This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 10 different clustering approaches and 8 clinical features across 502 patients, 37 significant findings detected with P value < 0.05 and Q value < 0.25.
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CNMF clustering analysis on array-based mRNA expression data identified 3 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 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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CNMF clustering analysis on RPPA data identified 6 subtypes that correlate to 'Time to Death', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGY.N', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', 'PATHOLOGICSPREAD(M)', and 'TUMOR.STAGE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'PATHOLOGY.T', and 'TUMOR.STAGE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 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, 37 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 | Fisher's exact test | Fisher's exact test | Chi-square test |
mRNA CNMF subtypes |
0.231 (1.00) |
0.795 (1.00) |
0.634 (1.00) |
0.00911 (0.355) |
0.0704 (1.00) |
0.12 (1.00) |
0.0134 (0.442) |
|
mRNA cHierClus subtypes |
0.422 (1.00) |
0.607 (1.00) |
0.82 (1.00) |
0.00779 (0.32) |
0.124 (1.00) |
0.0988 (1.00) |
0.0102 (0.368) |
|
Copy Number Ratio CNMF subtypes |
0.000165 (0.00889) |
0.0782 (1.00) |
0.177 (1.00) |
0.889 (1.00) |
0.00196 (0.0884) |
0.0272 (0.87) |
0.000486 (0.0253) |
0.00154 (0.0709) |
METHLYATION CNMF |
6.32e-06 (0.000385) |
0.00858 (0.343) |
0.00326 (0.14) |
0.134 (1.00) |
2.64e-10 (2.01e-08) |
0.147 (1.00) |
6.41e-05 (0.00372) |
3.89e-09 (2.88e-07) |
RPPA CNMF subtypes |
1.79e-09 (1.34e-07) |
0.129 (1.00) |
0.0891 (1.00) |
0.322 (1.00) |
9.52e-07 (6.38e-05) |
0.00106 (0.0511) |
3.89e-07 (2.72e-05) |
8e-09 (5.84e-07) |
RPPA cHierClus subtypes |
8.92e-08 (6.33e-06) |
0.00927 (0.355) |
0.578 (1.00) |
0.0463 (1.00) |
4.03e-07 (2.78e-05) |
0.253 (1.00) |
9.01e-05 (0.00513) |
6.23e-07 (4.24e-05) |
RNAseq CNMF subtypes |
1.63e-06 (0.000106) |
0.09 (1.00) |
0.000154 (0.00847) |
0.66 (1.00) |
4.85e-06 (0.000301) |
0.0117 (0.398) |
0.000645 (0.0323) |
1.27e-06 (8.38e-05) |
RNAseq cHierClus subtypes |
3.34e-08 (2.4e-06) |
0.358 (1.00) |
0.00326 (0.14) |
0.357 (1.00) |
7.56e-12 (5.82e-10) |
0.000966 (0.0474) |
2e-06 (0.000128) |
2.63e-12 (2.05e-10) |
MIRSEQ CNMF |
2.31e-06 (0.000146) |
0.0424 (1.00) |
0.000437 (0.0232) |
0.172 (1.00) |
0.000549 (0.028) |
0.00922 (0.355) |
5.25e-05 (0.0031) |
2.55e-05 (0.00153) |
MIRSEQ CHIERARCHICAL |
0.00211 (0.0928) |
0.298 (1.00) |
0.124 (1.00) |
0.943 (1.00) |
0.000115 (0.00642) |
0.011 (0.385) |
0.351 (1.00) |
0.00108 (0.0511) |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 34 | 24 | 14 |
P value = 0.231 (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 | 71 | 13 | 0.5 - 101.1 (32.6) |
subtype1 | 33 | 4 | 0.5 - 101.1 (31.0) |
subtype2 | 24 | 8 | 0.5 - 93.3 (36.7) |
subtype3 | 14 | 1 | 1.3 - 84.4 (25.0) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.795 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 71 | 60.5 (12.4) |
subtype1 | 33 | 60.2 (13.8) |
subtype2 | 24 | 59.9 (11.1) |
subtype3 | 14 | 62.6 (11.3) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.634 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 29 | 43 |
subtype1 | 15 | 19 |
subtype2 | 10 | 14 |
subtype3 | 4 | 10 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 0.00911 (Chi-square test), Q value = 0.36
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 41 | 14 | 17 |
subtype1 | 23 | 4 | 7 |
subtype2 | 10 | 4 | 10 |
subtype3 | 8 | 6 | 0 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D1V5.png)
P value = 0.0704 (Fisher's exact test), Q value = 1
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 35 | 3 |
subtype1 | 18 | 0 |
subtype2 | 10 | 3 |
subtype3 | 7 | 0 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D1V6.png)
P value = 0.12 (Fisher's exact test), Q value = 1
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 67 | 5 |
subtype1 | 33 | 1 |
subtype2 | 20 | 4 |
subtype3 | 14 | 0 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D1V7.png)
P value = 0.0134 (Chi-square test), Q value = 0.44
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 40 | 15 | 12 | 5 |
subtype1 | 23 | 5 | 5 | 1 |
subtype2 | 9 | 4 | 7 | 4 |
subtype3 | 8 | 6 | 0 | 0 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D1V8.png)
Table S9. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 15 | 23 | 34 |
P value = 0.422 (logrank test), Q value = 1
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 71 | 13 | 0.5 - 101.1 (32.6) |
subtype1 | 15 | 2 | 1.3 - 84.4 (24.2) |
subtype2 | 23 | 7 | 0.5 - 93.3 (36.8) |
subtype3 | 33 | 4 | 0.5 - 101.1 (31.0) |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.607 (ANOVA), Q value = 1
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 71 | 60.5 (12.4) |
subtype1 | 15 | 63.2 (11.2) |
subtype2 | 23 | 59.1 (10.7) |
subtype3 | 33 | 60.4 (14.0) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.82 (Fisher's exact test), Q value = 1
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 29 | 43 |
subtype1 | 5 | 10 |
subtype2 | 9 | 14 |
subtype3 | 15 | 19 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.00779 (Chi-square test), Q value = 0.32
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 41 | 14 | 17 |
subtype1 | 9 | 6 | 0 |
subtype2 | 9 | 4 | 10 |
subtype3 | 23 | 4 | 7 |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D2V5.png)
P value = 0.124 (Fisher's exact test), Q value = 1
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 35 | 3 |
subtype1 | 7 | 0 |
subtype2 | 11 | 3 |
subtype3 | 17 | 0 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D2V6.png)
P value = 0.0988 (Fisher's exact test), Q value = 1
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 67 | 5 |
subtype1 | 15 | 0 |
subtype2 | 19 | 4 |
subtype3 | 33 | 1 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D2V7.png)
P value = 0.0102 (Chi-square test), Q value = 0.37
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 40 | 15 | 12 | 5 |
subtype1 | 9 | 6 | 0 | 0 |
subtype2 | 8 | 4 | 7 | 4 |
subtype3 | 23 | 5 | 5 | 1 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D2V8.png)
Table S17. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 132 | 197 | 164 |
P value = 0.000165 (logrank test), Q value = 0.0089
Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 490 | 158 | 0.1 - 111.0 (35.2) |
subtype1 | 132 | 50 | 0.2 - 97.5 (36.6) |
subtype2 | 195 | 43 | 0.1 - 111.0 (37.5) |
subtype3 | 163 | 65 | 0.1 - 109.9 (28.5) |
Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.0782 (ANOVA), Q value = 1
Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 492 | 60.6 (12.2) |
subtype1 | 131 | 62.5 (11.8) |
subtype2 | 197 | 59.5 (12.7) |
subtype3 | 164 | 60.3 (11.7) |
Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.177 (Fisher's exact test), Q value = 1
Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 171 | 322 |
subtype1 | 42 | 90 |
subtype2 | 78 | 119 |
subtype3 | 51 | 113 |
Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.889 (ANOVA), Q value = 1
Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 36 | 88.3 (23.0) |
subtype1 | 14 | 88.6 (26.0) |
subtype2 | 9 | 91.1 (10.5) |
subtype3 | 13 | 86.2 (26.9) |
Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 0.00196 (Chi-square test), Q value = 0.088
Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 242 | 64 | 176 | 11 |
subtype1 | 54 | 19 | 57 | 2 |
subtype2 | 116 | 25 | 55 | 1 |
subtype3 | 72 | 20 | 64 | 8 |
Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D3V5.png)
P value = 0.0272 (Fisher's exact test), Q value = 0.87
Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 228 | 18 |
subtype1 | 55 | 5 |
subtype2 | 91 | 2 |
subtype3 | 82 | 11 |
Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D3V6.png)
P value = 0.000486 (Fisher's exact test), Q value = 0.025
Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 417 | 76 |
subtype1 | 102 | 30 |
subtype2 | 181 | 16 |
subtype3 | 134 | 30 |
Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D3V7.png)
P value = 0.00154 (Chi-square test), Q value = 0.071
Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 238 | 52 | 123 | 80 |
subtype1 | 52 | 16 | 33 | 31 |
subtype2 | 116 | 19 | 45 | 17 |
subtype3 | 70 | 17 | 45 | 32 |
Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D3V8.png)
Table S26. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 97 | 120 | 66 |
P value = 6.32e-06 (logrank test), Q value = 0.00039
Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 281 | 95 | 0.1 - 109.9 (28.5) |
subtype1 | 96 | 49 | 0.2 - 84.7 (28.6) |
subtype2 | 119 | 21 | 0.1 - 109.6 (31.5) |
subtype3 | 66 | 25 | 0.1 - 109.9 (21.3) |
Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.00858 (ANOVA), Q value = 0.34
Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 283 | 61.5 (12.0) |
subtype1 | 97 | 63.6 (10.2) |
subtype2 | 120 | 59.0 (12.9) |
subtype3 | 66 | 63.0 (11.9) |
Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.00326 (Fisher's exact test), Q value = 0.14
Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 96 | 187 |
subtype1 | 21 | 76 |
subtype2 | 52 | 68 |
subtype3 | 23 | 43 |
Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.134 (ANOVA), Q value = 1
Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 28 | 92.5 (8.0) |
subtype1 | 6 | 88.3 (7.5) |
subtype2 | 17 | 92.4 (8.3) |
subtype3 | 5 | 98.0 (4.5) |
Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D4V4.png)
P value = 2.64e-10 (Chi-square test), Q value = 2e-08
Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 132 | 36 | 107 | 8 |
subtype1 | 20 | 14 | 59 | 4 |
subtype2 | 78 | 18 | 24 | 0 |
subtype3 | 34 | 4 | 24 | 4 |
Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D4V5.png)
P value = 0.147 (Fisher's exact test), Q value = 1
Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 127 | 9 |
subtype1 | 43 | 5 |
subtype2 | 54 | 1 |
subtype3 | 30 | 3 |
Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D4V6.png)
P value = 6.41e-05 (Fisher's exact test), Q value = 0.0037
Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 232 | 51 |
subtype1 | 66 | 31 |
subtype2 | 109 | 11 |
subtype3 | 57 | 9 |
Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D4V7.png)
P value = 3.89e-09 (Chi-square test), Q value = 2.9e-07
Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 130 | 24 | 73 | 56 |
subtype1 | 20 | 9 | 35 | 33 |
subtype2 | 78 | 12 | 19 | 11 |
subtype3 | 32 | 3 | 19 | 12 |
Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D4V8.png)
Table S35. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Number of samples | 101 | 90 | 86 | 76 | 44 | 57 |
P value = 1.79e-09 (logrank test), Q value = 1.3e-07
Table S36. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 452 | 151 | 0.1 - 111.0 (34.3) |
subtype1 | 101 | 26 | 0.2 - 111.0 (43.7) |
subtype2 | 90 | 35 | 0.1 - 90.4 (29.4) |
subtype3 | 85 | 22 | 0.2 - 93.0 (35.3) |
subtype4 | 75 | 23 | 0.1 - 96.8 (27.9) |
subtype5 | 44 | 8 | 0.2 - 83.8 (36.5) |
subtype6 | 57 | 37 | 0.6 - 84.0 (19.7) |
Figure S31. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.129 (ANOVA), Q value = 1
Table S37. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 453 | 60.4 (12.3) |
subtype1 | 100 | 61.8 (11.1) |
subtype2 | 90 | 58.0 (12.2) |
subtype3 | 86 | 62.3 (12.6) |
subtype4 | 76 | 60.0 (11.8) |
subtype5 | 44 | 58.3 (15.7) |
subtype6 | 57 | 61.2 (11.4) |
Figure S32. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0891 (Chi-square test), Q value = 1
Table S38. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 151 | 303 |
subtype1 | 43 | 58 |
subtype2 | 29 | 61 |
subtype3 | 28 | 58 |
subtype4 | 18 | 58 |
subtype5 | 18 | 26 |
subtype6 | 15 | 42 |
Figure S33. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.322 (ANOVA), Q value = 1
Table S39. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 34 | 93.5 (7.7) |
subtype1 | 7 | 92.9 (7.6) |
subtype2 | 8 | 93.8 (5.2) |
subtype3 | 10 | 90.0 (9.4) |
subtype4 | 2 | 100.0 (0.0) |
subtype5 | 4 | 100.0 (0.0) |
subtype6 | 3 | 93.3 (11.5) |
Figure S34. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V4.png)
P value = 9.52e-07 (Chi-square test), Q value = 6.4e-05
Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 224 | 54 | 165 | 11 |
subtype1 | 54 | 15 | 31 | 1 |
subtype2 | 43 | 10 | 32 | 5 |
subtype3 | 41 | 12 | 33 | 0 |
subtype4 | 45 | 7 | 23 | 1 |
subtype5 | 33 | 2 | 9 | 0 |
subtype6 | 8 | 8 | 37 | 4 |
Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D5V5.png)
P value = 0.00106 (Chi-square test), Q value = 0.051
Table S41. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 208 | 16 |
subtype1 | 48 | 1 |
subtype2 | 46 | 2 |
subtype3 | 43 | 2 |
subtype4 | 35 | 3 |
subtype5 | 13 | 0 |
subtype6 | 23 | 8 |
Figure S36. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D5V6.png)
P value = 3.89e-07 (Chi-square test), Q value = 2.7e-05
Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 380 | 74 |
subtype1 | 87 | 14 |
subtype2 | 71 | 19 |
subtype3 | 76 | 10 |
subtype4 | 68 | 8 |
subtype5 | 44 | 0 |
subtype6 | 34 | 23 |
Figure S37. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D5V7.png)
P value = 8e-09 (Chi-square test), Q value = 5.8e-07
Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 219 | 43 | 114 | 78 |
subtype1 | 54 | 10 | 22 | 15 |
subtype2 | 42 | 10 | 18 | 20 |
subtype3 | 39 | 11 | 26 | 10 |
subtype4 | 44 | 7 | 16 | 9 |
subtype5 | 33 | 2 | 9 | 0 |
subtype6 | 7 | 3 | 23 | 24 |
Figure S38. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D5V8.png)
Table S44. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 189 | 153 | 112 |
P value = 8.92e-08 (logrank test), Q value = 6.3e-06
Table S45. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 452 | 151 | 0.1 - 111.0 (34.3) |
subtype1 | 189 | 42 | 0.1 - 96.8 (37.0) |
subtype2 | 153 | 50 | 0.2 - 111.0 (36.8) |
subtype3 | 110 | 59 | 0.1 - 91.4 (21.5) |
Figure S39. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.00927 (ANOVA), Q value = 0.36
Table S46. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 453 | 60.4 (12.3) |
subtype1 | 189 | 58.4 (12.7) |
subtype2 | 152 | 62.3 (12.3) |
subtype3 | 112 | 61.3 (11.1) |
Figure S40. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.578 (Fisher's exact test), Q value = 1
Table S47. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 151 | 303 |
subtype1 | 68 | 121 |
subtype2 | 47 | 106 |
subtype3 | 36 | 76 |
Figure S41. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.0463 (ANOVA), Q value = 1
Table S48. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 34 | 93.5 (7.7) |
subtype1 | 17 | 96.5 (4.9) |
subtype2 | 10 | 89.0 (8.8) |
subtype3 | 7 | 92.9 (9.5) |
Figure S42. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D6V4.png)
P value = 4.03e-07 (Chi-square test), Q value = 2.8e-05
Table S49. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 224 | 54 | 165 | 11 |
subtype1 | 120 | 19 | 48 | 2 |
subtype2 | 67 | 20 | 65 | 1 |
subtype3 | 37 | 15 | 52 | 8 |
Figure S43. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D6V5.png)
P value = 0.253 (Fisher's exact test), Q value = 1
Table S50. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 208 | 16 |
subtype1 | 72 | 4 |
subtype2 | 76 | 4 |
subtype3 | 60 | 8 |
Figure S44. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D6V6.png)
P value = 9.01e-05 (Fisher's exact test), Q value = 0.0051
Table S51. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 380 | 74 |
subtype1 | 170 | 19 |
subtype2 | 131 | 22 |
subtype3 | 79 | 33 |
Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D6V7.png)
P value = 6.23e-07 (Chi-square test), Q value = 4.2e-05
Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 219 | 43 | 114 | 78 |
subtype1 | 118 | 16 | 36 | 19 |
subtype2 | 65 | 16 | 48 | 24 |
subtype3 | 36 | 11 | 30 | 35 |
Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D6V8.png)
Table S53. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 199 | 180 | 101 |
P value = 1.63e-06 (logrank test), Q value = 0.00011
Table S54. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 155 | 0.1 - 111.0 (34.3) |
subtype1 | 199 | 44 | 0.1 - 111.0 (37.0) |
subtype2 | 179 | 83 | 0.1 - 90.3 (30.6) |
subtype3 | 100 | 28 | 0.1 - 93.3 (35.2) |
Figure S47. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.09 (ANOVA), Q value = 1
Table S55. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 479 | 60.6 (12.2) |
subtype1 | 198 | 61.7 (12.2) |
subtype2 | 180 | 60.6 (11.8) |
subtype3 | 101 | 58.4 (12.7) |
Figure S48. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.000154 (Fisher's exact test), Q value = 0.0085
Table S56. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 167 | 313 |
subtype1 | 90 | 109 |
subtype2 | 45 | 135 |
subtype3 | 32 | 69 |
Figure S49. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.66 (ANOVA), Q value = 1
Table S57. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 34 | 90.9 (17.8) |
subtype1 | 14 | 91.4 (8.6) |
subtype2 | 12 | 87.5 (28.3) |
subtype3 | 8 | 95.0 (7.6) |
Figure S50. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V4.png)
P value = 4.85e-06 (Chi-square test), Q value = 3e-04
Table S58. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 238 | 60 | 171 | 11 |
subtype1 | 118 | 22 | 57 | 2 |
subtype2 | 59 | 27 | 88 | 6 |
subtype3 | 61 | 11 | 26 | 3 |
Figure S51. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D7V5.png)
P value = 0.0117 (Fisher's exact test), Q value = 0.4
Table S59. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 228 | 17 |
subtype1 | 96 | 2 |
subtype2 | 85 | 12 |
subtype3 | 47 | 3 |
Figure S52. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D7V6.png)
P value = 0.000645 (Fisher's exact test), Q value = 0.032
Table S60. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 403 | 77 |
subtype1 | 176 | 23 |
subtype2 | 136 | 44 |
subtype3 | 91 | 10 |
Figure S53. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D7V7.png)
P value = 1.27e-06 (Chi-square test), Q value = 8.4e-05
Table S61. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 234 | 48 | 117 | 81 |
subtype1 | 118 | 18 | 39 | 24 |
subtype2 | 56 | 21 | 58 | 45 |
subtype3 | 60 | 9 | 20 | 12 |
Figure S54. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D7V8.png)
Table S62. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 73 | 215 | 192 |
P value = 3.34e-08 (logrank test), Q value = 2.4e-06
Table S63. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 478 | 155 | 0.1 - 111.0 (34.3) |
subtype1 | 72 | 14 | 0.2 - 92.0 (27.8) |
subtype2 | 214 | 48 | 0.1 - 111.0 (37.2) |
subtype3 | 192 | 93 | 0.1 - 93.3 (30.5) |
Figure S55. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.358 (ANOVA), Q value = 1
Table S64. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 479 | 60.6 (12.2) |
subtype1 | 73 | 58.7 (13.2) |
subtype2 | 214 | 61.0 (12.4) |
subtype3 | 192 | 60.8 (11.5) |
Figure S56. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.00326 (Fisher's exact test), Q value = 0.14
Table S65. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 167 | 313 |
subtype1 | 23 | 50 |
subtype2 | 92 | 123 |
subtype3 | 52 | 140 |
Figure S57. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
P value = 0.357 (ANOVA), Q value = 1
Table S66. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 34 | 90.9 (17.8) |
subtype1 | 10 | 95.0 (9.7) |
subtype2 | 13 | 93.1 (6.3) |
subtype3 | 11 | 84.5 (29.1) |
Figure S58. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D8V4.png)
P value = 7.56e-12 (Chi-square test), Q value = 5.8e-10
Table S67. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 238 | 60 | 171 | 11 |
subtype1 | 55 | 6 | 11 | 1 |
subtype2 | 126 | 27 | 61 | 1 |
subtype3 | 57 | 27 | 99 | 9 |
Figure S59. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D8V5.png)
P value = 0.000966 (Fisher's exact test), Q value = 0.047
Table S68. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 228 | 17 |
subtype1 | 32 | 2 |
subtype2 | 104 | 1 |
subtype3 | 92 | 14 |
Figure S60. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D8V6.png)
P value = 2e-06 (Fisher's exact test), Q value = 0.00013
Table S69. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 403 | 77 |
subtype1 | 70 | 3 |
subtype2 | 191 | 24 |
subtype3 | 142 | 50 |
Figure S61. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D8V7.png)
P value = 2.63e-12 (Chi-square test), Q value = 2.1e-10
Table S70. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 234 | 48 | 117 | 81 |
subtype1 | 54 | 6 | 10 | 3 |
subtype2 | 126 | 21 | 43 | 25 |
subtype3 | 54 | 21 | 64 | 53 |
Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D8V8.png)
Table S71. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 117 | 192 | 172 |
P value = 2.31e-06 (logrank test), Q value = 0.00015
Table S72. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 479 | 156 | 0.1 - 111.0 (35.2) |
subtype1 | 117 | 32 | 0.1 - 109.9 (37.0) |
subtype2 | 192 | 43 | 0.1 - 111.0 (35.8) |
subtype3 | 170 | 81 | 0.2 - 93.3 (30.6) |
Figure S63. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.0424 (ANOVA), Q value = 1
Table S73. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 481 | 60.6 (12.2) |
subtype1 | 117 | 58.6 (12.3) |
subtype2 | 192 | 62.1 (12.2) |
subtype3 | 172 | 60.2 (11.9) |
Figure S64. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D9V2.png)
P value = 0.000437 (Fisher's exact test), Q value = 0.023
Table S74. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 163 | 318 |
subtype1 | 37 | 80 |
subtype2 | 84 | 108 |
subtype3 | 42 | 130 |
Figure S65. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
![](D9V3.png)
P value = 0.172 (ANOVA), Q value = 1
Table S75. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 36 | 88.3 (23.0) |
subtype1 | 9 | 95.6 (7.3) |
subtype2 | 15 | 92.0 (8.6) |
subtype3 | 12 | 78.3 (37.1) |
Figure S66. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D9V4.png)
P value = 0.000549 (Chi-square test), Q value = 0.028
Table S76. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 233 | 62 | 175 | 11 |
subtype1 | 68 | 11 | 34 | 4 |
subtype2 | 106 | 23 | 61 | 2 |
subtype3 | 59 | 28 | 80 | 5 |
Figure S67. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D9V5.png)
P value = 0.00922 (Fisher's exact test), Q value = 0.36
Table S77. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 222 | 18 |
subtype1 | 56 | 3 |
subtype2 | 86 | 2 |
subtype3 | 80 | 13 |
Figure S68. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D9V6.png)
P value = 5.25e-05 (Fisher's exact test), Q value = 0.0031
Table S78. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 405 | 76 |
subtype1 | 108 | 9 |
subtype2 | 169 | 23 |
subtype3 | 128 | 44 |
Figure S69. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D9V7.png)
P value = 2.55e-05 (Chi-square test), Q value = 0.0015
Table S79. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 229 | 50 | 122 | 80 |
subtype1 | 66 | 11 | 30 | 10 |
subtype2 | 106 | 17 | 45 | 24 |
subtype3 | 57 | 22 | 47 | 46 |
Figure S70. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D9V8.png)
Table S80. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 37 | 162 | 282 |
P value = 0.00211 (logrank test), Q value = 0.093
Table S81. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 479 | 156 | 0.1 - 111.0 (35.2) |
subtype1 | 36 | 13 | 0.5 - 85.2 (29.0) |
subtype2 | 162 | 70 | 0.1 - 109.9 (35.4) |
subtype3 | 281 | 73 | 0.1 - 111.0 (35.2) |
Figure S71. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.298 (ANOVA), Q value = 1
Table S82. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 481 | 60.6 (12.2) |
subtype1 | 37 | 57.8 (11.0) |
subtype2 | 162 | 60.3 (12.1) |
subtype3 | 282 | 61.1 (12.4) |
Figure S72. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D10V2.png)
P value = 0.124 (Fisher's exact test), Q value = 1
Table S83. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 163 | 318 |
subtype1 | 13 | 24 |
subtype2 | 45 | 117 |
subtype3 | 105 | 177 |
Figure S73. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
![](D10V3.png)
P value = 0.943 (ANOVA), Q value = 1
Table S84. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 36 | 88.3 (23.0) |
subtype1 | 2 | 95.0 (7.1) |
subtype2 | 12 | 87.5 (28.3) |
subtype3 | 22 | 88.2 (21.3) |
Figure S74. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D10V4.png)
P value = 0.000115 (Chi-square test), Q value = 0.0064
Table S85. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 233 | 62 | 175 | 11 |
subtype1 | 16 | 10 | 10 | 1 |
subtype2 | 62 | 17 | 75 | 8 |
subtype3 | 155 | 35 | 90 | 2 |
Figure S75. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D10V5.png)
P value = 0.011 (Fisher's exact test), Q value = 0.38
Table S86. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | 0 | 1 |
---|---|---|
ALL | 222 | 18 |
subtype1 | 19 | 3 |
subtype2 | 76 | 11 |
subtype3 | 127 | 4 |
Figure S76. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D10V6.png)
P value = 0.351 (Fisher's exact test), Q value = 1
Table S87. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 405 | 76 |
subtype1 | 30 | 7 |
subtype2 | 132 | 30 |
subtype3 | 243 | 39 |
Figure S77. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D10V7.png)
P value = 0.00108 (Chi-square test), Q value = 0.051
Table S88. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 229 | 50 | 122 | 80 |
subtype1 | 16 | 9 | 5 | 7 |
subtype2 | 61 | 15 | 54 | 32 |
subtype3 | 152 | 26 | 63 | 41 |
Figure S78. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D10V8.png)
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Cluster data file = KIRC-TP.mergedcluster.txt
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Clinical data file = KIRC-TP.clin.merged.picked.txt
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Number of patients = 502
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Number of clustering approaches = 10
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Number of selected clinical features = 8
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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
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 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.