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 9 clinical features across 422 patients, 13 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 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE', 'PATHOLOGY.N', and 'TUMOR.STAGE'.
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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
-
CNMF clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER', 'HISTOLOGICAL.TYPE', and 'TUMOR.STAGE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE' and 'PATHOLOGICSPREAD(M)'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'PATHOLOGICSPREAD(M)'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 9 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 13 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
HISTOLOGICAL TYPE |
PATHOLOGY T |
PATHOLOGY N |
PATHOLOGICSPREAD(M) |
TUMOR STAGE |
RADIATIONS RADIATION REGIMENINDICATION |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Fisher's exact test | Chi-square test | Chi-square test | Chi-square test | Chi-square test | Fisher's exact test |
mRNA CNMF subtypes |
0.31 (1.00) |
0.418 (1.00) |
0.0625 (1.00) |
3.12e-06 (0.000262) |
0.89 (1.00) |
0.0267 (1.00) |
0.0549 (1.00) |
0.0374 (1.00) |
|
mRNA cHierClus subtypes |
0.755 (1.00) |
0.146 (1.00) |
0.0978 (1.00) |
3.57e-09 (3.14e-07) |
0.561 (1.00) |
0.58 (1.00) |
0.848 (1.00) |
0.509 (1.00) |
|
Copy Number Ratio CNMF subtypes |
0.755 (1.00) |
0.472 (1.00) |
0.487 (1.00) |
3.7e-08 (3.22e-06) |
0.944 (1.00) |
0.00113 (0.0895) |
0.00999 (0.729) |
3.6e-05 (0.00292) |
0.337 (1.00) |
METHLYATION CNMF |
0.928 (1.00) |
0.00205 (0.16) |
0.0531 (1.00) |
0.0769 (1.00) |
0.375 (1.00) |
0.904 (1.00) |
0.304 (1.00) |
0.036 (1.00) |
0.473 (1.00) |
RPPA CNMF subtypes |
0.429 (1.00) |
0.501 (1.00) |
0.306 (1.00) |
0.015 (1.00) |
0.23 (1.00) |
0.141 (1.00) |
0.186 (1.00) |
0.117 (1.00) |
0.302 (1.00) |
RPPA cHierClus subtypes |
0.302 (1.00) |
0.169 (1.00) |
0.348 (1.00) |
0.464 (1.00) |
0.28 (1.00) |
0.123 (1.00) |
0.0432 (1.00) |
0.0747 (1.00) |
0.339 (1.00) |
RNAseq CNMF subtypes |
0.871 (1.00) |
0.105 (1.00) |
0.00324 (0.246) |
2.4e-07 (2.04e-05) |
0.7 (1.00) |
0.0246 (1.00) |
0.022 (1.00) |
0.00265 (0.204) |
1 (1.00) |
RNAseq cHierClus subtypes |
0.311 (1.00) |
0.00338 (0.254) |
0.456 (1.00) |
8.97e-08 (7.71e-06) |
0.638 (1.00) |
0.238 (1.00) |
0.342 (1.00) |
0.631 (1.00) |
1 (1.00) |
MIRSEQ CNMF |
0.246 (1.00) |
0.000748 (0.0599) |
0.525 (1.00) |
0.0162 (1.00) |
0.264 (1.00) |
0.515 (1.00) |
1.25e-05 (0.00102) |
0.596 (1.00) |
0.544 (1.00) |
MIRSEQ CHIERARCHICAL |
0.521 (1.00) |
0.00978 (0.724) |
0.514 (1.00) |
0.67 (1.00) |
0.498 (1.00) |
0.95 (1.00) |
7.64e-06 (0.000634) |
0.448 (1.00) |
1 (1.00) |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 63 | 35 | 31 | 26 |
P value = 0.31 (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 | 75 | 11 | 0.9 - 52.0 (5.0) |
subtype1 | 27 | 6 | 1.0 - 41.0 (14.4) |
subtype2 | 22 | 1 | 1.0 - 30.0 (1.0) |
subtype3 | 18 | 3 | 1.0 - 52.0 (8.0) |
subtype4 | 8 | 1 | 0.9 - 1.0 (1.0) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.418 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 155 | 70.6 (11.7) |
subtype1 | 63 | 69.2 (11.9) |
subtype2 | 35 | 72.1 (11.7) |
subtype3 | 31 | 72.7 (10.9) |
subtype4 | 26 | 69.3 (12.1) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.0625 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 76 | 79 |
subtype1 | 32 | 31 |
subtype2 | 23 | 12 |
subtype3 | 11 | 20 |
subtype4 | 10 | 16 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 3.12e-06 (Fisher's exact test), Q value = 0.00026
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 128 | 24 |
subtype1 | 62 | 1 |
subtype2 | 22 | 12 |
subtype3 | 21 | 9 |
subtype4 | 23 | 2 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D1V4.png)
P value = 0.89 (Chi-square test), Q value = 1
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 4 | 31 | 105 | 13 |
subtype1 | 2 | 13 | 43 | 5 |
subtype2 | 1 | 7 | 23 | 2 |
subtype3 | 0 | 6 | 20 | 5 |
subtype4 | 1 | 5 | 19 | 1 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D1V5.png)
P value = 0.0267 (Chi-square test), Q value = 1
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 95 | 28 | 32 |
subtype1 | 29 | 15 | 19 |
subtype2 | 24 | 4 | 7 |
subtype3 | 24 | 6 | 1 |
subtype4 | 18 | 3 | 5 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D1V6.png)
P value = 0.0549 (Chi-square test), Q value = 1
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A |
---|---|---|---|
ALL | 129 | 22 | 1 |
subtype1 | 48 | 15 | 0 |
subtype2 | 30 | 4 | 0 |
subtype3 | 28 | 1 | 1 |
subtype4 | 23 | 2 | 0 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D1V7.png)
P value = 0.0374 (Chi-square test), Q value = 1
Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 28 | 63 | 39 | 22 |
subtype1 | 12 | 15 | 21 | 15 |
subtype2 | 6 | 17 | 7 | 3 |
subtype3 | 5 | 18 | 5 | 2 |
subtype4 | 5 | 13 | 6 | 2 |
Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D1V8.png)
Table S10. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 47 | 48 | 14 | 46 |
P value = 0.755 (logrank test), Q value = 1
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 75 | 11 | 0.9 - 52.0 (5.0) |
subtype1 | 28 | 2 | 1.0 - 30.0 (1.0) |
subtype2 | 27 | 5 | 1.0 - 41.0 (15.0) |
subtype3 | 7 | 2 | 0.9 - 52.0 (14.4) |
subtype4 | 13 | 2 | 0.9 - 34.0 (1.0) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.146 (ANOVA), Q value = 1
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 155 | 70.6 (11.7) |
subtype1 | 47 | 73.1 (11.5) |
subtype2 | 48 | 70.7 (9.6) |
subtype3 | 14 | 71.6 (12.8) |
subtype4 | 46 | 67.5 (13.0) |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.0978 (Fisher's exact test), Q value = 1
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 76 | 79 |
subtype1 | 27 | 20 |
subtype2 | 27 | 21 |
subtype3 | 6 | 8 |
subtype4 | 16 | 30 |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 3.57e-09 (Fisher's exact test), Q value = 3.1e-07
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 128 | 24 |
subtype1 | 25 | 20 |
subtype2 | 46 | 2 |
subtype3 | 12 | 2 |
subtype4 | 45 | 0 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D2V4.png)
P value = 0.561 (Chi-square test), Q value = 1
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 4 | 31 | 105 | 13 |
subtype1 | 1 | 10 | 28 | 6 |
subtype2 | 1 | 7 | 36 | 4 |
subtype3 | 1 | 1 | 11 | 1 |
subtype4 | 1 | 13 | 30 | 2 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D2V5.png)
P value = 0.58 (Chi-square test), Q value = 1
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 95 | 28 | 32 |
subtype1 | 31 | 7 | 9 |
subtype2 | 25 | 13 | 10 |
subtype3 | 10 | 1 | 3 |
subtype4 | 29 | 7 | 10 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D2V6.png)
P value = 0.848 (Chi-square test), Q value = 1
Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A |
---|---|---|---|
ALL | 129 | 22 | 1 |
subtype1 | 40 | 6 | 0 |
subtype2 | 38 | 8 | 1 |
subtype3 | 12 | 2 | 0 |
subtype4 | 39 | 6 | 0 |
Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D2V7.png)
P value = 0.509 (Chi-square test), Q value = 1
Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 28 | 63 | 39 | 22 |
subtype1 | 9 | 21 | 10 | 5 |
subtype2 | 5 | 17 | 16 | 9 |
subtype3 | 2 | 8 | 2 | 2 |
subtype4 | 12 | 17 | 11 | 6 |
Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D2V8.png)
Table S19. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 170 | 187 | 32 | 24 |
P value = 0.755 (logrank test), Q value = 1
Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 319 | 49 | 0.1 - 135.5 (7.7) |
subtype1 | 127 | 23 | 0.1 - 129.1 (10.0) |
subtype2 | 149 | 19 | 0.1 - 129.1 (6.1) |
subtype3 | 23 | 4 | 0.3 - 135.5 (7.0) |
subtype4 | 20 | 3 | 0.2 - 112.7 (6.0) |
Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.472 (ANOVA), Q value = 1
Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 412 | 67.3 (13.0) |
subtype1 | 170 | 66.4 (12.4) |
subtype2 | 186 | 68.3 (13.7) |
subtype3 | 32 | 66.0 (13.0) |
subtype4 | 24 | 68.2 (12.0) |
Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.487 (Fisher's exact test), Q value = 1
Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 193 | 220 |
subtype1 | 77 | 93 |
subtype2 | 92 | 95 |
subtype3 | 16 | 16 |
subtype4 | 8 | 16 |
Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 3.7e-08 (Fisher's exact test), Q value = 3.2e-06
Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 356 | 54 |
subtype1 | 162 | 7 |
subtype2 | 140 | 45 |
subtype3 | 31 | 1 |
subtype4 | 23 | 1 |
Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D3V4.png)
P value = 0.944 (Chi-square test), Q value = 1
Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 11 | 72 | 283 | 45 |
subtype1 | 5 | 27 | 120 | 18 |
subtype2 | 6 | 33 | 124 | 22 |
subtype3 | 0 | 6 | 23 | 3 |
subtype4 | 0 | 6 | 16 | 2 |
Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D3V5.png)
P value = 0.00113 (Chi-square test), Q value = 0.089
Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 245 | 95 | 71 |
subtype1 | 78 | 54 | 36 |
subtype2 | 131 | 29 | 27 |
subtype3 | 20 | 7 | 5 |
subtype4 | 16 | 5 | 3 |
Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D3V6.png)
P value = 0.00999 (Chi-square test), Q value = 0.73
Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 312 | 49 | 7 | 1 | 36 |
subtype1 | 119 | 32 | 3 | 0 | 14 |
subtype2 | 153 | 10 | 1 | 1 | 18 |
subtype3 | 21 | 6 | 2 | 0 | 2 |
subtype4 | 19 | 1 | 1 | 0 | 2 |
Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D3V7.png)
P value = 3.6e-05 (Chi-square test), Q value = 0.0029
Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 67 | 157 | 116 | 56 |
subtype1 | 23 | 46 | 60 | 35 |
subtype2 | 35 | 89 | 44 | 11 |
subtype3 | 4 | 13 | 5 | 8 |
subtype4 | 5 | 9 | 7 | 2 |
Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D3V8.png)
P value = 0.337 (Fisher's exact test), Q value = 1
Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 410 |
subtype1 | 3 | 167 |
subtype2 | 0 | 187 |
subtype3 | 0 | 32 |
subtype4 | 0 | 24 |
Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V9.png)
Table S29. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 106 | 86 | 63 |
P value = 0.928 (logrank test), Q value = 1
Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 237 | 36 | 0.1 - 135.5 (7.6) |
subtype1 | 95 | 15 | 0.1 - 135.5 (8.0) |
subtype2 | 81 | 13 | 0.1 - 129.1 (8.0) |
subtype3 | 61 | 8 | 0.1 - 102.4 (5.8) |
Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.00205 (ANOVA), Q value = 0.16
Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 254 | 65.4 (13.3) |
subtype1 | 106 | 66.3 (13.1) |
subtype2 | 86 | 68.0 (13.1) |
subtype3 | 62 | 60.5 (12.6) |
Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0531 (Fisher's exact test), Q value = 1
Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 115 | 140 |
subtype1 | 42 | 64 |
subtype2 | 48 | 38 |
subtype3 | 25 | 38 |
Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.0769 (Fisher's exact test), Q value = 1
Table S33. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 225 | 30 |
subtype1 | 99 | 7 |
subtype2 | 73 | 13 |
subtype3 | 53 | 10 |
Figure S29. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D4V4.png)
P value = 0.375 (Chi-square test), Q value = 1
Table S34. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 7 | 38 | 178 | 31 |
subtype1 | 3 | 18 | 72 | 13 |
subtype2 | 3 | 16 | 58 | 8 |
subtype3 | 1 | 4 | 48 | 10 |
Figure S30. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D4V5.png)
P value = 0.904 (Chi-square test), Q value = 1
Table S35. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 152 | 64 | 37 |
subtype1 | 59 | 29 | 16 |
subtype2 | 53 | 20 | 13 |
subtype3 | 40 | 15 | 8 |
Figure S31. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D4V6.png)
P value = 0.304 (Chi-square test), Q value = 1
Table S36. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 180 | 27 | 6 | 1 | 36 |
subtype1 | 69 | 16 | 4 | 0 | 16 |
subtype2 | 61 | 6 | 1 | 1 | 14 |
subtype3 | 50 | 5 | 1 | 0 | 6 |
Figure S32. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D4V7.png)
P value = 0.036 (Chi-square test), Q value = 1
Table S37. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 37 | 98 | 72 | 34 |
subtype1 | 15 | 34 | 31 | 20 |
subtype2 | 18 | 32 | 23 | 9 |
subtype3 | 4 | 32 | 18 | 5 |
Figure S33. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D4V8.png)
P value = 0.473 (Fisher's exact test), Q value = 1
Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 252 |
subtype1 | 2 | 104 |
subtype2 | 0 | 86 |
subtype3 | 1 | 62 |
Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V9.png)
Table S39. Get Full Table Description of clustering approach #5: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 65 | 119 | 7 | 78 |
P value = 0.429 (logrank test), Q value = 1
Table S40. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 206 | 19 | 0.1 - 105.3 (6.0) |
subtype1 | 56 | 2 | 0.1 - 87.8 (5.2) |
subtype2 | 82 | 7 | 0.1 - 75.2 (6.5) |
subtype3 | 2 | 0 | 0.6 - 18.1 (9.4) |
subtype4 | 66 | 10 | 0.1 - 105.3 (6.0) |
Figure S35. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.501 (ANOVA), Q value = 1
Table S41. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 268 | 66.6 (13.3) |
subtype1 | 65 | 66.7 (12.0) |
subtype2 | 118 | 66.2 (13.4) |
subtype3 | 7 | 74.1 (9.4) |
subtype4 | 78 | 66.3 (14.6) |
Figure S36. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.306 (Fisher's exact test), Q value = 1
Table S42. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 127 | 142 |
subtype1 | 34 | 31 |
subtype2 | 55 | 64 |
subtype3 | 1 | 6 |
subtype4 | 37 | 41 |
Figure S37. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.015 (Fisher's exact test), Q value = 1
Table S43. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 238 | 30 |
subtype1 | 51 | 14 |
subtype2 | 112 | 7 |
subtype3 | 6 | 1 |
subtype4 | 69 | 8 |
Figure S38. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D5V4.png)
P value = 0.23 (Chi-square test), Q value = 1
Table S44. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 5 | 45 | 185 | 31 |
subtype1 | 0 | 9 | 46 | 10 |
subtype2 | 1 | 22 | 82 | 11 |
subtype3 | 0 | 0 | 7 | 0 |
subtype4 | 4 | 14 | 50 | 10 |
Figure S39. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D5V5.png)
P value = 0.141 (Chi-square test), Q value = 1
Table S45. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 160 | 66 | 42 |
subtype1 | 33 | 19 | 12 |
subtype2 | 66 | 31 | 22 |
subtype3 | 6 | 0 | 1 |
subtype4 | 55 | 16 | 7 |
Figure S40. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D5V6.png)
P value = 0.186 (Chi-square test), Q value = 1
Table S46. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 202 | 31 | 6 | 1 | 26 |
subtype1 | 50 | 7 | 3 | 0 | 4 |
subtype2 | 84 | 20 | 0 | 1 | 13 |
subtype3 | 7 | 0 | 0 | 0 | 0 |
subtype4 | 61 | 4 | 3 | 0 | 9 |
Figure S41. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D5V7.png)
P value = 0.117 (Chi-square test), Q value = 1
Table S47. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 40 | 107 | 77 | 38 |
subtype1 | 7 | 24 | 22 | 10 |
subtype2 | 19 | 39 | 35 | 21 |
subtype3 | 0 | 6 | 1 | 0 |
subtype4 | 14 | 38 | 19 | 7 |
Figure S42. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D5V8.png)
P value = 0.302 (Fisher's exact test), Q value = 1
Table S48. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 266 |
subtype1 | 2 | 63 |
subtype2 | 1 | 118 |
subtype3 | 0 | 7 |
subtype4 | 0 | 78 |
Figure S43. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V9.png)
Table S49. Get Full Table Description of clustering approach #6: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 143 | 25 | 49 | 52 |
P value = 0.302 (logrank test), Q value = 1
Table S50. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 206 | 19 | 0.1 - 105.3 (6.0) |
subtype1 | 100 | 11 | 0.1 - 103.0 (6.6) |
subtype2 | 15 | 1 | 0.1 - 105.3 (8.0) |
subtype3 | 44 | 6 | 0.1 - 100.0 (5.1) |
subtype4 | 47 | 1 | 0.1 - 87.8 (5.4) |
Figure S44. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.169 (ANOVA), Q value = 1
Table S51. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 268 | 66.6 (13.3) |
subtype1 | 142 | 68.0 (13.6) |
subtype2 | 25 | 67.9 (12.0) |
subtype3 | 49 | 63.9 (13.8) |
subtype4 | 52 | 64.6 (12.6) |
Figure S45. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.348 (Fisher's exact test), Q value = 1
Table S52. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 127 | 142 |
subtype1 | 71 | 72 |
subtype2 | 14 | 11 |
subtype3 | 18 | 31 |
subtype4 | 24 | 28 |
Figure S46. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.464 (Fisher's exact test), Q value = 1
Table S53. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 238 | 30 |
subtype1 | 129 | 13 |
subtype2 | 23 | 2 |
subtype3 | 41 | 8 |
subtype4 | 45 | 7 |
Figure S47. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D6V4.png)
P value = 0.28 (Chi-square test), Q value = 1
Table S54. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 5 | 45 | 185 | 31 |
subtype1 | 1 | 23 | 99 | 17 |
subtype2 | 0 | 3 | 20 | 2 |
subtype3 | 3 | 12 | 30 | 4 |
subtype4 | 1 | 7 | 36 | 8 |
Figure S48. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D6V5.png)
P value = 0.123 (Chi-square test), Q value = 1
Table S55. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 160 | 66 | 42 |
subtype1 | 78 | 39 | 26 |
subtype2 | 19 | 5 | 1 |
subtype3 | 36 | 8 | 5 |
subtype4 | 27 | 14 | 10 |
Figure S49. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D6V6.png)
P value = 0.0432 (Chi-square test), Q value = 1
Table S56. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 202 | 31 | 6 | 1 | 26 |
subtype1 | 103 | 22 | 0 | 1 | 17 |
subtype2 | 22 | 0 | 0 | 0 | 1 |
subtype3 | 39 | 2 | 3 | 0 | 5 |
subtype4 | 38 | 7 | 3 | 0 | 3 |
Figure S50. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D6V7.png)
P value = 0.0747 (Chi-square test), Q value = 1
Table S57. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 40 | 107 | 77 | 38 |
subtype1 | 20 | 51 | 45 | 23 |
subtype2 | 2 | 16 | 6 | 0 |
subtype3 | 11 | 23 | 10 | 5 |
subtype4 | 7 | 17 | 16 | 10 |
Figure S51. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D6V8.png)
P value = 0.339 (Fisher's exact test), Q value = 1
Table S58. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 266 |
subtype1 | 1 | 142 |
subtype2 | 0 | 25 |
subtype3 | 0 | 49 |
subtype4 | 2 | 50 |
Figure S52. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V9.png)
Table S59. Get Full Table Description of clustering approach #7: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 68 | 61 | 40 | 23 |
P value = 0.871 (logrank test), Q value = 1
Table S60. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 108 | 16 | 0.9 - 71.7 (12.4) |
subtype1 | 36 | 6 | 0.9 - 71.7 (15.9) |
subtype2 | 41 | 5 | 1.0 - 49.0 (8.1) |
subtype3 | 25 | 5 | 0.9 - 52.0 (12.0) |
subtype4 | 6 | 0 | 1.0 - 4.0 (1.0) |
Figure S53. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.105 (ANOVA), Q value = 1
Table S61. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 192 | 69.7 (12.4) |
subtype1 | 68 | 66.8 (13.0) |
subtype2 | 61 | 72.0 (12.2) |
subtype3 | 40 | 70.6 (10.9) |
subtype4 | 23 | 70.1 (12.9) |
Figure S54. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.00324 (Fisher's exact test), Q value = 0.25
Table S62. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 99 | 93 |
subtype1 | 38 | 30 |
subtype2 | 40 | 21 |
subtype3 | 13 | 27 |
subtype4 | 8 | 15 |
Figure S55. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 2.4e-07 (Fisher's exact test), Q value = 2e-05
Table S63. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 162 | 27 |
subtype1 | 67 | 0 |
subtype2 | 41 | 19 |
subtype3 | 32 | 7 |
subtype4 | 22 | 1 |
Figure S56. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D7V4.png)
P value = 0.7 (Chi-square test), Q value = 1
Table S64. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 6 | 38 | 128 | 18 |
subtype1 | 2 | 15 | 44 | 7 |
subtype2 | 2 | 8 | 41 | 8 |
subtype3 | 1 | 8 | 28 | 3 |
subtype4 | 1 | 7 | 15 | 0 |
Figure S57. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D7V5.png)
P value = 0.0246 (Chi-square test), Q value = 1
Table S65. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 114 | 41 | 36 |
subtype1 | 31 | 20 | 16 |
subtype2 | 34 | 15 | 12 |
subtype3 | 31 | 4 | 5 |
subtype4 | 18 | 2 | 3 |
Figure S58. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D7V6.png)
P value = 0.022 (Chi-square test), Q value = 1
Table S66. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A |
---|---|---|---|
ALL | 159 | 28 | 2 |
subtype1 | 47 | 18 | 1 |
subtype2 | 53 | 7 | 1 |
subtype3 | 37 | 2 | 0 |
subtype4 | 22 | 1 | 0 |
Figure S59. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D7V7.png)
P value = 0.00265 (Chi-square test), Q value = 0.2
Table S67. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 35 | 73 | 50 | 27 |
subtype1 | 11 | 16 | 21 | 17 |
subtype2 | 8 | 25 | 19 | 7 |
subtype3 | 8 | 22 | 6 | 2 |
subtype4 | 8 | 10 | 4 | 1 |
Figure S60. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D7V8.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S68. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 1 | 191 |
subtype1 | 1 | 67 |
subtype2 | 0 | 61 |
subtype3 | 0 | 40 |
subtype4 | 0 | 23 |
Figure S61. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V9.png)
Table S69. Get Full Table Description of clustering approach #8: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 67 | 19 | 106 |
P value = 0.311 (logrank test), Q value = 1
Table S70. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 108 | 16 | 0.9 - 71.7 (12.4) |
subtype1 | 44 | 8 | 1.0 - 52.0 (8.0) |
subtype2 | 11 | 0 | 7.0 - 34.0 (15.2) |
subtype3 | 53 | 8 | 0.9 - 71.7 (10.9) |
Figure S62. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.00338 (ANOVA), Q value = 0.25
Table S71. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 192 | 69.7 (12.4) |
subtype1 | 67 | 73.4 (11.6) |
subtype2 | 19 | 71.1 (12.6) |
subtype3 | 106 | 67.0 (12.4) |
Figure S63. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.456 (Fisher's exact test), Q value = 1
Table S72. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 99 | 93 |
subtype1 | 36 | 31 |
subtype2 | 12 | 7 |
subtype3 | 51 | 55 |
Figure S64. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
P value = 8.97e-08 (Fisher's exact test), Q value = 7.7e-06
Table S73. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 162 | 27 |
subtype1 | 43 | 22 |
subtype2 | 17 | 2 |
subtype3 | 102 | 3 |
Figure S65. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D8V4.png)
P value = 0.638 (Chi-square test), Q value = 1
Table S74. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 6 | 38 | 128 | 18 |
subtype1 | 2 | 14 | 41 | 8 |
subtype2 | 0 | 2 | 14 | 3 |
subtype3 | 4 | 22 | 73 | 7 |
Figure S66. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D8V5.png)
P value = 0.238 (Chi-square test), Q value = 1
Table S75. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 114 | 41 | 36 |
subtype1 | 44 | 15 | 8 |
subtype2 | 10 | 2 | 6 |
subtype3 | 60 | 24 | 22 |
Figure S67. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D8V6.png)
P value = 0.342 (Chi-square test), Q value = 1
Table S76. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A |
---|---|---|---|
ALL | 159 | 28 | 2 |
subtype1 | 59 | 6 | 1 |
subtype2 | 13 | 5 | 0 |
subtype3 | 87 | 17 | 1 |
Figure S68. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D8V7.png)
P value = 0.631 (Chi-square test), Q value = 1
Table S77. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 35 | 73 | 50 | 27 |
subtype1 | 14 | 29 | 16 | 6 |
subtype2 | 2 | 7 | 4 | 4 |
subtype3 | 19 | 37 | 30 | 17 |
Figure S69. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D8V8.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S78. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 1 | 191 |
subtype1 | 0 | 67 |
subtype2 | 0 | 19 |
subtype3 | 1 | 105 |
Figure S70. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D8V9.png)
Table S79. Get Full Table Description of clustering approach #9: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 182 | 163 | 62 |
P value = 0.246 (logrank test), Q value = 1
Table S80. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 309 | 49 | 0.1 - 135.5 (7.5) |
subtype1 | 98 | 17 | 0.9 - 71.7 (7.3) |
subtype2 | 153 | 26 | 0.1 - 135.5 (9.4) |
subtype3 | 58 | 6 | 0.1 - 100.0 (4.3) |
Figure S71. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.000748 (ANOVA), Q value = 0.06
Table S81. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 406 | 67.3 (13.1) |
subtype1 | 182 | 69.8 (12.5) |
subtype2 | 162 | 66.0 (12.6) |
subtype3 | 62 | 63.3 (14.8) |
Figure S72. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D9V2.png)
P value = 0.525 (Fisher's exact test), Q value = 1
Table S82. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 192 | 215 |
subtype1 | 91 | 91 |
subtype2 | 75 | 88 |
subtype3 | 26 | 36 |
Figure S73. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
![](D9V3.png)
P value = 0.0162 (Fisher's exact test), Q value = 1
Table S83. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 349 | 55 |
subtype1 | 154 | 25 |
subtype2 | 148 | 15 |
subtype3 | 47 | 15 |
Figure S74. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D9V4.png)
P value = 0.264 (Chi-square test), Q value = 1
Table S84. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 11 | 69 | 279 | 45 |
subtype1 | 6 | 37 | 120 | 17 |
subtype2 | 5 | 23 | 117 | 17 |
subtype3 | 0 | 9 | 42 | 11 |
Figure S75. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D9V5.png)
P value = 0.515 (Chi-square test), Q value = 1
Table S85. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 241 | 94 | 70 |
subtype1 | 111 | 36 | 34 |
subtype2 | 98 | 40 | 25 |
subtype3 | 32 | 18 | 11 |
Figure S76. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D9V6.png)
P value = 1.25e-05 (Chi-square test), Q value = 0.001
Table S86. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 305 | 50 | 7 | 1 | 36 |
subtype1 | 148 | 28 | 1 | 0 | 1 |
subtype2 | 111 | 15 | 5 | 1 | 28 |
subtype3 | 46 | 7 | 1 | 0 | 7 |
Figure S77. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D9V7.png)
P value = 0.596 (Chi-square test), Q value = 1
Table S87. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 65 | 155 | 113 | 57 |
subtype1 | 34 | 69 | 45 | 27 |
subtype2 | 26 | 63 | 48 | 22 |
subtype3 | 5 | 23 | 20 | 8 |
Figure S78. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D9V8.png)
P value = 0.544 (Fisher's exact test), Q value = 1
Table S88. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 404 |
subtype1 | 1 | 181 |
subtype2 | 1 | 162 |
subtype3 | 1 | 61 |
Figure S79. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D9V9.png)
Table S89. Get Full Table Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 180 | 45 | 182 |
P value = 0.521 (logrank test), Q value = 1
Table S90. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 309 | 49 | 0.1 - 135.5 (7.5) |
subtype1 | 97 | 16 | 0.9 - 71.7 (9.1) |
subtype2 | 42 | 8 | 0.1 - 129.1 (6.7) |
subtype3 | 170 | 25 | 0.1 - 135.5 (7.0) |
Figure S80. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.00978 (ANOVA), Q value = 0.72
Table S91. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 406 | 67.3 (13.1) |
subtype1 | 180 | 69.5 (12.6) |
subtype2 | 45 | 66.4 (11.8) |
subtype3 | 181 | 65.3 (13.6) |
Figure S81. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D10V2.png)
P value = 0.514 (Fisher's exact test), Q value = 1
Table S92. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 192 | 215 |
subtype1 | 90 | 90 |
subtype2 | 22 | 23 |
subtype3 | 80 | 102 |
Figure S82. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
![](D10V3.png)
P value = 0.67 (Fisher's exact test), Q value = 1
Table S93. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
nPatients | COLON ADENOCARCINOMA | COLON MUCINOUS ADENOCARCINOMA |
---|---|---|
ALL | 349 | 55 |
subtype1 | 151 | 26 |
subtype2 | 41 | 4 |
subtype3 | 157 | 25 |
Figure S83. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'
![](D10V4.png)
P value = 0.498 (Chi-square test), Q value = 1
Table S94. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
nPatients | T0+T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 11 | 69 | 279 | 45 |
subtype1 | 6 | 36 | 119 | 17 |
subtype2 | 2 | 5 | 30 | 7 |
subtype3 | 3 | 28 | 130 | 21 |
Figure S84. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'
![](D10V5.png)
P value = 0.95 (Chi-square test), Q value = 1
Table S95. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2 |
---|---|---|---|
ALL | 241 | 94 | 70 |
subtype1 | 107 | 39 | 33 |
subtype2 | 27 | 10 | 8 |
subtype3 | 107 | 45 | 29 |
Figure S85. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'
![](D10V6.png)
P value = 7.64e-06 (Chi-square test), Q value = 0.00063
Table S96. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | M1A | M1B | MX |
---|---|---|---|---|---|
ALL | 305 | 50 | 7 | 1 | 36 |
subtype1 | 145 | 29 | 2 | 0 | 0 |
subtype2 | 30 | 5 | 2 | 0 | 6 |
subtype3 | 130 | 16 | 3 | 1 | 30 |
Figure S86. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'
![](D10V7.png)
P value = 0.448 (Chi-square test), Q value = 1
Table S97. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
nPatients | I | II | III | IV |
---|---|---|---|---|
ALL | 65 | 155 | 113 | 57 |
subtype1 | 34 | 64 | 45 | 28 |
subtype2 | 8 | 18 | 10 | 7 |
subtype3 | 23 | 73 | 58 | 22 |
Figure S87. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'
![](D10V8.png)
P value = 1 (Fisher's exact test), Q value = 1
Table S98. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 3 | 404 |
subtype1 | 1 | 179 |
subtype2 | 0 | 45 |
subtype3 | 2 | 180 |
Figure S88. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D10V9.png)
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Cluster data file = COAD-TP.mergedcluster.txt
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Clinical data file = COAD-TP.clin.merged.picked.txt
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Number of patients = 422
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Number of clustering approaches = 10
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Number of selected clinical features = 9
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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.