Correlation between aggregated molecular cancer subtypes and selected clinical features
Kidney Chromophobe (Primary solid tumor)
23 May 2013  |  analyses__2013_05_23
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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1DB7ZVP
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 7 clinical features across 25 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.

  • 2 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 4 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

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

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

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

  • 4 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 8 different clustering approaches and 7 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
AGE GENDER DISTANT
METASTASIS
LYMPH
NODE
METASTASIS
TUMOR
STAGECODE
NEOPLASM
DISEASESTAGE
Statistical Tests logrank test ANOVA Fisher's exact test Chi-square test Chi-square test ANOVA Chi-square test
Copy Number Ratio CNMF subtypes 100
(1.00)
0.164
(1.00)
1
(1.00)
0.085
(1.00)
0.487
(1.00)
METHLYATION CNMF 100
(1.00)
0.463
(1.00)
0.846
(1.00)
0.128
(1.00)
0.34
(1.00)
RNAseq CNMF subtypes 100
(1.00)
0.76
(1.00)
0.841
(1.00)
0.177
(1.00)
0.39
(1.00)
RNAseq cHierClus subtypes 100
(1.00)
0.395
(1.00)
0.653
(1.00)
0.327
(1.00)
0.559
(1.00)
MIRSEQ CNMF 100
(1.00)
0.127
(1.00)
0.597
(1.00)
0.225
(1.00)
0.16
(1.00)
MIRSEQ CHIERARCHICAL 100
(1.00)
0.715
(1.00)
0.604
(1.00)
0.286
(1.00)
0.56
(1.00)
MIRseq Mature CNMF subtypes 100
(1.00)
0.785
(1.00)
0.236
(1.00)
0.313
(1.00)
0.127
(1.00)
MIRseq Mature cHierClus subtypes 100
(1.00)
0.494
(1.00)
0.588
(1.00)
0.492
(1.00)
0.265
(1.00)
Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

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

Cluster Labels 1 2 3
Number of samples 1 19 5
'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 100 (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 23 2 0.6 - 142.5 (51.0)
subtype2 18 2 0.6 - 142.5 (51.6)
subtype3 5 0 2.5 - 89.7 (20.9)

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 = 0.164 (t-test), Q value = 1

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

nPatients Mean (Std.Dev)
ALL 24 51.8 (14.6)
subtype2 19 49.3 (13.8)
subtype3 5 61.2 (15.2)

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 'GENDER'

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

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

nPatients FEMALE MALE
ALL 10 14
subtype2 8 11
subtype3 2 3

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.085 (Chi-square test), Q value = 1

Table S5.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 9
subtype2 12 2 5
subtype3 1 0 4

Figure S4.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.487 (Chi-square test), Q value = 1

Table S6.  Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 11 2 3
subtype2 7 8 1 3
subtype3 1 3 1 0

Figure S5.  Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S7.  Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels 1 2 3
Number of samples 4 16 5
'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S8.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 4 0 23.5 - 80.4 (54.8)
subtype2 15 2 2.5 - 142.5 (52.3)
subtype3 5 0 0.6 - 75.7 (11.2)

Figure S6.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.463 (ANOVA), Q value = 1

Table S9.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 4 57.5 (11.8)
subtype2 16 53.1 (16.1)
subtype3 5 45.6 (10.4)

Figure S7.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.846 (Fisher's exact test), Q value = 1

Table S10.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 1 3
subtype2 8 8
subtype3 2 3

Figure S8.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.128 (Chi-square test), Q value = 1

Table S11.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 3 0 1
subtype2 10 1 5
subtype3 0 1 4

Figure S9.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

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

Table S12.  Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 1 3 0 0
subtype2 4 9 1 2
subtype3 3 0 1 1

Figure S10.  Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S13.  Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels 1 2 3 4
Number of samples 3 11 7 4
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S14.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 3 0 37.0 - 89.7 (65.2)
subtype2 10 0 2.5 - 142.5 (24.5)
subtype3 7 2 20.9 - 114.2 (67.8)
subtype4 4 0 0.6 - 75.7 (29.6)

Figure S11.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.76 (ANOVA), Q value = 1

Table S15.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 3 55.3 (11.9)
subtype2 11 52.2 (17.5)
subtype3 7 55.1 (13.1)
subtype4 4 45.5 (12.0)

Figure S12.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.841 (Fisher's exact test), Q value = 1

Table S16.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 2 1
subtype2 5 6
subtype3 3 4
subtype4 1 3

Figure S13.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.177 (Chi-square test), Q value = 1

Table S17.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 1 0 2
subtype2 7 0 4
subtype3 5 1 1
subtype4 0 1 3

Figure S14.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.39 (Chi-square test), Q value = 1

Table S18.  Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 1 2 0 0
subtype2 4 5 1 1
subtype3 0 5 1 1
subtype4 3 0 0 1

Figure S15.  Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S19.  Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

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

P value = 100 (logrank test), Q value = 1

Table S20.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 5 2 28.1 - 114.2 (67.8)
subtype2 5 0 0.6 - 75.7 (23.5)
subtype3 8 0 3.5 - 142.5 (25.7)
subtype4 3 0 2.5 - 87.9 (20.9)
subtype5 3 0 37.0 - 89.7 (65.2)

Figure S16.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.395 (ANOVA), Q value = 1

Table S21.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 5 55.4 (15.1)
subtype2 5 45.0 (10.5)
subtype3 9 49.4 (14.9)
subtype4 3 65.0 (19.7)
subtype5 3 55.3 (11.9)

Figure S17.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.653 (Chi-square test), Q value = 1

Table S22.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 2 3
subtype2 1 4
subtype3 4 5
subtype4 2 1
subtype5 2 1

Figure S18.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.327 (Chi-square test), Q value = 1

Table S23.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 4 1 0
subtype2 1 1 3
subtype3 6 0 3
subtype4 1 0 2
subtype5 1 0 2

Figure S19.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.559 (Chi-square test), Q value = 1

Table S24.  Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 0 3 1 1
subtype2 3 1 0 1
subtype3 4 3 1 1
subtype4 0 3 0 0
subtype5 1 2 0 0

Figure S20.  Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S25.  Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

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

P value = 100 (logrank test), Q value = 1

Table S26.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 8 2 0.6 - 142.5 (39.5)
subtype2 8 0 3.5 - 102.8 (51.1)
subtype3 8 0 11.2 - 114.2 (46.9)

Figure S21.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.127 (ANOVA), Q value = 1

Table S27.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 9 58.4 (17.9)
subtype2 8 53.5 (9.1)
subtype3 8 44.2 (12.5)

Figure S22.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.597 (Fisher's exact test), Q value = 1

Table S28.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 3 6
subtype2 3 5
subtype3 5 3

Figure S23.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.225 (Chi-square test), Q value = 1

Table S29.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 3 2 4
subtype2 4 0 4
subtype3 6 0 2

Figure S24.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.16 (Chi-square test), Q value = 1

Table S30.  Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 4 3 0 2
subtype2 1 6 0 1
subtype3 3 3 2 0

Figure S25.  Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S31.  Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels 1 2
Number of samples 4 21
'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S32.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 4 1 0.6 - 75.7 (30.2)
subtype2 20 1 2.5 - 142.5 (44.0)

Figure S26.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.715 (t-test), Q value = 1

Table S33.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 4 49.2 (17.5)
subtype2 21 52.9 (14.4)

Figure S27.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.604 (Fisher's exact test), Q value = 1

Table S34.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 1 3
subtype2 10 11

Figure S28.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.286 (Chi-square test), Q value = 1

Table S35.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 1 1 2
subtype2 12 1 8

Figure S29.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.56 (Chi-square test), Q value = 1

Table S36.  Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 2 1 0 1
subtype2 6 11 2 2

Figure S30.  Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S37.  Get Full Table Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 10 7 8
'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

Table S38.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 24 2 0.6 - 142.5 (44.0)
subtype1 10 0 2.5 - 102.8 (25.7)
subtype2 7 0 11.2 - 114.2 (67.8)
subtype3 7 2 0.6 - 142.5 (51.0)

Figure S31.  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 = 0.785 (ANOVA), Q value = 1

Table S39.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 25 52.3 (14.6)
subtype1 10 52.1 (15.7)
subtype2 7 49.6 (13.3)
subtype3 8 55.0 (15.6)

Figure S32.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.236 (Fisher's exact test), Q value = 1

Table S40.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 11 14
subtype1 4 6
subtype2 5 2
subtype3 2 6

Figure S33.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.313 (Chi-square test), Q value = 1

Table S41.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 13 2 10
subtype1 6 0 4
subtype2 4 0 3
subtype3 3 2 3

Figure S34.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.127 (Chi-square test), Q value = 1

Table S42.  Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 8 12 2 3
subtype1 3 6 0 1
subtype2 1 4 2 0
subtype3 4 2 0 2

Figure S35.  Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S43.  Get Full Table Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

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

P value = 100 (logrank test), Q value = 1

Table S44.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

nPatients nDeath Duration Range (Median), Month
ALL 21 2 0.6 - 142.5 (51.0)
subtype2 6 1 23.4 - 142.5 (74.1)
subtype4 4 1 0.6 - 75.7 (30.2)
subtype5 7 0 2.5 - 102.8 (25.9)
subtype6 4 0 23.5 - 72.7 (38.2)

Figure S36.  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 = 0.494 (ANOVA), Q value = 1

Table S45.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

nPatients Mean (Std.Dev)
ALL 21 51.4 (14.6)
subtype2 6 49.2 (15.0)
subtype4 4 49.2 (17.5)
subtype5 7 58.3 (14.1)
subtype6 4 45.0 (12.6)

Figure S37.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.588 (Fisher's exact test), Q value = 1

Table S46.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients FEMALE MALE
ALL 8 13
subtype2 4 2
subtype4 1 3
subtype5 2 5
subtype6 1 3

Figure S38.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S47.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

nPatients N0 N1 NX
ALL 11 2 8
subtype2 4 1 1
subtype4 1 1 2
subtype5 3 0 4
subtype6 3 0 1

Figure S39.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'LYMPH.NODE.METASTASIS'

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

P value = 0.265 (Chi-square test), Q value = 1

Table S48.  Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

nPatients STAGE I STAGE II STAGE III STAGE IV
ALL 6 11 1 3
subtype2 0 4 1 1
subtype4 2 1 0 1
subtype5 1 5 0 1
subtype6 3 1 0 0

Figure S40.  Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'NEOPLASM.DISEASESTAGE'

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

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

  • Number of patients = 25

  • Number of clustering approaches = 8

  • Number of selected clinical features = 7

  • 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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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

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

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

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[5] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[7] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[8] 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)