.4)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00284 (ANOVA), Q value = 0.17

Table S48.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 121 61.4 (13.9)
subtype1 53 57.0 (15.0)
subtype2 58 65.8 (11.1)
subtype3 10 59.3 (15.9)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.947 (Chi-square test), Q value = 1

Table S49.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 46 26 2 29 3 5 1 1 1
subtype1 18 11 1 12 1 3 1 0 1
subtype2 25 13 1 13 2 1 0 1 0
subtype3 3 2 0 4 0 1 0 0 0

Figure S43.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.968 (Chi-square test), Q value = 1

Table S50.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 49 29 38 7
subtype1 20 13 17 3
subtype2 26 14 17 3
subtype3 3 2 4 1

Figure S44.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.717 (Fisher's exact test), Q value = 1

Table S51.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 35 2
subtype2 37 1
subtype3 8 0

Figure S45.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.469 (Chi-square test), Q value = 1

Table S52.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 98 2 23
subtype1 42 2 9
subtype2 49 0 11
subtype3 7 0 3

Figure S46.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.295 (Fisher's exact test), Q value = 1

Table S53.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 46 77
subtype1 24 29
subtype2 19 41
subtype3 3 7

Figure S47.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.0339 (Chi-square test), Q value = 1

Table S54.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 98 10 1 9
subtype1 46 2 1 4
subtype2 48 5 0 5
subtype3 4 3 0 0

Figure S48.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 21 64 38
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.808 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 118 52 0.0 - 113.0 (14.6)
subtype1 20 7 0.1 - 75.7 (9.4)
subtype2 62 30 0.2 - 113.0 (14.5)
subtype3 36 15 0.0 - 93.7 (18.0)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0505 (ANOVA), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 121 61.4 (13.9)
subtype1 21 55.7 (16.9)
subtype2 62 64.0 (13.6)
subtype3 38 60.4 (11.8)

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

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.149 (Chi-square test), Q value = 1

Table S58.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 46 26 2 29 3 5 1 1 1
subtype1 4 4 1 6 1 3 0 0 0
subtype2 28 13 1 17 1 0 0 0 0
subtype3 14 9 0 6 1 2 1 1 1

Figure S51.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.454 (Chi-square test), Q value = 1

Table S59.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 49 29 38 7
subtype1 6 4 8 3
subtype2 28 14 20 2
subtype3 15 11 10 2

Figure S52.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.116 (Fisher's exact test), Q value = 1

Table S60.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 15 1
subtype2 42 0
subtype3 23 2

Figure S53.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.241 (Chi-square test), Q value = 1

Table S61.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 98 2 23
subtype1 17 0 4
subtype2 50 0 14
subtype3 31 2 5

Figure S54.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.523 (Fisher's exact test), Q value = 1

Table S62.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 46 77
subtype1 9 12
subtype2 21 43
subtype3 16 22

Figure S55.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.522 (Chi-square test), Q value = 1

Table S63.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 98 10 1 9
subtype1 15 2 0 2
subtype2 49 7 0 5
subtype3 34 1 1 2

Figure S56.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels 1 2 3
Number of samples 9 52 62
'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.12 (logrank test), Q value = 1

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

nPatients nDeath Duration Range (Median), Month
ALL 118 52 0.0 - 113.0 (14.6)
subtype1 9 6 0.0 - 83.6 (3.3)
subtype2 50 20 0.1 - 80.8 (16.2)
subtype3 59 26 0.2 - 113.0 (16.4)

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

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 8.51e-05 (ANOVA), Q value = 0.0054

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

nPatients Mean (Std.Dev)
ALL 121 61.4 (13.9)
subtype1 9 57.7 (16.0)
subtype2 52 55.9 (14.1)
subtype3 60 66.8 (11.4)

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

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.917 (Chi-square test), Q value = 1

Table S67.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IIIA STAGE IIIB STAGE IIIC STAGE IV STAGE IVA STAGE IVB
ALL 46 26 2 29 3 5 1 1 1
subtype1 3 2 0 3 0 1 0 0 0
subtype2 16 11 1 14 1 3 1 0 1
subtype3 27 13 1 12 2 1 0 1 0

Figure S59.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.919 (Chi-square test), Q value = 1

Table S68.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients T1 T2 T3 T4
ALL 49 29 38 7
subtype1 3 2 3 1
subtype2 18 13 18 3
subtype3 28 14 17 3

Figure S60.  Get High-res Image Clustering Approach #8: '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 S69.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients 0 1
ALL 80 3
subtype1 7 0
subtype2 37 2
subtype3 36 1

Figure S61.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.293 (Chi-square test), Q value = 1

Table S70.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients M0 M1 MX
ALL 98 2 23
subtype1 6 0 3
subtype2 43 2 7
subtype3 49 0 13

Figure S62.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.213 (Fisher's exact test), Q value = 1

Table S71.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients FEMALE MALE
ALL 46 77
subtype1 2 7
subtype2 24 28
subtype3 20 42

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

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.34 (Chi-square test), Q value = 1

Table S72.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients R0 R1 R2 RX
ALL 98 10 1 9
subtype1 4 2 0 0
subtype2 44 3 1 4
subtype3 50 5 0 5

Figure S64.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

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

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

  • Number of patients = 128

  • Number of clustering approaches = 8

  • Number of selected clinical features = 8

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

Clustering approaches
CNMF clustering

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

Consensus hierarchical clustering

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

Survival analysis

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

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

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] 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)
[7] 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)