This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 4 different clustering approaches and 3 clinical features across 62 patients, one significant finding detected with P value < 0.05.
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3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.
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3 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 miR expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'AGE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 3 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.
Clinical Features |
Time to Death |
AGE | GENDER |
Statistical Tests | logrank test | ANOVA | Fisher's exact test |
CN CNMF | 0.751 | 0.871 | 0.495 |
METHLYATION CNMF | 0.199 | 0.154 | 0.469 |
MIRseq CNMF subtypes | 0.943 | 0.242 | 0.158 |
MIRseq cHierClus subtypes | 0.706 | 0.0122 | 0.262 |
Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 15 | 22 | 24 |
P value = 0.751 (logrank test)
Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 55 | 26 | 0.1 - 90.7 (12.8) |
subtype1 | 14 | 7 | 0.1 - 90.7 (7.8) |
subtype2 | 19 | 8 | 0.4 - 69.6 (14.3) |
subtype3 | 22 | 11 | 0.3 - 83.6 (11.3) |
Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.871 (ANOVA)
Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 61.8 (14.0) |
subtype1 | 14 | 60.4 (16.0) |
subtype2 | 19 | 61.5 (15.0) |
subtype3 | 23 | 62.9 (12.3) |
Figure S2. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.495 (Fisher's exact test)
Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 22 | 39 |
subtype1 | 4 | 11 |
subtype2 | 10 | 12 |
subtype3 | 8 | 16 |
Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
Table S5. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 19 | 15 | 27 |
P value = 0.199 (logrank test)
Table S6. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 54 | 25 | 0.1 - 90.7 (12.2) |
subtype1 | 15 | 8 | 0.4 - 90.7 (19.8) |
subtype2 | 13 | 7 | 0.1 - 53.3 (7.1) |
subtype3 | 26 | 10 | 0.3 - 83.6 (8.3) |
Figure S4. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.154 (ANOVA)
Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 60.8 (14.7) |
subtype1 | 15 | 57.7 (18.8) |
subtype2 | 15 | 56.9 (15.8) |
subtype3 | 26 | 64.9 (10.2) |
Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.469 (Fisher's exact test)
Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 23 | 38 |
subtype1 | 9 | 10 |
subtype2 | 6 | 9 |
subtype3 | 8 | 19 |
Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
Table S9. Get Full Table Description of clustering approach #3: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 22 | 12 | 27 |
P value = 0.943 (logrank test)
Table S10. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 54 | 25 | 0.1 - 83.6 (12.2) |
subtype1 | 19 | 11 | 0.1 - 69.6 (14.4) |
subtype2 | 9 | 4 | 1.1 - 83.6 (5.9) |
subtype3 | 26 | 10 | 0.3 - 53.3 (11.0) |
Figure S7. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.242 (ANOVA)
Table S11. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 61.0 (14.8) |
subtype1 | 21 | 57.2 (14.9) |
subtype2 | 9 | 59.7 (19.4) |
subtype3 | 26 | 64.5 (12.7) |
Figure S8. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.158 (Fisher's exact test)
Table S12. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 23 | 38 |
subtype1 | 11 | 11 |
subtype2 | 2 | 10 |
subtype3 | 10 | 17 |
Figure S9. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
Table S13. Get Full Table Description of clustering approach #4: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 8 | 9 | 20 | 24 |
P value = 0.706 (logrank test)
Table S14. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 54 | 25 | 0.1 - 83.6 (12.2) |
subtype1 | 8 | 2 | 0.3 - 53.3 (2.3) |
subtype2 | 7 | 4 | 1.1 - 83.6 (11.6) |
subtype3 | 17 | 11 | 0.1 - 69.6 (14.9) |
subtype4 | 22 | 8 | 2.6 - 51.2 (14.0) |
Figure S10. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.0122 (ANOVA)
Table S15. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 56 | 61.0 (14.8) |
subtype1 | 8 | 64.2 (6.5) |
subtype2 | 7 | 60.7 (18.5) |
subtype3 | 19 | 52.6 (16.8) |
subtype4 | 22 | 67.2 (10.7) |
Figure S11. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.262 (Fisher's exact test)
Table S16. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 23 | 38 |
subtype1 | 1 | 7 |
subtype2 | 2 | 7 |
subtype3 | 9 | 11 |
subtype4 | 11 | 13 |
Figure S12. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
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Cluster data file = LIHC-TP.mergedcluster.txt
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Clinical data file = LIHC-TP.clin.merged.picked.txt
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Number of patients = 62
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Number of clustering approaches = 4
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Number of selected clinical features = 3
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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 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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.