This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 8 clinical features across 128 patients, 5 significant findings detected with P value < 0.05 and Q value < 0.25.
-
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
-
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
-
CNMF clustering analysis on sequencing-based mRNA expression data identified 9 subtypes that correlate to 'GENDER'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'AGE' and 'GENDER'.
-
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
-
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 5 significant findings detected.
Clinical Features |
Time to Death |
AGE |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
PATHOLOGY N STAGE |
PATHOLOGY M STAGE |
GENDER |
COMPLETENESS OF RESECTION |
Statistical Tests | logrank test | ANOVA | Chi-square test | Chi-square test | Fisher's exact test | Chi-square test | Fisher's exact test | Chi-square test |
Copy Number Ratio CNMF subtypes |
0.13 (1.00) |
0.569 (1.00) |
0.262 (1.00) |
0.789 (1.00) |
1 (1.00) |
0.492 (1.00) |
0.453 (1.00) |
0.103 (1.00) |
METHLYATION CNMF |
0.34 (1.00) |
0.0289 (1.00) |
0.655 (1.00) |
0.856 (1.00) |
0.775 (1.00) |
0.178 (1.00) |
0.133 (1.00) |
0.794 (1.00) |
RNAseq CNMF subtypes |
0.578 (1.00) |
0.00633 (0.373) |
0.106 (1.00) |
0.0564 (1.00) |
0.677 (1.00) |
0.646 (1.00) |
0.00043 (0.0267) |
0.13 (1.00) |
RNAseq cHierClus subtypes |
0.566 (1.00) |
0.00187 (0.114) |
0.134 (1.00) |
0.00917 (0.532) |
0.862 (1.00) |
0.667 (1.00) |
0.000184 (0.0116) |
0.291 (1.00) |
MIRSEQ CNMF |
0.151 (1.00) |
0.0338 (1.00) |
0.346 (1.00) |
0.394 (1.00) |
0.488 (1.00) |
0.275 (1.00) |
0.475 (1.00) |
0.306 (1.00) |
MIRSEQ CHIERARCHICAL |
0.0634 (1.00) |
0.00284 (0.171) |
0.947 (1.00) |
0.968 (1.00) |
0.717 (1.00) |
0.469 (1.00) |
0.295 (1.00) |
0.0339 (1.00) |
MIRseq Mature CNMF subtypes |
0.808 (1.00) |
0.0505 (1.00) |
0.149 (1.00) |
0.454 (1.00) |
0.116 (1.00) |
0.241 (1.00) |
0.523 (1.00) |
0.522 (1.00) |
MIRseq Mature cHierClus subtypes |
0.12 (1.00) |
8.51e-05 (0.00545) |
0.917 (1.00) |
0.919 (1.00) |
1 (1.00) |
0.293 (1.00) |
0.213 (1.00) |
0.34 (1.00) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 35 | 44 | 44 |
P value = 0.13 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 119 | 54 | 0.0 - 113.0 (14.9) |
subtype1 | 33 | 18 | 0.1 - 90.7 (14.4) |
subtype2 | 43 | 17 | 0.1 - 107.1 (25.3) |
subtype3 | 43 | 19 | 0.0 - 113.0 (13.6) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.569 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 121 | 61.6 (13.7) |
subtype1 | 34 | 63.7 (13.1) |
subtype2 | 44 | 60.5 (13.5) |
subtype3 | 43 | 61.1 (14.6) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.262 (Chi-square test), Q value = 1
Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 48 | 27 | 2 | 27 | 3 | 4 | 1 | 1 | 1 |
subtype1 | 15 | 7 | 0 | 9 | 0 | 0 | 1 | 1 | 0 |
subtype2 | 20 | 7 | 0 | 10 | 1 | 3 | 0 | 0 | 0 |
subtype3 | 13 | 13 | 2 | 8 | 2 | 1 | 0 | 0 | 1 |
Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.789 (Chi-square test), Q value = 1
Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 51 | 30 | 36 | 6 |
subtype1 | 16 | 8 | 10 | 1 |
subtype2 | 21 | 9 | 12 | 2 |
subtype3 | 14 | 13 | 14 | 3 |
Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 1 (Fisher's exact test), Q value = 1
Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 18 | 1 |
subtype2 | 34 | 1 |
subtype3 | 28 | 1 |
Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.492 (Chi-square test), Q value = 1
Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 96 | 2 | 25 |
subtype1 | 24 | 1 | 10 |
subtype2 | 36 | 0 | 8 |
subtype3 | 36 | 1 | 7 |
Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.453 (Fisher's exact test), Q value = 1
Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 77 |
subtype1 | 10 | 25 |
subtype2 | 18 | 26 |
subtype3 | 18 | 26 |
Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

P value = 0.103 (Chi-square test), Q value = 1
Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 99 | 10 | 1 | 8 |
subtype1 | 28 | 5 | 1 | 0 |
subtype2 | 36 | 2 | 0 | 2 |
subtype3 | 35 | 3 | 0 | 6 |
Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S10. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 35 | 37 | 55 |
P value = 0.34 (logrank test), Q value = 1
Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 122 | 54 | 0.0 - 113.0 (14.6) |
subtype1 | 34 | 13 | 0.1 - 107.1 (17.1) |
subtype2 | 36 | 17 | 0.1 - 113.0 (14.3) |
subtype3 | 52 | 24 | 0.0 - 102.7 (14.5) |
Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.0289 (ANOVA), Q value = 1
Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 61.4 (13.9) |
subtype1 | 35 | 57.0 (14.5) |
subtype2 | 37 | 60.5 (15.0) |
subtype3 | 53 | 64.9 (12.0) |
Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.655 (Chi-square test), Q value = 1
Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 48 | 27 | 2 | 30 | 3 | 5 | 1 | 1 | 1 |
subtype1 | 14 | 7 | 0 | 8 | 2 | 3 | 0 | 0 | 0 |
subtype2 | 12 | 7 | 1 | 9 | 1 | 1 | 1 | 0 | 1 |
subtype3 | 22 | 13 | 1 | 13 | 0 | 1 | 0 | 1 | 0 |
Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.856 (Chi-square test), Q value = 1
Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 51 | 30 | 39 | 7 |
subtype1 | 15 | 8 | 10 | 2 |
subtype2 | 12 | 8 | 14 | 3 |
subtype3 | 24 | 14 | 15 | 2 |
Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.775 (Fisher's exact test), Q value = 1
Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 83 | 3 |
subtype1 | 29 | 1 |
subtype2 | 24 | 0 |
subtype3 | 30 | 2 |
Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.178 (Chi-square test), Q value = 1
Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 101 | 2 | 24 |
subtype1 | 30 | 0 | 5 |
subtype2 | 29 | 2 | 6 |
subtype3 | 42 | 0 | 13 |
Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.133 (Fisher's exact test), Q value = 1
Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 49 | 78 |
subtype1 | 15 | 20 |
subtype2 | 18 | 19 |
subtype3 | 16 | 39 |
Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

P value = 0.794 (Chi-square test), Q value = 1
Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 102 | 10 | 1 | 9 |
subtype1 | 29 | 2 | 0 | 2 |
subtype2 | 29 | 4 | 1 | 3 |
subtype3 | 44 | 4 | 0 | 4 |
Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S19. Description of clustering approach #3: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
---|---|---|---|---|---|---|---|---|---|
Number of samples | 17 | 14 | 13 | 23 | 20 | 3 | 15 | 10 | 9 |
P value = 0.578 (logrank test), Q value = 1
Table S20. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 120 | 53 | 0.0 - 113.0 (14.3) |
subtype1 | 16 | 7 | 0.2 - 60.4 (12.1) |
subtype2 | 14 | 7 | 0.1 - 113.0 (12.5) |
subtype3 | 12 | 4 | 0.0 - 80.8 (51.5) |
subtype4 | 23 | 13 | 0.3 - 102.7 (21.8) |
subtype5 | 20 | 8 | 0.4 - 93.7 (13.9) |
subtype6 | 3 | 1 | 1.2 - 16.4 (13.8) |
subtype7 | 15 | 4 | 0.1 - 49.1 (6.0) |
subtype8 | 8 | 2 | 0.3 - 61.5 (9.7) |
subtype9 | 9 | 7 | 0.1 - 79.7 (13.8) |
Figure S17. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.00633 (ANOVA), Q value = 0.37
Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 122 | 61.5 (13.7) |
subtype1 | 17 | 58.0 (16.4) |
subtype2 | 14 | 60.0 (13.0) |
subtype3 | 13 | 61.4 (10.4) |
subtype4 | 22 | 69.0 (8.7) |
subtype5 | 20 | 62.1 (15.4) |
subtype6 | 3 | 69.0 (5.6) |
subtype7 | 15 | 51.4 (13.8) |
subtype8 | 9 | 69.2 (11.4) |
subtype9 | 9 | 58.3 (12.0) |
Figure S18. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.106 (Chi-square test), Q value = 1
Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 47 | 27 | 2 | 29 | 2 | 5 | 1 | 1 | 1 |
subtype1 | 2 | 4 | 0 | 8 | 0 | 1 | 0 | 0 | 0 |
subtype2 | 2 | 4 | 0 | 3 | 0 | 2 | 0 | 0 | 1 |
subtype3 | 7 | 2 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
subtype4 | 14 | 1 | 1 | 5 | 0 | 0 | 0 | 0 | 0 |
subtype5 | 8 | 5 | 0 | 6 | 0 | 0 | 0 | 0 | 0 |
subtype6 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
subtype7 | 4 | 5 | 1 | 3 | 1 | 1 | 0 | 0 | 0 |
subtype8 | 5 | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 0 |
subtype9 | 4 | 1 | 0 | 2 | 1 | 0 | 1 | 0 | 0 |
Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.0564 (Chi-square test), Q value = 1
Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 50 | 30 | 37 | 7 |
subtype1 | 2 | 4 | 9 | 2 |
subtype2 | 2 | 6 | 4 | 2 |
subtype3 | 8 | 2 | 3 | 0 |
subtype4 | 14 | 1 | 6 | 2 |
subtype5 | 8 | 6 | 6 | 0 |
subtype6 | 1 | 2 | 0 | 0 |
subtype7 | 5 | 5 | 5 | 0 |
subtype8 | 6 | 3 | 0 | 1 |
subtype9 | 4 | 1 | 4 | 0 |
Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.677 (Chi-square test), Q value = 1
Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 10 | 0 |
subtype2 | 9 | 1 |
subtype3 | 9 | 0 |
subtype4 | 14 | 0 |
subtype5 | 12 | 0 |
subtype6 | 2 | 0 |
subtype7 | 11 | 1 |
subtype8 | 7 | 1 |
subtype9 | 6 | 0 |
Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.646 (Chi-square test), Q value = 1
Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 97 | 2 | 25 |
subtype1 | 13 | 0 | 4 |
subtype2 | 10 | 1 | 3 |
subtype3 | 10 | 0 | 3 |
subtype4 | 18 | 0 | 5 |
subtype5 | 15 | 0 | 5 |
subtype6 | 3 | 0 | 0 |
subtype7 | 14 | 0 | 1 |
subtype8 | 7 | 0 | 3 |
subtype9 | 7 | 1 | 1 |
Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.00043 (Chi-square test), Q value = 0.027
Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 47 | 77 |
subtype1 | 5 | 12 |
subtype2 | 11 | 3 |
subtype3 | 5 | 8 |
subtype4 | 10 | 13 |
subtype5 | 0 | 20 |
subtype6 | 2 | 1 |
subtype7 | 9 | 6 |
subtype8 | 3 | 7 |
subtype9 | 2 | 7 |
Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

P value = 0.13 (Chi-square test), Q value = 1
Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 100 | 10 | 1 | 8 |
subtype1 | 10 | 4 | 0 | 2 |
subtype2 | 11 | 1 | 0 | 2 |
subtype3 | 13 | 0 | 0 | 0 |
subtype4 | 18 | 2 | 0 | 3 |
subtype5 | 17 | 1 | 0 | 0 |
subtype6 | 2 | 1 | 0 | 0 |
subtype7 | 14 | 0 | 0 | 1 |
subtype8 | 7 | 1 | 0 | 0 |
subtype9 | 8 | 0 | 1 | 0 |
Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S28. Description of clustering approach #4: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 20 | 20 | 29 | 55 |
P value = 0.566 (logrank test), Q value = 1
Table S29. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 120 | 53 | 0.0 - 113.0 (14.3) |
subtype1 | 20 | 6 | 0.4 - 93.7 (13.9) |
subtype2 | 17 | 6 | 0.0 - 80.8 (19.8) |
subtype3 | 29 | 15 | 0.3 - 102.7 (21.8) |
subtype4 | 54 | 26 | 0.1 - 113.0 (12.1) |
Figure S25. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.00187 (ANOVA), Q value = 0.11
Table S30. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 122 | 61.5 (13.7) |
subtype1 | 20 | 62.5 (14.7) |
subtype2 | 19 | 57.2 (13.9) |
subtype3 | 28 | 69.6 (8.6) |
subtype4 | 55 | 58.6 (13.8) |
Figure S26. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.134 (Chi-square test), Q value = 1
Table S31. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 47 | 27 | 2 | 29 | 2 | 5 | 1 | 1 | 1 |
subtype1 | 10 | 5 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
subtype2 | 10 | 5 | 0 | 2 | 0 | 1 | 0 | 1 | 0 |
subtype3 | 17 | 2 | 1 | 6 | 0 | 0 | 0 | 0 | 0 |
subtype4 | 10 | 15 | 1 | 16 | 2 | 4 | 1 | 0 | 1 |
Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.00917 (Chi-square test), Q value = 0.53
Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 50 | 30 | 37 | 7 |
subtype1 | 10 | 5 | 5 | 0 |
subtype2 | 12 | 5 | 2 | 1 |
subtype3 | 17 | 2 | 8 | 2 |
subtype4 | 11 | 18 | 22 | 4 |
Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.862 (Fisher's exact test), Q value = 1
Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 13 | 0 |
subtype2 | 13 | 1 |
subtype3 | 18 | 0 |
subtype4 | 36 | 2 |
Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.667 (Chi-square test), Q value = 1
Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 97 | 2 | 25 |
subtype1 | 16 | 0 | 4 |
subtype2 | 14 | 0 | 6 |
subtype3 | 23 | 0 | 6 |
subtype4 | 44 | 2 | 9 |
Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.000184 (Fisher's exact test), Q value = 0.012
Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 47 | 77 |
subtype1 | 0 | 20 |
subtype2 | 8 | 12 |
subtype3 | 11 | 18 |
subtype4 | 28 | 27 |
Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

P value = 0.291 (Chi-square test), Q value = 1
Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 100 | 10 | 1 | 8 |
subtype1 | 18 | 0 | 0 | 0 |
subtype2 | 18 | 0 | 0 | 0 |
subtype3 | 22 | 4 | 0 | 3 |
subtype4 | 42 | 6 | 1 | 5 |
Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S37. Description of clustering approach #5: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 38 | 32 | 53 |
P value = 0.151 (logrank test), Q value = 1
Table S38. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 118 | 52 | 0.0 - 113.0 (14.6) |
subtype1 | 36 | 13 | 0.0 - 80.8 (20.1) |
subtype2 | 30 | 19 | 0.1 - 102.7 (16.4) |
subtype3 | 52 | 20 | 0.2 - 113.0 (14.2) |
Figure S33. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

P value = 0.0338 (ANOVA), Q value = 1
Table S39. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 121 | 61.4 (13.9) |
subtype1 | 38 | 56.7 (13.9) |
subtype2 | 30 | 64.8 (13.7) |
subtype3 | 53 | 62.9 (13.4) |
Figure S34. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

P value = 0.346 (Chi-square test), Q value = 1
Table S40. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 46 | 26 | 2 | 29 | 3 | 5 | 1 | 1 | 1 |
subtype1 | 12 | 9 | 1 | 10 | 0 | 2 | 1 | 0 | 0 |
subtype2 | 13 | 2 | 1 | 7 | 2 | 2 | 0 | 1 | 0 |
subtype3 | 21 | 15 | 0 | 12 | 1 | 1 | 0 | 0 | 1 |
Figure S35. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.394 (Chi-square test), Q value = 1
Table S41. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 49 | 29 | 38 | 7 |
subtype1 | 13 | 10 | 13 | 2 |
subtype2 | 15 | 3 | 11 | 3 |
subtype3 | 21 | 16 | 14 | 2 |
Figure S36. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.488 (Fisher's exact test), Q value = 1
Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 25 | 0 |
subtype2 | 23 | 2 |
subtype3 | 32 | 1 |
Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.275 (Chi-square test), Q value = 1
Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 98 | 2 | 23 |
subtype1 | 32 | 1 | 5 |
subtype2 | 22 | 0 | 10 |
subtype3 | 44 | 1 | 8 |
Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.475 (Fisher's exact test), Q value = 1
Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 77 |
subtype1 | 15 | 23 |
subtype2 | 9 | 23 |
subtype3 | 22 | 31 |
Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

P value = 0.306 (Chi-square test), Q value = 1
Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 98 | 10 | 1 | 9 |
subtype1 | 32 | 2 | 1 | 3 |
subtype2 | 20 | 5 | 0 | 3 |
subtype3 | 46 | 3 | 0 | 3 |
Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S46. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 53 | 60 | 10 |
P value = 0.0634 (logrank test), Q value = 1
Table S47. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 118 | 52 | 0.0 - 113.0 (14.6) |
subtype1 | 51 | 24 | 0.1 - 80.8 (17.6) |
subtype2 | 57 | 21 | 0.1 - 113.0 (14.9) |
subtype3 | 10 | 7 | 0.0 - 83.6 (3.4) |
Figure S41. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

P value = 0.00284 (ANOVA), Q value = 0.17
Table S48. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 121 | 61.4 (13.9) |
subtype1 | 53 | 57.0 (15.0) |
subtype2 | 58 | 65.8 (11.1) |
subtype3 | 10 | 59.3 (15.9) |
Figure S42. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

P value = 0.947 (Chi-square test), Q value = 1
Table S49. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 46 | 26 | 2 | 29 | 3 | 5 | 1 | 1 | 1 |
subtype1 | 18 | 11 | 1 | 12 | 1 | 3 | 1 | 0 | 1 |
subtype2 | 25 | 13 | 1 | 13 | 2 | 1 | 0 | 1 | 0 |
subtype3 | 3 | 2 | 0 | 4 | 0 | 1 | 0 | 0 | 0 |
Figure S43. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.968 (Chi-square test), Q value = 1
Table S50. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 49 | 29 | 38 | 7 |
subtype1 | 20 | 13 | 17 | 3 |
subtype2 | 26 | 14 | 17 | 3 |
subtype3 | 3 | 2 | 4 | 1 |
Figure S44. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.717 (Fisher's exact test), Q value = 1
Table S51. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 35 | 2 |
subtype2 | 37 | 1 |
subtype3 | 8 | 0 |
Figure S45. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.469 (Chi-square test), Q value = 1
Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 98 | 2 | 23 |
subtype1 | 42 | 2 | 9 |
subtype2 | 49 | 0 | 11 |
subtype3 | 7 | 0 | 3 |
Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.295 (Fisher's exact test), Q value = 1
Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 77 |
subtype1 | 24 | 29 |
subtype2 | 19 | 41 |
subtype3 | 3 | 7 |
Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

P value = 0.0339 (Chi-square test), Q value = 1
Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 98 | 10 | 1 | 9 |
subtype1 | 46 | 2 | 1 | 4 |
subtype2 | 48 | 5 | 0 | 5 |
subtype3 | 4 | 3 | 0 | 0 |
Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S55. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 21 | 64 | 38 |
P value = 0.808 (logrank test), Q value = 1
Table S56. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 118 | 52 | 0.0 - 113.0 (14.6) |
subtype1 | 20 | 7 | 0.1 - 75.7 (9.4) |
subtype2 | 62 | 30 | 0.2 - 113.0 (14.5) |
subtype3 | 36 | 15 | 0.0 - 93.7 (18.0) |
Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 0.0505 (ANOVA), Q value = 1
Table S57. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 121 | 61.4 (13.9) |
subtype1 | 21 | 55.7 (16.9) |
subtype2 | 62 | 64.0 (13.6) |
subtype3 | 38 | 60.4 (11.8) |
Figure S50. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.149 (Chi-square test), Q value = 1
Table S58. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 46 | 26 | 2 | 29 | 3 | 5 | 1 | 1 | 1 |
subtype1 | 4 | 4 | 1 | 6 | 1 | 3 | 0 | 0 | 0 |
subtype2 | 28 | 13 | 1 | 17 | 1 | 0 | 0 | 0 | 0 |
subtype3 | 14 | 9 | 0 | 6 | 1 | 2 | 1 | 1 | 1 |
Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.454 (Chi-square test), Q value = 1
Table S59. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 49 | 29 | 38 | 7 |
subtype1 | 6 | 4 | 8 | 3 |
subtype2 | 28 | 14 | 20 | 2 |
subtype3 | 15 | 11 | 10 | 2 |
Figure S52. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 0.116 (Fisher's exact test), Q value = 1
Table S60. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 15 | 1 |
subtype2 | 42 | 0 |
subtype3 | 23 | 2 |
Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.241 (Chi-square test), Q value = 1
Table S61. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 98 | 2 | 23 |
subtype1 | 17 | 0 | 4 |
subtype2 | 50 | 0 | 14 |
subtype3 | 31 | 2 | 5 |
Figure S54. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.523 (Fisher's exact test), Q value = 1
Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 77 |
subtype1 | 9 | 12 |
subtype2 | 21 | 43 |
subtype3 | 16 | 22 |
Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

P value = 0.522 (Chi-square test), Q value = 1
Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 98 | 10 | 1 | 9 |
subtype1 | 15 | 2 | 0 | 2 |
subtype2 | 49 | 7 | 0 | 5 |
subtype3 | 34 | 1 | 1 | 2 |
Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Table S64. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 9 | 52 | 62 |
P value = 0.12 (logrank test), Q value = 1
Table S65. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 118 | 52 | 0.0 - 113.0 (14.6) |
subtype1 | 9 | 6 | 0.0 - 83.6 (3.3) |
subtype2 | 50 | 20 | 0.1 - 80.8 (16.2) |
subtype3 | 59 | 26 | 0.2 - 113.0 (16.4) |
Figure S57. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

P value = 8.51e-05 (ANOVA), Q value = 0.0054
Table S66. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 121 | 61.4 (13.9) |
subtype1 | 9 | 57.7 (16.0) |
subtype2 | 52 | 55.9 (14.1) |
subtype3 | 60 | 66.8 (11.4) |
Figure S58. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

P value = 0.917 (Chi-square test), Q value = 1
Table S67. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IIIA | STAGE IIIB | STAGE IIIC | STAGE IV | STAGE IVA | STAGE IVB |
---|---|---|---|---|---|---|---|---|---|
ALL | 46 | 26 | 2 | 29 | 3 | 5 | 1 | 1 | 1 |
subtype1 | 3 | 2 | 0 | 3 | 0 | 1 | 0 | 0 | 0 |
subtype2 | 16 | 11 | 1 | 14 | 1 | 3 | 1 | 0 | 1 |
subtype3 | 27 | 13 | 1 | 12 | 2 | 1 | 0 | 1 | 0 |
Figure S59. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

P value = 0.919 (Chi-square test), Q value = 1
Table S68. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 49 | 29 | 38 | 7 |
subtype1 | 3 | 2 | 3 | 1 |
subtype2 | 18 | 13 | 18 | 3 |
subtype3 | 28 | 14 | 17 | 3 |
Figure S60. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

P value = 1 (Fisher's exact test), Q value = 1
Table S69. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | 0 | 1 |
---|---|---|
ALL | 80 | 3 |
subtype1 | 7 | 0 |
subtype2 | 37 | 2 |
subtype3 | 36 | 1 |
Figure S61. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

P value = 0.293 (Chi-square test), Q value = 1
Table S70. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 98 | 2 | 23 |
subtype1 | 6 | 0 | 3 |
subtype2 | 43 | 2 | 7 |
subtype3 | 49 | 0 | 13 |
Figure S62. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

P value = 0.213 (Fisher's exact test), Q value = 1
Table S71. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 77 |
subtype1 | 2 | 7 |
subtype2 | 24 | 28 |
subtype3 | 20 | 42 |
Figure S63. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

P value = 0.34 (Chi-square test), Q value = 1
Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'
nPatients | R0 | R1 | R2 | RX |
---|---|---|---|---|
ALL | 98 | 10 | 1 | 9 |
subtype1 | 4 | 2 | 0 | 0 |
subtype2 | 44 | 3 | 1 | 4 |
subtype3 | 50 | 5 | 0 | 5 |
Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

-
Cluster data file = LIHC-TP.mergedcluster.txt
-
Clinical data file = LIHC-TP.merged_data.txt
-
Number of patients = 128
-
Number of clustering approaches = 8
-
Number of selected clinical features = 8
-
Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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 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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.