This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 2 clinical features across 33 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
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5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
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2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.
Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 2 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 |
Statistical Tests | logrank test | t-test |
Copy Number Ratio CNMF subtypes |
0.938 (1.00) |
0.526 (1.00) |
METHLYATION CNMF |
0.637 (1.00) |
0.0435 (0.696) |
RNAseq CNMF subtypes |
0.419 (1.00) |
0.803 (1.00) |
RNAseq cHierClus subtypes |
0.263 (1.00) |
0.193 (1.00) |
MIRSEQ CNMF |
0.691 (1.00) |
0.832 (1.00) |
MIRSEQ CHIERARCHICAL |
0.84 (1.00) |
0.151 (1.00) |
MIRseq Mature CNMF subtypes |
0.69 (1.00) |
0.551 (1.00) |
MIRseq Mature cHierClus subtypes |
0.96 (1.00) |
0.212 (1.00) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 13 | 12 | 7 |
P value = 0.938 (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 | 32 | 19 | 3.7 - 85.3 (15.8) |
subtype1 | 13 | 9 | 6.6 - 74.3 (23.5) |
subtype2 | 12 | 7 | 4.6 - 59.7 (14.4) |
subtype3 | 7 | 3 | 3.7 - 85.3 (5.5) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.526 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 69.8 (8.5) |
subtype1 | 13 | 68.2 (8.3) |
subtype2 | 12 | 72.0 (8.4) |
subtype3 | 7 | 68.9 (9.3) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
Table S4. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 7 | 8 | 6 | 4 | 8 |
P value = 0.637 (logrank test), Q value = 1
Table S5. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 33 | 20 | 3.7 - 102.4 (16.3) |
subtype1 | 7 | 4 | 4.8 - 102.4 (27.4) |
subtype2 | 8 | 6 | 4.6 - 69.6 (18.6) |
subtype3 | 6 | 3 | 7.2 - 47.3 (20.9) |
subtype4 | 4 | 1 | 5.4 - 24.0 (10.5) |
subtype5 | 8 | 6 | 3.7 - 85.3 (15.0) |
Figure S3. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.0435 (ANOVA), Q value = 0.7
Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 33 | 69.6 (8.4) |
subtype1 | 7 | 64.9 (5.6) |
subtype2 | 8 | 65.2 (6.8) |
subtype3 | 6 | 74.8 (8.9) |
subtype4 | 4 | 69.8 (4.1) |
subtype5 | 8 | 74.2 (9.5) |
Figure S4. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
Table S7. Description of clustering approach #3: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 11 | 8 | 13 |
P value = 0.419 (logrank test), Q value = 1
Table S8. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 32 | 19 | 3.7 - 102.4 (17.2) |
subtype1 | 11 | 7 | 4.6 - 102.4 (13.9) |
subtype2 | 8 | 2 | 5.4 - 47.3 (20.2) |
subtype3 | 13 | 10 | 3.7 - 85.3 (18.1) |
Figure S5. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.803 (ANOVA), Q value = 1
Table S9. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 69.9 (8.4) |
subtype1 | 11 | 69.2 (8.2) |
subtype2 | 8 | 71.6 (5.4) |
subtype3 | 13 | 69.4 (10.3) |
Figure S6. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
Table S10. Description of clustering approach #4: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 2 | 3 | 10 | 8 | 9 |
P value = 0.263 (logrank test), Q value = 1
Table S11. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 30 | 18 | 3.7 - 102.4 (17.2) |
subtype2 | 3 | 1 | 24.0 - 59.7 (50.2) |
subtype3 | 10 | 7 | 4.6 - 102.4 (18.2) |
subtype4 | 8 | 7 | 3.7 - 85.3 (16.4) |
subtype5 | 9 | 3 | 5.4 - 47.3 (15.2) |
Figure S7. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.193 (ANOVA), Q value = 1
Table S12. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 69.7 (8.6) |
subtype2 | 3 | 62.3 (8.5) |
subtype3 | 10 | 67.3 (5.7) |
subtype4 | 8 | 72.1 (11.1) |
subtype5 | 9 | 72.8 (7.9) |
Figure S8. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
Table S13. Description of clustering approach #5: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 9 | 14 | 2 | 8 |
P value = 0.691 (logrank test), Q value = 1
Table S14. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 19 | 3.7 - 102.4 (15.2) |
subtype1 | 9 | 6 | 4.6 - 102.4 (7.2) |
subtype2 | 14 | 9 | 4.9 - 85.3 (23.7) |
subtype4 | 8 | 4 | 3.7 - 31.2 (14.6) |
Figure S9. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.832 (ANOVA), Q value = 1
Table S15. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 31 | 69.8 (8.6) |
subtype1 | 9 | 71.0 (8.9) |
subtype2 | 14 | 68.8 (9.6) |
subtype4 | 8 | 70.2 (7.3) |
Figure S10. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
Table S16. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 1 | 15 | 17 |
P value = 0.84 (logrank test), Q value = 1
Table S17. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 32 | 19 | 4.6 - 102.4 (17.2) |
subtype2 | 15 | 8 | 4.6 - 102.4 (14.7) |
subtype3 | 17 | 11 | 4.9 - 85.3 (24.0) |
Figure S11. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.151 (t-test), Q value = 1
Table S18. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 69.9 (8.4) |
subtype2 | 15 | 72.1 (7.4) |
subtype3 | 17 | 67.9 (8.9) |
Figure S12. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
Table S19. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 10 | 15 | 2 | 6 |
P value = 0.69 (logrank test), Q value = 1
Table S20. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 19 | 3.7 - 102.4 (16.3) |
subtype1 | 10 | 7 | 3.7 - 102.4 (10.9) |
subtype2 | 15 | 10 | 4.9 - 85.3 (24.0) |
subtype4 | 6 | 2 | 5.4 - 31.2 (14.8) |
Figure S13. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.551 (ANOVA), Q value = 1
Table S21. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 31 | 69.5 (8.6) |
subtype1 | 10 | 68.8 (9.0) |
subtype2 | 15 | 68.5 (9.3) |
subtype4 | 6 | 73.0 (6.2) |
Figure S14. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
Table S22. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 1 | 16 | 16 |
P value = 0.96 (logrank test), Q value = 1
Table S23. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 32 | 19 | 4.6 - 102.4 (17.2) |
subtype2 | 16 | 11 | 4.9 - 85.3 (25.3) |
subtype3 | 16 | 8 | 4.6 - 102.4 (14.6) |
Figure S15. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.212 (t-test), Q value = 1
Table S24. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 32 | 69.9 (8.4) |
subtype2 | 16 | 68.0 (9.2) |
subtype3 | 16 | 71.8 (7.3) |
Figure S16. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
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Cluster data file = UCS-TP.mergedcluster.txt
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Clinical data file = UCS-TP.merged_data.txt
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Number of patients = 33
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Number of clustering approaches = 8
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Number of selected clinical features = 2
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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
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.
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.