This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 7 different clustering approaches and 7 clinical features across 95 patients, 11 significant findings detected with P value < 0.05.
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CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE', 'PATHOLOGY.T', and 'PATHOLOGICSPREAD(M)'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'PATHOLOGY.T'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death' and 'PATHOLOGICSPREAD(M)'.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGY.T', and 'PATHOLOGICSPREAD(M)'.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'PATHOLOGY.T'.
Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 7 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 11 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
KARNOFSKY PERFORMANCE SCORE |
PATHOLOGY T |
PATHOLOGY N |
PATHOLOGICSPREAD(M) |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test |
mRNA CNMF subtypes | 0.666 | 0.182 | 0.585 | 0.0623 | 1 | ||
mRNA cHierClus subtypes | 0.0699 | 0.948 | 1 | 0.216 | 1 | ||
METHLYATION CNMF | 0.0814 | 0.00131 | 0.379 | 0.241 | 4.2e-07 | 0.222 | 0.0368 |
RNAseq CNMF subtypes | 0.0626 | 0.434 | 0.0644 | 0.00023 | 0.934 | 0.108 | |
RNAseq cHierClus subtypes | 0.0122 | 0.246 | 0.0625 | 0.481 | 0.37 | 0.0693 | 0.0402 |
MIRseq CNMF subtypes | 0.00206 | 0.257 | 0.918 | 0.0389 | 0.0601 | 0.0161 | |
MIRseq cHierClus subtypes | 0.0135 | 0.789 | 0.597 | 0.0252 | 0.119 | 0.143 |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 7 | 9 |
P value = 0.666 (logrank test)
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 2 | 0.5 - 58.5 (7.8) |
subtype1 | 7 | 1 | 0.5 - 53.8 (5.9) |
subtype2 | 9 | 1 | 1.1 - 58.5 (10.8) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.182 (t-test)
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 16 | 57.9 (11.5) |
subtype1 | 7 | 53.6 (10.3) |
subtype2 | 9 | 61.3 (11.7) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.585 (Fisher's exact test)
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 12 | 4 |
subtype1 | 6 | 1 |
subtype2 | 6 | 3 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 0.0623 (Chi-square test)
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 7 | 7 | 2 |
subtype1 | 1 | 4 | 2 |
subtype2 | 6 | 3 | 0 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D1V5.png)
P value = 1 (Fisher's exact test)
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 9 | 1 |
subtype1 | 4 | 1 |
subtype2 | 5 | 0 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D1V7.png)
Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 4 | 7 | 5 |
P value = 0.0699 (logrank test)
Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 2 | 0.5 - 58.5 (7.8) |
subtype1 | 4 | 1 | 10.8 - 58.5 (40.4) |
subtype2 | 7 | 1 | 0.5 - 25.1 (4.4) |
subtype3 | 5 | 0 | 0.7 - 53.8 (4.1) |
Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.948 (ANOVA)
Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 16 | 57.9 (11.5) |
subtype1 | 4 | 57.0 (5.0) |
subtype2 | 7 | 57.4 (13.0) |
subtype3 | 5 | 59.4 (14.8) |
Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 1 (Fisher's exact test)
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 12 | 4 |
subtype1 | 3 | 1 |
subtype2 | 5 | 2 |
subtype3 | 4 | 1 |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.216 (Chi-square test)
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 7 | 7 | 2 |
subtype1 | 3 | 1 | 0 |
subtype2 | 1 | 4 | 2 |
subtype3 | 3 | 2 | 0 |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D2V5.png)
P value = 1 (Fisher's exact test)
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 9 | 1 |
subtype1 | 2 | 0 |
subtype2 | 4 | 1 |
subtype3 | 3 | 0 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D2V7.png)
Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 36 | 19 | 24 |
P value = 0.0814 (logrank test)
Table S14. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 72 | 12 | 0.0 - 182.7 (20.9) |
subtype1 | 32 | 2 | 0.0 - 129.9 (15.1) |
subtype2 | 17 | 4 | 0.7 - 80.8 (26.3) |
subtype3 | 23 | 6 | 0.9 - 182.7 (20.1) |
Figure S11. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.00131 (ANOVA)
Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 76 | 60.5 (12.0) |
subtype1 | 35 | 55.9 (9.4) |
subtype2 | 18 | 67.9 (8.7) |
subtype3 | 23 | 61.8 (14.5) |
Figure S12. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.379 (Fisher's exact test)
Table S16. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 55 | 24 |
subtype1 | 28 | 8 |
subtype2 | 12 | 7 |
subtype3 | 15 | 9 |
Figure S13. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.241 (ANOVA)
Table S17. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 92.1 (13.6) |
subtype1 | 14 | 95.7 (5.1) |
subtype2 | 4 | 77.5 (25.0) |
subtype3 | 1 | 100.0 (NA) |
Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 4.2e-07 (Chi-square test)
Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 47 | 3 | 29 |
subtype1 | 30 | 2 | 4 |
subtype2 | 14 | 0 | 5 |
subtype3 | 3 | 1 | 20 |
Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D3V5.png)
P value = 0.222 (Chi-square test)
Table S19. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 19 | 8 | 4 |
subtype1 | 7 | 0 | 0 |
subtype2 | 3 | 2 | 1 |
subtype3 | 9 | 6 | 3 |
Figure S16. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D3V6.png)
P value = 0.0368 (Chi-square test)
Table S20. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 45 | 4 | 27 |
subtype1 | 16 | 0 | 17 |
subtype2 | 12 | 1 | 6 |
subtype3 | 17 | 3 | 4 |
Figure S17. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D3V7.png)
Table S21. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 22 | 18 | 12 | 11 |
P value = 0.0626 (logrank test)
Table S22. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 63 | 13 | 0.5 - 123.6 (15.5) |
subtype1 | 22 | 7 | 0.9 - 93.3 (16.2) |
subtype2 | 18 | 2 | 0.5 - 63.7 (12.6) |
subtype3 | 12 | 2 | 7.0 - 123.6 (29.1) |
subtype4 | 11 | 2 | 3.8 - 80.8 (15.5) |
Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.434 (ANOVA)
Table S23. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 63 | 60.3 (12.5) |
subtype1 | 22 | 59.2 (14.2) |
subtype2 | 18 | 58.7 (11.7) |
subtype3 | 12 | 65.8 (11.2) |
subtype4 | 11 | 59.3 (11.6) |
Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0644 (Fisher's exact test)
Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 43 | 20 |
subtype1 | 11 | 11 |
subtype2 | 12 | 6 |
subtype3 | 11 | 1 |
subtype4 | 9 | 2 |
Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.00023 (Chi-square test)
Table S25. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 32 | 8 | 23 |
subtype1 | 6 | 1 | 15 |
subtype2 | 10 | 5 | 3 |
subtype3 | 5 | 2 | 5 |
subtype4 | 11 | 0 | 0 |
Figure S21. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D4V5.png)
P value = 0.934 (Chi-square test)
Table S26. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 14 | 9 | 3 |
subtype1 | 7 | 6 | 2 |
subtype2 | 2 | 1 | 0 |
subtype3 | 4 | 2 | 1 |
subtype4 | 1 | 0 | 0 |
Figure S22. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D4V6.png)
P value = 0.108 (Chi-square test)
Table S27. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 42 | 5 | 9 |
subtype1 | 14 | 5 | 3 |
subtype2 | 10 | 0 | 2 |
subtype3 | 11 | 0 | 1 |
subtype4 | 7 | 0 | 3 |
Figure S23. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D4V7.png)
Table S28. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 16 | 25 | 22 |
P value = 0.0122 (logrank test)
Table S29. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 63 | 13 | 0.5 - 123.6 (15.5) |
subtype1 | 16 | 6 | 2.8 - 80.8 (10.9) |
subtype2 | 25 | 3 | 0.5 - 123.6 (13.6) |
subtype3 | 22 | 4 | 6.4 - 93.3 (25.2) |
Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.246 (ANOVA)
Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 63 | 60.3 (12.5) |
subtype1 | 16 | 59.1 (13.4) |
subtype2 | 25 | 58.0 (11.3) |
subtype3 | 22 | 63.9 (13.1) |
Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0625 (Fisher's exact test)
Table S31. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 43 | 20 |
subtype1 | 7 | 9 |
subtype2 | 19 | 6 |
subtype3 | 17 | 5 |
Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.481 (ANOVA)
Table S32. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 8 | 83.8 (34.2) |
subtype1 | 3 | 93.3 (5.8) |
subtype2 | 5 | 78.0 (43.8) |
Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V4.png)
P value = 0.37 (Chi-square test)
Table S33. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 32 | 8 | 23 |
subtype1 | 7 | 2 | 7 |
subtype2 | 14 | 5 | 6 |
subtype3 | 11 | 1 | 10 |
Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D5V5.png)
P value = 0.0693 (Chi-square test)
Table S34. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 14 | 9 | 3 |
subtype1 | 1 | 5 | 2 |
subtype2 | 4 | 2 | 0 |
subtype3 | 9 | 2 | 1 |
Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D5V6.png)
P value = 0.0402 (Chi-square test)
Table S35. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 42 | 5 | 9 |
subtype1 | 8 | 4 | 3 |
subtype2 | 15 | 1 | 4 |
subtype3 | 19 | 0 | 2 |
Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D5V7.png)
Table S36. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 14 | 15 | 14 |
P value = 0.00206 (logrank test)
Table S37. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 43 | 8 | 0.5 - 86.7 (11.6) |
subtype1 | 14 | 2 | 0.9 - 86.7 (25.6) |
subtype2 | 15 | 1 | 0.5 - 58.5 (5.8) |
subtype3 | 14 | 5 | 2.8 - 40.8 (11.2) |
Figure S31. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.257 (ANOVA)
Table S38. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 43 | 60.0 (12.3) |
subtype1 | 14 | 64.3 (10.0) |
subtype2 | 15 | 59.0 (10.5) |
subtype3 | 14 | 56.8 (15.4) |
Figure S32. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.918 (Fisher's exact test)
Table S39. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 30 | 13 |
subtype1 | 10 | 4 |
subtype2 | 11 | 4 |
subtype3 | 9 | 5 |
Figure S33. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.0389 (Chi-square test)
Table S40. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 19 | 7 | 17 |
subtype1 | 4 | 1 | 9 |
subtype2 | 8 | 5 | 2 |
subtype3 | 7 | 1 | 6 |
Figure S34. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D6V5.png)
P value = 0.0601 (Fisher's exact test)
Table S41. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 |
---|---|---|
ALL | 7 | 9 |
subtype1 | 6 | 3 |
subtype2 | 0 | 0 |
subtype3 | 1 | 6 |
Figure S35. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D6V6.png)
P value = 0.0161 (Fisher's exact test)
Table S42. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 30 | 5 |
subtype1 | 13 | 0 |
subtype2 | 8 | 0 |
subtype3 | 9 | 5 |
Figure S36. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D6V7.png)
Table S43. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 13 | 15 | 15 |
P value = 0.0135 (logrank test)
Table S44. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 43 | 8 | 0.5 - 86.7 (11.6) |
subtype1 | 13 | 4 | 2.8 - 40.8 (11.6) |
subtype2 | 15 | 3 | 0.9 - 86.7 (21.6) |
subtype3 | 15 | 1 | 0.5 - 63.7 (5.9) |
Figure S37. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.789 (ANOVA)
Table S45. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 43 | 60.0 (12.3) |
subtype1 | 13 | 58.0 (15.3) |
subtype2 | 15 | 60.9 (10.7) |
subtype3 | 15 | 60.8 (11.5) |
Figure S38. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.597 (Fisher's exact test)
Table S46. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 30 | 13 |
subtype1 | 8 | 5 |
subtype2 | 12 | 3 |
subtype3 | 10 | 5 |
Figure S39. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.0252 (Chi-square test)
Table S47. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 19 | 7 | 17 |
subtype1 | 7 | 1 | 5 |
subtype2 | 4 | 1 | 10 |
subtype3 | 8 | 5 | 2 |
Figure S40. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D7V5.png)
P value = 0.119 (Fisher's exact test)
Table S48. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 |
---|---|---|
ALL | 7 | 9 |
subtype1 | 1 | 5 |
subtype2 | 6 | 3 |
subtype3 | 0 | 1 |
Figure S41. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D7V6.png)
P value = 0.143 (Fisher's exact test)
Table S49. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 30 | 5 |
subtype1 | 9 | 4 |
subtype2 | 13 | 1 |
subtype3 | 8 | 0 |
Figure S42. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D7V7.png)
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Cluster data file = KIRP.mergedcluster.txt
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Clinical data file = KIRP.clin.merged.picked.txt
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Number of patients = 95
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Number of clustering approaches = 7
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Number of selected clinical features = 7
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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, 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 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
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
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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.