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 8 clinical features across 104 patients, 9 significant findings detected with P value < 0.05 and Q value < 0.25.
-
CNMF clustering analysis on array-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
-
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
-
5 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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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'PATHOLOGY.T' and 'TUMOR.STAGE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'GENDER', 'PATHOLOGY.T', and 'TUMOR.STAGE'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'GENDER'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGY.T'.
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, 9 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
KARNOFSKY PERFORMANCE SCORE |
PATHOLOGY T |
PATHOLOGY N |
PATHOLOGICSPREAD(M) |
TUMOR STAGE |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Chi-square test | Chi-square test | Chi-square test |
mRNA CNMF subtypes |
100 (1.00) |
0.182 (1.00) |
0.585 (1.00) |
0.0623 (1.00) |
1 (1.00) |
0.292 (1.00) |
||
mRNA cHierClus subtypes |
100 (1.00) |
0.948 (1.00) |
1 (1.00) |
0.216 (1.00) |
1 (1.00) |
|||
Copy Number Ratio CNMF subtypes |
0.0309 (1.00) |
0.399 (1.00) |
0.0797 (1.00) |
0.787 (1.00) |
0.0702 (1.00) |
0.848 (1.00) |
0.632 (1.00) |
0.231 (1.00) |
METHLYATION CNMF |
0.147 (1.00) |
0.0601 (1.00) |
0.219 (1.00) |
0.192 (1.00) |
1.85e-08 (1.09e-06) |
0.115 (1.00) |
0.099 (1.00) |
2.46e-06 (0.000143) |
RNAseq CNMF subtypes |
0.0331 (1.00) |
0.00566 (0.277) |
0.0139 (0.614) |
0.433 (1.00) |
6.14e-05 (0.00344) |
0.55 (1.00) |
0.0127 (0.569) |
7.89e-05 (0.00434) |
RNAseq cHierClus subtypes |
0.0119 (0.545) |
0.495 (1.00) |
0.000483 (0.0256) |
0.578 (1.00) |
0.000174 (0.00939) |
0.072 (1.00) |
0.00815 (0.391) |
1.4e-05 (8e-04) |
MIRSEQ CNMF |
0.987 (1.00) |
0.111 (1.00) |
0.00489 (0.249) |
0.488 (1.00) |
0.00975 (0.458) |
0.133 (1.00) |
0.284 (1.00) |
0.018 (0.774) |
MIRSEQ CHIERARCHICAL |
0.354 (1.00) |
0.541 (1.00) |
0.0627 (1.00) |
0.395 (1.00) |
0.000669 (0.0348) |
0.29 (1.00) |
0.408 (1.00) |
0.00519 (0.259) |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 7 | 9 |
P value = 100 (logrank test), Q value = 1
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), Q value = 1
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), Q value = 1
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 4 | 12 |
subtype1 | 1 | 6 |
subtype2 | 3 | 6 |
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), Q value = 1
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 = 0.292 (Chi-square test), Q value = 1
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 5 | 2 | 1 | 1 |
subtype1 | 1 | 1 | 1 | 1 |
subtype2 | 4 | 1 | 0 | 0 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'
![](D1V8.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 = 100 (logrank test), Q value = 1
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), Q value = 1
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), Q value = 1
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 4 | 12 |
subtype1 | 1 | 3 |
subtype2 | 2 | 5 |
subtype3 | 1 | 4 |
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), Q value = 1
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 = NA (Chi-square test), Q value = 1
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 5 | 2 | 1 | 1 |
subtype1 | 2 | 0 | 0 | 0 |
subtype2 | 1 | 2 | 1 | 1 |
subtype3 | 2 | 0 | 0 | 0 |
Table S13. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 20 | 38 | 9 | 20 | 17 |
P value = 0.0309 (logrank test), Q value = 1
Table S14. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 97 | 14 | 0.0 - 182.7 (13.7) |
subtype1 | 20 | 4 | 0.0 - 80.8 (14.1) |
subtype2 | 34 | 3 | 0.2 - 129.9 (21.0) |
subtype3 | 9 | 1 | 0.0 - 50.5 (15.5) |
subtype4 | 19 | 4 | 0.5 - 25.4 (11.1) |
subtype5 | 15 | 2 | 0.6 - 182.7 (32.1) |
Figure S10. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.399 (ANOVA), Q value = 1
Table S15. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 101 | 59.6 (12.4) |
subtype1 | 20 | 58.5 (16.0) |
subtype2 | 38 | 60.7 (12.9) |
subtype3 | 9 | 52.8 (7.7) |
subtype4 | 19 | 59.7 (10.9) |
subtype5 | 15 | 62.7 (8.9) |
Figure S11. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.0797 (Chi-square test), Q value = 1
Table S16. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 70 |
subtype1 | 8 | 12 |
subtype2 | 17 | 21 |
subtype3 | 1 | 8 |
subtype4 | 6 | 14 |
subtype5 | 2 | 15 |
Figure S12. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.787 (ANOVA), Q value = 1
Table S17. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 22 | 87.7 (23.3) |
subtype1 | 4 | 92.5 (5.0) |
subtype2 | 7 | 87.1 (21.4) |
subtype3 | 3 | 93.3 (5.8) |
subtype4 | 6 | 78.3 (38.7) |
subtype5 | 2 | 100.0 (0.0) |
Figure S13. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 0.0702 (Chi-square test), Q value = 1
Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 58 | 13 | 33 |
subtype1 | 7 | 2 | 11 |
subtype2 | 26 | 4 | 8 |
subtype3 | 6 | 2 | 1 |
subtype4 | 8 | 2 | 10 |
subtype5 | 11 | 3 | 3 |
Figure S14. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D3V5.png)
P value = 0.848 (Chi-square test), Q value = 1
Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 20 | 12 | 4 |
subtype1 | 5 | 6 | 1 |
subtype2 | 5 | 2 | 1 |
subtype3 | 1 | 0 | 0 |
subtype4 | 7 | 3 | 2 |
subtype5 | 2 | 1 | 0 |
Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D3V6.png)
P value = 0.632 (Chi-square test), Q value = 1
Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 54 | 5 | 36 |
subtype1 | 9 | 2 | 7 |
subtype2 | 21 | 1 | 14 |
subtype3 | 3 | 0 | 4 |
subtype4 | 10 | 2 | 7 |
subtype5 | 11 | 0 | 4 |
Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D3V7.png)
P value = 0.231 (Chi-square test), Q value = 1
Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 53 | 7 | 24 | 9 |
subtype1 | 7 | 1 | 7 | 3 |
subtype2 | 23 | 4 | 7 | 1 |
subtype3 | 5 | 0 | 1 | 0 |
subtype4 | 7 | 1 | 7 | 4 |
subtype5 | 11 | 1 | 2 | 1 |
Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D3V8.png)
Table S22. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 21 | 40 | 27 |
P value = 0.147 (logrank test), Q value = 1
Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 81 | 12 | 0.0 - 182.7 (14.6) |
subtype1 | 19 | 4 | 0.0 - 80.8 (26.0) |
subtype2 | 36 | 2 | 0.0 - 129.9 (14.4) |
subtype3 | 26 | 6 | 0.2 - 182.7 (13.3) |
Figure S18. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.0601 (ANOVA), Q value = 1
Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 85 | 60.0 (12.6) |
subtype1 | 20 | 65.7 (10.9) |
subtype2 | 39 | 57.6 (10.0) |
subtype3 | 26 | 59.2 (15.9) |
Figure S19. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.219 (Fisher's exact test), Q value = 1
Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 30 | 58 |
subtype1 | 8 | 13 |
subtype2 | 10 | 30 |
subtype3 | 12 | 15 |
Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.192 (ANOVA), Q value = 1
Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 91.9 (12.9) |
subtype1 | 5 | 80.0 (22.4) |
subtype2 | 14 | 95.7 (5.1) |
subtype3 | 2 | 95.0 (7.1) |
Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D4V4.png)
P value = 1.85e-08 (Chi-square test), Q value = 1.1e-06
Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 51 | 6 | 31 |
subtype1 | 15 | 0 | 6 |
subtype2 | 32 | 5 | 3 |
subtype3 | 4 | 1 | 22 |
Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D4V5.png)
P value = 0.115 (Chi-square test), Q value = 1
Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 20 | 10 | 4 |
subtype1 | 3 | 2 | 1 |
subtype2 | 8 | 0 | 0 |
subtype3 | 9 | 8 | 3 |
Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D4V6.png)
P value = 0.099 (Chi-square test), Q value = 1
Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 45 | 4 | 36 |
subtype1 | 12 | 1 | 7 |
subtype2 | 17 | 0 | 21 |
subtype3 | 16 | 3 | 8 |
Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D4V7.png)
P value = 2.46e-06 (Chi-square test), Q value = 0.00014
Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 48 | 5 | 23 | 8 |
subtype1 | 14 | 1 | 3 | 2 |
subtype2 | 30 | 3 | 4 | 0 |
subtype3 | 4 | 1 | 16 | 6 |
Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D4V8.png)
Table S31. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 26 | 24 | 25 |
P value = 0.0331 (logrank test), Q value = 1
Table S32. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 72 | 13 | 0.5 - 182.7 (15.1) |
subtype1 | 24 | 3 | 0.5 - 54.9 (13.1) |
subtype2 | 23 | 8 | 0.9 - 93.3 (13.2) |
subtype3 | 25 | 2 | 6.4 - 182.7 (26.0) |
Figure S26. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.00566 (ANOVA), Q value = 0.28
Table S33. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 72 | 59.7 (13.1) |
subtype1 | 24 | 56.5 (12.1) |
subtype2 | 23 | 55.8 (14.2) |
subtype3 | 25 | 66.3 (10.5) |
Figure S27. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0139 (Fisher's exact test), Q value = 0.61
Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 23 | 52 |
subtype1 | 5 | 21 |
subtype2 | 13 | 11 |
subtype3 | 5 | 20 |
Figure S28. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.433 (ANOVA), Q value = 1
Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 12 | 86.7 (27.7) |
subtype1 | 7 | 94.3 (5.3) |
subtype2 | 3 | 63.3 (55.1) |
subtype3 | 2 | 95.0 (7.1) |
Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V4.png)
P value = 6.14e-05 (Chi-square test), Q value = 0.0034
Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 38 | 9 | 28 |
subtype1 | 20 | 5 | 1 |
subtype2 | 5 | 2 | 17 |
subtype3 | 13 | 2 | 10 |
Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D5V5.png)
P value = 0.55 (Chi-square test), Q value = 1
Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 15 | 12 | 3 |
subtype1 | 2 | 0 | 0 |
subtype2 | 6 | 8 | 2 |
subtype3 | 7 | 4 | 1 |
Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D5V6.png)
P value = 0.0127 (Chi-square test), Q value = 0.57
Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 46 | 5 | 17 |
subtype1 | 14 | 0 | 7 |
subtype2 | 12 | 5 | 6 |
subtype3 | 20 | 0 | 4 |
Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D5V7.png)
P value = 7.89e-05 (Chi-square test), Q value = 0.0043
Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 35 | 3 | 21 | 8 |
subtype1 | 19 | 0 | 2 | 0 |
subtype2 | 4 | 1 | 10 | 7 |
subtype3 | 12 | 2 | 9 | 1 |
Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D5V8.png)
Table S40. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 18 | 36 | 21 |
P value = 0.0119 (logrank test), Q value = 0.55
Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 72 | 13 | 0.5 - 182.7 (15.1) |
subtype1 | 17 | 6 | 2.8 - 80.8 (10.8) |
subtype2 | 34 | 3 | 0.5 - 182.7 (13.8) |
subtype3 | 21 | 4 | 0.9 - 123.6 (25.1) |
Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.495 (ANOVA), Q value = 1
Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 72 | 59.7 (13.1) |
subtype1 | 17 | 57.3 (15.0) |
subtype2 | 34 | 59.3 (11.7) |
subtype3 | 21 | 62.3 (13.7) |
Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.000483 (Fisher's exact test), Q value = 0.026
Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 23 | 52 |
subtype1 | 11 | 7 |
subtype2 | 4 | 32 |
subtype3 | 8 | 13 |
Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.578 (ANOVA), Q value = 1
Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 12 | 86.7 (27.7) |
subtype1 | 3 | 93.3 (5.8) |
subtype2 | 7 | 95.7 (5.3) |
subtype3 | 2 | 45.0 (63.6) |
Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D6V4.png)
P value = 0.000174 (Chi-square test), Q value = 0.0094
Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 38 | 9 | 28 |
subtype1 | 7 | 2 | 9 |
subtype2 | 26 | 6 | 4 |
subtype3 | 5 | 1 | 15 |
Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D6V5.png)
P value = 0.072 (Chi-square test), Q value = 1
Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 15 | 12 | 3 |
subtype1 | 1 | 6 | 2 |
subtype2 | 4 | 1 | 0 |
subtype3 | 10 | 5 | 1 |
Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D6V6.png)
P value = 0.00815 (Chi-square test), Q value = 0.39
Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 46 | 5 | 17 |
subtype1 | 9 | 4 | 4 |
subtype2 | 19 | 0 | 11 |
subtype3 | 18 | 1 | 2 |
Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D6V7.png)
P value = 1.4e-05 (Chi-square test), Q value = 8e-04
Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 35 | 3 | 21 | 8 |
subtype1 | 6 | 1 | 3 | 6 |
subtype2 | 24 | 1 | 4 | 1 |
subtype3 | 5 | 1 | 14 | 1 |
Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D6V8.png)
Table S49. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 24 | 34 | 29 | 17 |
P value = 0.987 (logrank test), Q value = 1
Table S50. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 97 | 14 | 0.0 - 182.7 (13.7) |
subtype1 | 24 | 4 | 0.0 - 63.7 (18.1) |
subtype2 | 32 | 5 | 0.2 - 182.7 (17.4) |
subtype3 | 24 | 3 | 0.0 - 123.6 (13.0) |
subtype4 | 17 | 2 | 0.5 - 86.7 (13.7) |
Figure S42. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.111 (ANOVA), Q value = 1
Table S51. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 101 | 59.6 (12.4) |
subtype1 | 24 | 62.1 (12.6) |
subtype2 | 34 | 56.2 (13.7) |
subtype3 | 26 | 63.1 (10.1) |
subtype4 | 17 | 57.8 (11.2) |
Figure S43. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.00489 (Fisher's exact test), Q value = 0.25
Table S52. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 70 |
subtype1 | 3 | 21 |
subtype2 | 16 | 18 |
subtype3 | 6 | 23 |
subtype4 | 9 | 8 |
Figure S44. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.488 (ANOVA), Q value = 1
Table S53. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 22 | 87.7 (23.3) |
subtype1 | 5 | 94.0 (5.5) |
subtype2 | 11 | 80.9 (31.8) |
subtype3 | 4 | 95.0 (5.8) |
subtype4 | 2 | 95.0 (7.1) |
Figure S45. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V4.png)
P value = 0.00975 (Chi-square test), Q value = 0.46
Table S54. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 58 | 13 | 33 |
subtype1 | 13 | 2 | 9 |
subtype2 | 19 | 2 | 13 |
subtype3 | 22 | 4 | 3 |
subtype4 | 4 | 5 | 8 |
Figure S46. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D7V5.png)
P value = 0.133 (Chi-square test), Q value = 1
Table S55. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 20 | 12 | 4 |
subtype1 | 7 | 2 | 1 |
subtype2 | 5 | 6 | 3 |
subtype3 | 5 | 0 | 0 |
subtype4 | 3 | 4 | 0 |
Figure S47. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D7V6.png)
P value = 0.284 (Chi-square test), Q value = 1
Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 54 | 5 | 36 |
subtype1 | 14 | 0 | 8 |
subtype2 | 17 | 3 | 14 |
subtype3 | 14 | 0 | 11 |
subtype4 | 9 | 2 | 3 |
Figure S48. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D7V7.png)
P value = 0.018 (Chi-square test), Q value = 0.77
Table S57. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 53 | 7 | 24 | 9 |
subtype1 | 12 | 2 | 8 | 0 |
subtype2 | 18 | 3 | 6 | 7 |
subtype3 | 20 | 1 | 4 | 0 |
subtype4 | 3 | 1 | 6 | 2 |
Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D7V8.png)
Table S58. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 25 | 40 | 11 | 28 |
P value = 0.354 (logrank test), Q value = 1
Table S59. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 97 | 14 | 0.0 - 182.7 (13.7) |
subtype1 | 25 | 5 | 0.9 - 86.7 (14.6) |
subtype2 | 36 | 7 | 0.2 - 182.7 (17.8) |
subtype3 | 10 | 0 | 3.8 - 96.9 (22.6) |
subtype4 | 26 | 2 | 0.0 - 123.6 (6.6) |
Figure S50. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.541 (ANOVA), Q value = 1
Table S60. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 101 | 59.6 (12.4) |
subtype1 | 25 | 62.7 (13.0) |
subtype2 | 39 | 58.7 (13.8) |
subtype3 | 10 | 57.1 (12.0) |
subtype4 | 27 | 59.1 (9.6) |
Figure S51. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.0627 (Fisher's exact test), Q value = 1
Table S61. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 34 | 70 |
subtype1 | 5 | 20 |
subtype2 | 18 | 22 |
subtype3 | 1 | 10 |
subtype4 | 10 | 18 |
Figure S52. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
P value = 0.395 (ANOVA), Q value = 1
Table S62. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 22 | 87.7 (23.3) |
subtype1 | 5 | 74.0 (41.6) |
subtype2 | 11 | 89.1 (17.0) |
subtype3 | 2 | 100.0 (0.0) |
subtype4 | 4 | 95.0 (5.8) |
Figure S53. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D8V4.png)
P value = 0.000669 (Chi-square test), Q value = 0.035
Table S63. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3+T4 |
---|---|---|---|
ALL | 58 | 13 | 33 |
subtype1 | 9 | 2 | 14 |
subtype2 | 22 | 3 | 15 |
subtype3 | 10 | 0 | 1 |
subtype4 | 17 | 8 | 3 |
Figure S54. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D8V5.png)
P value = 0.29 (Chi-square test), Q value = 1
Table S64. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 20 | 12 | 4 |
subtype1 | 8 | 5 | 1 |
subtype2 | 7 | 7 | 3 |
subtype3 | 1 | 0 | 0 |
subtype4 | 4 | 0 | 0 |
Figure S55. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D8V6.png)
P value = 0.408 (Chi-square test), Q value = 1
Table S65. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 54 | 5 | 36 |
subtype1 | 17 | 1 | 7 |
subtype2 | 19 | 4 | 17 |
subtype3 | 7 | 0 | 3 |
subtype4 | 11 | 0 | 9 |
Figure S56. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D8V7.png)
P value = 0.00519 (Chi-square test), Q value = 0.26
Table S66. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 53 | 7 | 24 | 9 |
subtype1 | 9 | 2 | 13 | 1 |
subtype2 | 21 | 3 | 7 | 8 |
subtype3 | 9 | 0 | 1 | 0 |
subtype4 | 14 | 2 | 3 | 0 |
Figure S57. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D8V8.png)
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Cluster data file = KIRP-TP.mergedcluster.txt
-
Clinical data file = KIRP-TP.clin.merged.picked.txt
-
Number of patients = 104
-
Number of clustering approaches = 8
-
Number of selected clinical features = 8
-
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.