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 11 clinical features across 218 patients, 10 significant findings detected with P value < 0.05 and Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
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4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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CNMF clustering analysis on RPPA data identified 5 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 3 subtypes that correlate to 'RACE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', and 'RACE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
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7 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE' and 'PATHOLOGY.T.STAGE'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'RACE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 11 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 10 significant findings detected.
Clinical Features |
Statistical Tests |
Copy Number Ratio CNMF subtypes |
METHLYATION CNMF |
RPPA CNMF subtypes |
RPPA cHierClus subtypes |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRSEQ CNMF |
MIRSEQ CHIERARCHICAL |
MIRseq Mature CNMF subtypes |
MIRseq Mature cHierClus subtypes |
Time to Death | logrank test |
0.991 (1.00) |
0.878 (1.00) |
0.0979 (1.00) |
0.365 (1.00) |
0.422 (1.00) |
0.0783 (1.00) |
0.295 (1.00) |
0.72 (1.00) |
0.357 (1.00) |
0.806 (1.00) |
AGE | Kruskal-Wallis (anova) |
0.339 (1.00) |
0.0274 (1.00) |
0.457 (1.00) |
0.322 (1.00) |
0.0505 (1.00) |
0.00482 (0.457) |
0.0285 (1.00) |
0.0742 (1.00) |
0.00462 (0.443) |
0.00771 (0.709) |
NEOPLASM DISEASESTAGE | Fisher's exact test |
0.115 (1.00) |
0.00295 (0.295) |
0.124 (1.00) |
0.0583 (1.00) |
0.0107 (0.934) |
1e-05 (0.0011) |
0.0008 (0.0848) |
0.00029 (0.031) |
0.00084 (0.0874) |
0.0173 (1.00) |
PATHOLOGY T STAGE | Fisher's exact test |
0.816 (1.00) |
0.2 (1.00) |
0.687 (1.00) |
0.357 (1.00) |
0.0103 (0.911) |
0.0008 (0.0848) |
0.0248 (1.00) |
0.00215 (0.217) |
0.00514 (0.483) |
0.027 (1.00) |
PATHOLOGY N STAGE | Fisher's exact test |
0.0448 (1.00) |
0.0646 (1.00) |
0.427 (1.00) |
0.616 (1.00) |
0.598 (1.00) |
0.00115 (0.117) |
0.0112 (0.961) |
0.00383 (0.372) |
0.128 (1.00) |
0.336 (1.00) |
PATHOLOGY M STAGE | Fisher's exact test |
0.798 (1.00) |
0.0344 (1.00) |
0.0306 (1.00) |
0.171 (1.00) |
0.0184 (1.00) |
0.0528 (1.00) |
0.53 (1.00) |
0.202 (1.00) |
0.137 (1.00) |
0.533 (1.00) |
GENDER | Fisher's exact test |
0.956 (1.00) |
0.00907 (0.816) |
0.516 (1.00) |
0.171 (1.00) |
0.102 (1.00) |
0.00841 (0.765) |
0.00349 (0.342) |
0.0676 (1.00) |
0.0601 (1.00) |
0.13 (1.00) |
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) |
0.0228 (1.00) |
0.0278 (1.00) |
0.369 (1.00) |
0.393 (1.00) |
0.602 (1.00) |
0.691 (1.00) |
0.623 (1.00) |
0.293 (1.00) |
0.9 (1.00) |
0.552 (1.00) |
NUMBERPACKYEARSSMOKED | Kruskal-Wallis (anova) |
0.234 (1.00) |
0.332 (1.00) |
0.187 (1.00) |
0.156 (1.00) |
0.149 (1.00) |
0.237 (1.00) |
0.184 (1.00) |
0.783 (1.00) |
0.424 (1.00) |
0.814 (1.00) |
NUMBER OF LYMPH NODES | Kruskal-Wallis (anova) |
0.112 (1.00) |
0.0348 (1.00) |
0.0993 (1.00) |
0.242 (1.00) |
0.585 (1.00) |
0.00346 (0.342) |
0.00988 (0.879) |
0.0196 (1.00) |
0.0178 (1.00) |
0.304 (1.00) |
RACE | Fisher's exact test |
0.869 (1.00) |
0.0292 (1.00) |
0.0658 (1.00) |
0.00614 (0.571) |
4e-05 (0.00432) |
2e-05 (0.00218) |
0.232 (1.00) |
0.0226 (1.00) |
0.0144 (1.00) |
0.00084 (0.0874) |
Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 54 | 77 | 83 |
P value = 0.991 (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 | 208 | 57 | 0.1 - 140.8 (8.6) |
subtype1 | 54 | 14 | 0.4 - 130.9 (7.7) |
subtype2 | 75 | 19 | 0.1 - 140.8 (7.8) |
subtype3 | 79 | 24 | 0.1 - 88.9 (11.3) |
Figure S1. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.339 (Kruskal-Wallis (anova)), Q value = 1
Table S3. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 213 | 67.6 (10.7) |
subtype1 | 53 | 65.9 (9.9) |
subtype2 | 77 | 67.9 (11.1) |
subtype3 | 83 | 68.4 (10.9) |
Figure S2. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.115 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 68 | 72 | 67 |
subtype1 | 0 | 1 | 16 | 12 | 23 |
subtype2 | 0 | 1 | 29 | 29 | 18 |
subtype3 | 1 | 0 | 23 | 31 | 26 |
Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D1V3.png)
P value = 0.816 (Fisher's exact test), Q value = 1
Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 101 | 32 |
subtype1 | 15 | 25 | 7 |
subtype2 | 26 | 34 | 11 |
subtype3 | 21 | 42 | 14 |
Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D1V4.png)
P value = 0.0448 (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 | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 130 | 22 | 38 | 7 |
subtype1 | 21 | 6 | 15 | 3 |
subtype2 | 57 | 7 | 9 | 2 |
subtype3 | 52 | 9 | 14 | 2 |
Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D1V5.png)
P value = 0.798 (Fisher's exact 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 | 106 | 5 | 102 |
subtype1 | 26 | 2 | 25 |
subtype2 | 36 | 1 | 40 |
subtype3 | 44 | 2 | 37 |
Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D1V6.png)
P value = 0.956 (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 | 50 | 164 |
subtype1 | 12 | 42 |
subtype2 | 19 | 58 |
subtype3 | 19 | 64 |
Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D1V7.png)
P value = 0.0228 (Kruskal-Wallis (anova)), Q value = 1
Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 60 | 79.7 (15.3) |
subtype1 | 18 | 86.1 (12.4) |
subtype2 | 18 | 76.7 (11.9) |
subtype3 | 24 | 77.1 (18.3) |
Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D1V8.png)
P value = 0.234 (Kruskal-Wallis (anova)), Q value = 1
Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 37.8 (28.1) |
subtype1 | 32 | 33.1 (28.1) |
subtype2 | 43 | 40.9 (31.6) |
subtype3 | 50 | 38.2 (24.8) |
Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D1V9.png)
P value = 0.112 (Kruskal-Wallis (anova)), Q value = 1
Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 163 | 1.5 (3.2) |
subtype1 | 41 | 1.6 (2.8) |
subtype2 | 60 | 1.3 (3.2) |
subtype3 | 62 | 1.7 (3.5) |
Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D1V10.png)
P value = 0.869 (Fisher's exact test), Q value = 1
Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 11 | 170 |
subtype1 | 4 | 3 | 43 |
subtype2 | 8 | 5 | 62 |
subtype3 | 10 | 3 | 65 |
Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D1V11.png)
Table S13. Description of clustering approach #2: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 77 | 48 | 49 | 40 |
P value = 0.878 (logrank test), Q value = 1
Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 208 | 58 | 0.1 - 140.8 (8.2) |
subtype1 | 76 | 23 | 0.4 - 140.8 (9.3) |
subtype2 | 48 | 13 | 0.5 - 130.9 (9.8) |
subtype3 | 46 | 12 | 0.1 - 50.3 (5.8) |
subtype4 | 38 | 10 | 0.1 - 76.6 (7.0) |
Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.0274 (Kruskal-Wallis (anova)), Q value = 1
Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 213 | 67.6 (10.6) |
subtype1 | 77 | 70.2 (10.0) |
subtype2 | 47 | 68.2 (9.4) |
subtype3 | 49 | 64.4 (12.1) |
subtype4 | 40 | 66.0 (10.3) |
Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.00295 (Fisher's exact test), Q value = 0.29
Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 70 | 69 | 68 |
subtype1 | 0 | 0 | 18 | 38 | 20 |
subtype2 | 1 | 2 | 14 | 11 | 19 |
subtype3 | 0 | 0 | 21 | 13 | 14 |
subtype4 | 0 | 0 | 17 | 7 | 15 |
Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D2V3.png)
P value = 0.2 (Fisher's exact test), Q value = 1
Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 63 | 98 | 33 |
subtype1 | 16 | 40 | 16 |
subtype2 | 14 | 22 | 5 |
subtype3 | 20 | 22 | 5 |
subtype4 | 13 | 14 | 7 |
Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D2V4.png)
P value = 0.0646 (Fisher's exact test), Q value = 1
Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 129 | 22 | 39 | 7 |
subtype1 | 54 | 8 | 9 | 4 |
subtype2 | 22 | 6 | 12 | 1 |
subtype3 | 33 | 7 | 6 | 1 |
subtype4 | 20 | 1 | 12 | 1 |
Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D2V5.png)
P value = 0.0344 (Fisher's exact test), Q value = 1
Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 108 | 5 | 100 |
subtype1 | 31 | 1 | 45 |
subtype2 | 26 | 2 | 19 |
subtype3 | 25 | 0 | 24 |
subtype4 | 26 | 2 | 12 |
Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D2V6.png)
P value = 0.00907 (Fisher's exact test), Q value = 0.82
Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 51 | 163 |
subtype1 | 26 | 51 |
subtype2 | 14 | 34 |
subtype3 | 6 | 43 |
subtype4 | 5 | 35 |
Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'
![](D2V7.png)
P value = 0.0278 (Kruskal-Wallis (anova)), Q value = 1
Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 57 | 79.3 (15.6) |
subtype1 | 13 | 83.1 (14.9) |
subtype2 | 12 | 86.7 (12.3) |
subtype3 | 20 | 75.5 (17.9) |
subtype4 | 12 | 74.2 (12.4) |
Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D2V8.png)
P value = 0.332 (Kruskal-Wallis (anova)), Q value = 1
Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 123 | 38.9 (28.1) |
subtype1 | 46 | 43.6 (31.6) |
subtype2 | 26 | 37.8 (28.6) |
subtype3 | 31 | 38.9 (26.9) |
subtype4 | 20 | 29.4 (18.1) |
Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D2V9.png)
P value = 0.0348 (Kruskal-Wallis (anova)), Q value = 1
Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 160 | 1.6 (3.2) |
subtype1 | 66 | 0.9 (2.0) |
subtype2 | 34 | 1.8 (3.1) |
subtype3 | 37 | 1.7 (4.2) |
subtype4 | 23 | 2.9 (4.1) |
Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D2V10.png)
P value = 0.0292 (Fisher's exact test), Q value = 1
Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 167 |
subtype1 | 2 | 6 | 64 |
subtype2 | 4 | 1 | 39 |
subtype3 | 8 | 3 | 37 |
subtype4 | 8 | 2 | 27 |
Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'
![](D2V11.png)
Table S25. Description of clustering approach #3: 'RPPA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 37 | 29 | 36 | 5 | 20 |
P value = 0.0979 (logrank test), Q value = 1
Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 122 | 47 | 0.1 - 140.8 (9.3) |
subtype1 | 33 | 6 | 0.1 - 130.9 (7.8) |
subtype2 | 28 | 11 | 1.8 - 140.8 (7.8) |
subtype3 | 36 | 17 | 0.4 - 123.8 (12.5) |
subtype4 | 5 | 3 | 3.1 - 11.9 (5.7) |
subtype5 | 20 | 10 | 2.1 - 61.9 (10.1) |
Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.457 (Kruskal-Wallis (anova)), Q value = 1
Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 126 | 67.2 (10.5) |
subtype1 | 36 | 65.3 (10.7) |
subtype2 | 29 | 69.4 (9.5) |
subtype3 | 36 | 68.7 (10.6) |
subtype4 | 5 | 64.4 (11.4) |
subtype5 | 20 | 65.0 (11.0) |
Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.124 (Fisher's exact test), Q value = 1
Table S28. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 1 | 36 | 42 | 44 |
subtype1 | 1 | 12 | 9 | 12 |
subtype2 | 0 | 5 | 8 | 16 |
subtype3 | 0 | 8 | 16 | 11 |
subtype4 | 0 | 1 | 3 | 1 |
subtype5 | 0 | 10 | 6 | 4 |
Figure S25. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D3V3.png)
P value = 0.687 (Fisher's exact test), Q value = 1
Table S29. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 27 | 63 | 19 |
subtype1 | 10 | 15 | 4 |
subtype2 | 6 | 15 | 8 |
subtype3 | 8 | 21 | 5 |
subtype4 | 0 | 3 | 1 |
subtype5 | 3 | 9 | 1 |
Figure S26. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D3V4.png)
P value = 0.427 (Fisher's exact test), Q value = 1
Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 71 | 13 | 24 | 6 |
subtype1 | 20 | 1 | 9 | 2 |
subtype2 | 13 | 4 | 9 | 2 |
subtype3 | 23 | 5 | 4 | 2 |
subtype4 | 4 | 1 | 0 | 0 |
subtype5 | 11 | 2 | 2 | 0 |
Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D3V5.png)
P value = 0.0306 (Fisher's exact test), Q value = 1
Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 73 | 5 | 48 |
subtype1 | 25 | 2 | 9 |
subtype2 | 10 | 2 | 17 |
subtype3 | 25 | 0 | 11 |
subtype4 | 4 | 0 | 1 |
subtype5 | 9 | 1 | 10 |
Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D3V6.png)
P value = 0.516 (Fisher's exact test), Q value = 1
Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 33 | 94 |
subtype1 | 7 | 30 |
subtype2 | 10 | 19 |
subtype3 | 8 | 28 |
subtype4 | 2 | 3 |
subtype5 | 6 | 14 |
Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D3V7.png)
P value = 0.369 (Kruskal-Wallis (anova)), Q value = 1
Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 35 | 79.1 (15.8) |
subtype1 | 12 | 75.0 (17.8) |
subtype2 | 2 | 80.0 (14.1) |
subtype3 | 10 | 77.0 (20.0) |
subtype4 | 1 | 90.0 (NA) |
subtype5 | 10 | 85.0 (7.1) |
Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V8.png)
P value = 0.187 (Kruskal-Wallis (anova)), Q value = 1
Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 36.8 (23.8) |
subtype1 | 24 | 34.7 (28.9) |
subtype2 | 18 | 44.4 (22.5) |
subtype3 | 22 | 36.8 (20.3) |
subtype4 | 2 | 35.5 (6.4) |
subtype5 | 13 | 30.1 (22.2) |
Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D3V9.png)
P value = 0.0993 (Kruskal-Wallis (anova)), Q value = 1
Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 90 | 1.8 (3.8) |
subtype1 | 22 | 1.8 (3.1) |
subtype2 | 26 | 3.2 (5.4) |
subtype3 | 27 | 0.9 (2.4) |
subtype4 | 4 | 0.2 (0.5) |
subtype5 | 11 | 1.4 (3.6) |
Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D3V10.png)
P value = 0.0658 (Fisher's exact test), Q value = 1
Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 8 | 9 | 100 |
subtype1 | 7 | 1 | 27 |
subtype2 | 0 | 2 | 24 |
subtype3 | 1 | 3 | 29 |
subtype4 | 0 | 0 | 5 |
subtype5 | 0 | 3 | 15 |
Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D3V11.png)
Table S37. Description of clustering approach #4: 'RPPA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 43 | 38 | 27 | 19 |
P value = 0.365 (logrank test), Q value = 1
Table S38. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 122 | 47 | 0.1 - 140.8 (9.3) |
subtype1 | 39 | 10 | 0.1 - 112.4 (10.6) |
subtype2 | 37 | 14 | 1.8 - 140.8 (8.3) |
subtype3 | 27 | 15 | 0.4 - 130.9 (14.9) |
subtype4 | 19 | 8 | 2.1 - 61.9 (8.3) |
Figure S34. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.322 (Kruskal-Wallis (anova)), Q value = 1
Table S39. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 126 | 67.2 (10.5) |
subtype1 | 42 | 65.4 (10.8) |
subtype2 | 38 | 68.7 (10.4) |
subtype3 | 27 | 69.0 (10.9) |
subtype4 | 19 | 65.3 (9.0) |
Figure S35. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0583 (Fisher's exact test), Q value = 1
Table S40. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|
ALL | 1 | 36 | 42 | 44 |
subtype1 | 1 | 15 | 12 | 12 |
subtype2 | 0 | 7 | 11 | 20 |
subtype3 | 0 | 5 | 14 | 7 |
subtype4 | 0 | 9 | 5 | 5 |
Figure S36. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D4V3.png)
P value = 0.357 (Fisher's exact test), Q value = 1
Table S41. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 27 | 63 | 19 |
subtype1 | 14 | 16 | 6 |
subtype2 | 7 | 22 | 8 |
subtype3 | 4 | 18 | 4 |
subtype4 | 2 | 7 | 1 |
Figure S37. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D4V4.png)
P value = 0.616 (Fisher's exact test), Q value = 1
Table S42. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 71 | 13 | 24 | 6 |
subtype1 | 24 | 4 | 8 | 1 |
subtype2 | 19 | 6 | 10 | 2 |
subtype3 | 19 | 3 | 4 | 1 |
subtype4 | 9 | 0 | 2 | 2 |
Figure S38. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D4V5.png)
P value = 0.171 (Fisher's exact test), Q value = 1
Table S43. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 73 | 5 | 48 |
subtype1 | 30 | 1 | 11 |
subtype2 | 18 | 2 | 18 |
subtype3 | 16 | 0 | 11 |
subtype4 | 9 | 2 | 8 |
Figure S39. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D4V6.png)
P value = 0.171 (Fisher's exact test), Q value = 1
Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 33 | 94 |
subtype1 | 7 | 36 |
subtype2 | 14 | 24 |
subtype3 | 6 | 21 |
subtype4 | 6 | 13 |
Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D4V7.png)
P value = 0.393 (Kruskal-Wallis (anova)), Q value = 1
Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 35 | 79.1 (15.8) |
subtype1 | 12 | 75.8 (17.8) |
subtype2 | 5 | 74.0 (20.7) |
subtype3 | 8 | 78.8 (17.3) |
subtype4 | 10 | 86.0 (7.0) |
Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D4V8.png)
P value = 0.156 (Kruskal-Wallis (anova)), Q value = 1
Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 36.8 (23.8) |
subtype1 | 27 | 34.8 (26.9) |
subtype2 | 24 | 43.2 (24.2) |
subtype3 | 16 | 35.9 (18.1) |
subtype4 | 12 | 29.5 (21.8) |
Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D4V9.png)
P value = 0.242 (Kruskal-Wallis (anova)), Q value = 1
Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 90 | 1.8 (3.8) |
subtype1 | 27 | 1.6 (2.9) |
subtype2 | 33 | 2.6 (4.9) |
subtype3 | 21 | 0.5 (0.9) |
subtype4 | 9 | 2.9 (5.2) |
Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D4V10.png)
P value = 0.00614 (Fisher's exact test), Q value = 0.57
Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 8 | 9 | 100 |
subtype1 | 7 | 1 | 32 |
subtype2 | 0 | 1 | 32 |
subtype3 | 1 | 4 | 20 |
subtype4 | 0 | 3 | 16 |
Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D4V11.png)
Table S49. Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 91 | 60 | 65 |
P value = 0.422 (logrank test), Q value = 1
Table S50. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 86 | 17 | 0.1 - 112.4 (6.3) |
subtype2 | 59 | 21 | 1.2 - 140.8 (11.0) |
subtype3 | 65 | 21 | 0.4 - 97.5 (9.3) |
Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.0505 (Kruskal-Wallis (anova)), Q value = 1
Table S51. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.7 (10.7) |
subtype1 | 90 | 65.6 (11.3) |
subtype2 | 60 | 69.8 (8.8) |
subtype3 | 65 | 68.5 (11.0) |
Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0107 (Fisher's exact test), Q value = 0.93
Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 73 | 67 |
subtype1 | 1 | 2 | 39 | 23 | 23 |
subtype2 | 0 | 0 | 11 | 24 | 25 |
subtype3 | 0 | 0 | 19 | 26 | 19 |
Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D5V3.png)
P value = 0.0103 (Fisher's exact test), Q value = 0.91
Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 101 | 33 |
subtype1 | 35 | 33 | 9 |
subtype2 | 12 | 38 | 10 |
subtype3 | 15 | 30 | 14 |
Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D5V4.png)
P value = 0.598 (Fisher's exact test), Q value = 1
Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 132 | 21 | 39 | 7 |
subtype1 | 55 | 6 | 16 | 2 |
subtype2 | 36 | 7 | 15 | 2 |
subtype3 | 41 | 8 | 8 | 3 |
Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D5V5.png)
P value = 0.0184 (Fisher's exact test), Q value = 1
Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 55 | 2 | 33 |
subtype2 | 23 | 3 | 34 |
subtype3 | 31 | 0 | 34 |
Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D5V6.png)
P value = 0.102 (Fisher's exact test), Q value = 1
Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 51 | 165 |
subtype1 | 16 | 75 |
subtype2 | 14 | 46 |
subtype3 | 21 | 44 |
Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D5V7.png)
P value = 0.602 (Kruskal-Wallis (anova)), Q value = 1
Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 35 | 78.6 (15.2) |
subtype2 | 8 | 83.8 (13.0) |
subtype3 | 18 | 80.6 (16.6) |
Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V8.png)
P value = 0.149 (Kruskal-Wallis (anova)), Q value = 1
Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.3 (28.2) |
subtype1 | 51 | 35.4 (28.8) |
subtype2 | 38 | 39.3 (33.1) |
subtype3 | 36 | 41.4 (21.3) |
Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D5V9.png)
P value = 0.585 (Kruskal-Wallis (anova)), Q value = 1
Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 162 | 1.5 (3.2) |
subtype1 | 55 | 1.5 (2.7) |
subtype2 | 55 | 1.8 (3.4) |
subtype3 | 52 | 1.3 (3.6) |
Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D5V10.png)
P value = 4e-05 (Fisher's exact test), Q value = 0.0043
Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 20 | 2 | 65 |
subtype2 | 1 | 3 | 52 |
subtype3 | 1 | 7 | 52 |
Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D5V11.png)
Table S61. Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 71 | 38 | 55 | 52 |
P value = 0.0783 (logrank test), Q value = 1
Table S62. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 66 | 11 | 0.1 - 130.9 (6.3) |
subtype2 | 37 | 18 | 1.2 - 140.8 (8.2) |
subtype3 | 55 | 18 | 0.5 - 112.4 (12.8) |
subtype4 | 52 | 12 | 0.4 - 97.5 (9.1) |
Figure S56. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.00482 (Kruskal-Wallis (anova)), Q value = 0.46
Table S63. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.7 (10.7) |
subtype1 | 70 | 64.1 (10.9) |
subtype2 | 38 | 69.4 (9.8) |
subtype3 | 55 | 70.7 (9.1) |
subtype4 | 52 | 68.0 (11.5) |
Figure S57. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 1e-05 (Fisher's exact test), Q value = 0.0011
Table S64. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 73 | 67 |
subtype1 | 1 | 2 | 37 | 17 | 12 |
subtype2 | 0 | 0 | 9 | 16 | 13 |
subtype3 | 0 | 0 | 9 | 15 | 30 |
subtype4 | 0 | 0 | 14 | 25 | 12 |
Figure S58. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D6V3.png)
P value = 8e-04 (Fisher's exact test), Q value = 0.085
Table S65. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 101 | 33 |
subtype1 | 33 | 21 | 5 |
subtype2 | 7 | 20 | 8 |
subtype3 | 10 | 33 | 10 |
subtype4 | 12 | 27 | 10 |
Figure S59. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D6V4.png)
P value = 0.00115 (Fisher's exact test), Q value = 0.12
Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 132 | 21 | 39 | 7 |
subtype1 | 46 | 3 | 8 | 2 |
subtype2 | 23 | 4 | 6 | 3 |
subtype3 | 26 | 8 | 21 | 0 |
subtype4 | 37 | 6 | 4 | 2 |
Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D6V5.png)
P value = 0.0528 (Fisher's exact test), Q value = 1
Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 44 | 2 | 24 |
subtype2 | 13 | 1 | 24 |
subtype3 | 27 | 2 | 26 |
subtype4 | 25 | 0 | 27 |
Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D6V6.png)
P value = 0.00841 (Fisher's exact test), Q value = 0.77
Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 51 | 165 |
subtype1 | 8 | 63 |
subtype2 | 9 | 29 |
subtype3 | 15 | 40 |
subtype4 | 19 | 33 |
Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D6V7.png)
P value = 0.691 (Kruskal-Wallis (anova)), Q value = 1
Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 31 | 78.4 (14.9) |
subtype2 | 9 | 84.4 (11.3) |
subtype3 | 8 | 78.8 (16.4) |
subtype4 | 13 | 80.8 (18.5) |
Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D6V8.png)
P value = 0.237 (Kruskal-Wallis (anova)), Q value = 1
Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.3 (28.2) |
subtype1 | 43 | 32.7 (25.7) |
subtype2 | 27 | 37.8 (25.0) |
subtype3 | 29 | 45.0 (38.7) |
subtype4 | 26 | 40.4 (20.2) |
Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D6V9.png)
P value = 0.00346 (Kruskal-Wallis (anova)), Q value = 0.34
Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 162 | 1.5 (3.2) |
subtype1 | 39 | 1.1 (2.6) |
subtype2 | 32 | 1.7 (4.3) |
subtype3 | 48 | 2.5 (3.6) |
subtype4 | 43 | 0.8 (2.1) |
Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D6V10.png)
P value = 2e-05 (Fisher's exact test), Q value = 0.0022
Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 19 | 2 | 48 |
subtype2 | 1 | 3 | 31 |
subtype3 | 1 | 1 | 47 |
subtype4 | 1 | 6 | 43 |
Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D6V11.png)
Table S73. Description of clustering approach #7: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 57 | 93 | 66 |
P value = 0.295 (logrank test), Q value = 1
Table S74. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 54 | 14 | 0.1 - 130.9 (6.4) |
subtype2 | 90 | 21 | 0.1 - 97.5 (9.8) |
subtype3 | 66 | 24 | 0.4 - 140.8 (10.8) |
Figure S67. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.0285 (Kruskal-Wallis (anova)), Q value = 1
Table S75. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.6 (10.7) |
subtype1 | 56 | 68.7 (11.1) |
subtype2 | 93 | 65.4 (11.1) |
subtype3 | 66 | 69.6 (9.2) |
Figure S68. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 8e-04 (Fisher's exact test), Q value = 0.085
Table S76. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 72 | 68 |
subtype1 | 0 | 1 | 12 | 18 | 25 |
subtype2 | 1 | 1 | 43 | 30 | 17 |
subtype3 | 0 | 0 | 14 | 24 | 26 |
Figure S69. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D7V3.png)
P value = 0.0248 (Fisher's exact test), Q value = 1
Table S77. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 102 | 32 |
subtype1 | 15 | 27 | 11 |
subtype2 | 35 | 35 | 10 |
subtype3 | 12 | 40 | 11 |
Figure S70. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D7V4.png)
P value = 0.0112 (Fisher's exact test), Q value = 0.96
Table S78. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 131 | 22 | 39 | 7 |
subtype1 | 29 | 10 | 12 | 3 |
subtype2 | 63 | 7 | 8 | 3 |
subtype3 | 39 | 5 | 19 | 1 |
Figure S71. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D7V5.png)
P value = 0.53 (Fisher's exact test), Q value = 1
Table S79. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 27 | 1 | 28 |
subtype2 | 53 | 2 | 38 |
subtype3 | 29 | 2 | 35 |
Figure S72. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D7V6.png)
P value = 0.00349 (Fisher's exact test), Q value = 0.34
Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 166 |
subtype1 | 8 | 49 |
subtype2 | 17 | 76 |
subtype3 | 25 | 41 |
Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'
![](D7V7.png)
P value = 0.623 (Kruskal-Wallis (anova)), Q value = 1
Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 10 | 84.0 (8.4) |
subtype2 | 43 | 79.8 (15.7) |
subtype3 | 8 | 75.0 (19.3) |
Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V8.png)
P value = 0.184 (Kruskal-Wallis (anova)), Q value = 1
Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.2 (28.3) |
subtype1 | 31 | 37.5 (30.6) |
subtype2 | 58 | 33.0 (19.9) |
subtype3 | 36 | 47.2 (35.4) |
Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D7V9.png)
P value = 0.00988 (Kruskal-Wallis (anova)), Q value = 0.88
Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 163 | 1.5 (3.2) |
subtype1 | 47 | 2.1 (4.1) |
subtype2 | 58 | 0.7 (1.7) |
subtype3 | 58 | 1.9 (3.5) |
Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D7V10.png)
P value = 0.232 (Fisher's exact test), Q value = 1
Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 6 | 2 | 44 |
subtype2 | 13 | 4 | 71 |
subtype3 | 3 | 6 | 54 |
Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'
![](D7V11.png)
Table S85. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Number of samples | 39 | 23 | 26 | 26 | 36 | 29 | 37 |
P value = 0.72 (logrank test), Q value = 1
Table S86. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 36 | 10 | 0.1 - 130.9 (5.8) |
subtype2 | 23 | 7 | 1.1 - 87.3 (6.9) |
subtype3 | 26 | 7 | 0.1 - 88.9 (6.6) |
subtype4 | 26 | 13 | 1.7 - 50.3 (15.2) |
subtype5 | 36 | 10 | 0.5 - 140.8 (11.5) |
subtype6 | 29 | 7 | 0.4 - 97.5 (10.6) |
subtype7 | 34 | 5 | 0.1 - 76.6 (7.1) |
Figure S78. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.0742 (Kruskal-Wallis (anova)), Q value = 1
Table S87. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.6 (10.7) |
subtype1 | 38 | 68.2 (11.5) |
subtype2 | 23 | 70.5 (11.1) |
subtype3 | 26 | 66.5 (10.8) |
subtype4 | 26 | 67.6 (10.0) |
subtype5 | 36 | 69.8 (9.5) |
subtype6 | 29 | 68.1 (12.1) |
subtype7 | 37 | 63.3 (9.2) |
Figure S79. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.00029 (Fisher's exact test), Q value = 0.031
Table S88. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 72 | 68 |
subtype1 | 0 | 1 | 9 | 11 | 17 |
subtype2 | 0 | 0 | 4 | 9 | 10 |
subtype3 | 1 | 0 | 9 | 13 | 3 |
subtype4 | 0 | 0 | 7 | 10 | 9 |
subtype5 | 0 | 0 | 7 | 10 | 18 |
subtype6 | 0 | 0 | 9 | 12 | 7 |
subtype7 | 0 | 1 | 24 | 7 | 4 |
Figure S80. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D8V3.png)
P value = 0.00215 (Fisher's exact test), Q value = 0.22
Table S89. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 102 | 32 |
subtype1 | 10 | 18 | 7 |
subtype2 | 6 | 13 | 4 |
subtype3 | 7 | 15 | 1 |
subtype4 | 5 | 10 | 8 |
subtype5 | 7 | 22 | 6 |
subtype6 | 7 | 18 | 2 |
subtype7 | 20 | 6 | 4 |
Figure S81. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D8V4.png)
P value = 0.00383 (Fisher's exact test), Q value = 0.37
Table S90. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 131 | 22 | 39 | 7 |
subtype1 | 18 | 7 | 9 | 2 |
subtype2 | 14 | 4 | 4 | 1 |
subtype3 | 19 | 0 | 2 | 1 |
subtype4 | 15 | 2 | 7 | 0 |
subtype5 | 18 | 3 | 14 | 0 |
subtype6 | 21 | 4 | 2 | 2 |
subtype7 | 26 | 2 | 1 | 1 |
Figure S82. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D8V5.png)
P value = 0.202 (Fisher's exact test), Q value = 1
Table S91. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 21 | 1 | 16 |
subtype2 | 7 | 0 | 16 |
subtype3 | 12 | 0 | 14 |
subtype4 | 12 | 2 | 12 |
subtype5 | 17 | 1 | 18 |
subtype6 | 15 | 0 | 14 |
subtype7 | 25 | 1 | 11 |
Figure S83. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D8V6.png)
P value = 0.0676 (Fisher's exact test), Q value = 1
Table S92. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 166 |
subtype1 | 6 | 33 |
subtype2 | 3 | 20 |
subtype3 | 6 | 20 |
subtype4 | 8 | 18 |
subtype5 | 15 | 21 |
subtype6 | 7 | 22 |
subtype7 | 5 | 32 |
Figure S84. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'
![](D8V7.png)
P value = 0.293 (Kruskal-Wallis (anova)), Q value = 1
Table S93. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 8 | 83.8 (9.2) |
subtype2 | 4 | 85.0 (5.8) |
subtype3 | 14 | 84.3 (12.8) |
subtype4 | 7 | 84.3 (9.8) |
subtype5 | 4 | 80.0 (18.3) |
subtype6 | 9 | 76.7 (21.8) |
subtype7 | 15 | 72.0 (17.0) |
Figure S85. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D8V8.png)
P value = 0.783 (Kruskal-Wallis (anova)), Q value = 1
Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.2 (28.3) |
subtype1 | 21 | 40.0 (30.6) |
subtype2 | 13 | 29.7 (17.6) |
subtype3 | 18 | 33.3 (27.5) |
subtype4 | 13 | 43.5 (27.8) |
subtype5 | 18 | 47.9 (42.9) |
subtype6 | 19 | 39.8 (26.0) |
subtype7 | 23 | 33.3 (17.7) |
Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D8V9.png)
P value = 0.0196 (Kruskal-Wallis (anova)), Q value = 1
Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 163 | 1.5 (3.2) |
subtype1 | 29 | 2.8 (5.0) |
subtype2 | 23 | 1.1 (1.8) |
subtype3 | 17 | 0.7 (2.0) |
subtype4 | 20 | 1.1 (1.9) |
subtype5 | 33 | 2.3 (3.9) |
subtype6 | 23 | 1.1 (2.8) |
subtype7 | 18 | 0.4 (1.2) |
Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D8V10.png)
P value = 0.0226 (Fisher's exact test), Q value = 1
Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 6 | 1 | 27 |
subtype2 | 0 | 1 | 21 |
subtype3 | 4 | 0 | 19 |
subtype4 | 0 | 2 | 21 |
subtype5 | 1 | 2 | 33 |
subtype6 | 2 | 3 | 23 |
subtype7 | 9 | 3 | 25 |
Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'
![](D8V11.png)
Table S97. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 42 | 90 | 84 |
P value = 0.357 (logrank test), Q value = 1
Table S98. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 39 | 10 | 0.1 - 130.9 (6.7) |
subtype2 | 90 | 31 | 0.4 - 140.8 (9.9) |
subtype3 | 81 | 18 | 0.1 - 88.9 (7.8) |
Figure S89. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D9V1.png)
P value = 0.00462 (Kruskal-Wallis (anova)), Q value = 0.44
Table S99. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.6 (10.7) |
subtype1 | 41 | 69.8 (11.4) |
subtype2 | 90 | 69.1 (10.2) |
subtype3 | 84 | 64.8 (10.3) |
Figure S90. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D9V2.png)
P value = 0.00084 (Fisher's exact test), Q value = 0.087
Table S100. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 72 | 68 |
subtype1 | 0 | 1 | 8 | 13 | 19 |
subtype2 | 0 | 0 | 21 | 34 | 33 |
subtype3 | 1 | 1 | 40 | 25 | 16 |
Figure S91. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D9V3.png)
P value = 0.00514 (Fisher's exact test), Q value = 0.48
Table S101. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 102 | 32 |
subtype1 | 11 | 19 | 10 |
subtype2 | 18 | 54 | 14 |
subtype3 | 33 | 29 | 8 |
Figure S92. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D9V4.png)
P value = 0.128 (Fisher's exact test), Q value = 1
Table S102. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 131 | 22 | 39 | 7 |
subtype1 | 20 | 7 | 11 | 2 |
subtype2 | 56 | 10 | 19 | 3 |
subtype3 | 55 | 5 | 9 | 2 |
Figure S93. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D9V5.png)
P value = 0.137 (Fisher's exact test), Q value = 1
Table S103. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 21 | 1 | 20 |
subtype2 | 39 | 1 | 50 |
subtype3 | 49 | 3 | 31 |
Figure S94. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D9V6.png)
P value = 0.0601 (Fisher's exact test), Q value = 1
Table S104. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 166 |
subtype1 | 6 | 36 |
subtype2 | 28 | 62 |
subtype3 | 16 | 68 |
Figure S95. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'
![](D9V7.png)
P value = 0.9 (Kruskal-Wallis (anova)), Q value = 1
Table S105. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 7 | 82.9 (9.5) |
subtype2 | 17 | 79.4 (17.8) |
subtype3 | 37 | 79.5 (15.1) |
Figure S96. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D9V8.png)
P value = 0.424 (Kruskal-Wallis (anova)), Q value = 1
Table S106. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.2 (28.3) |
subtype1 | 23 | 41.0 (33.4) |
subtype2 | 47 | 42.7 (32.2) |
subtype3 | 55 | 33.2 (21.2) |
Figure S97. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D9V9.png)
P value = 0.0178 (Kruskal-Wallis (anova)), Q value = 1
Table S107. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 163 | 1.5 (3.2) |
subtype1 | 34 | 2.7 (4.7) |
subtype2 | 79 | 1.5 (3.1) |
subtype3 | 50 | 0.7 (1.6) |
Figure S98. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D9V10.png)
P value = 0.0144 (Fisher's exact test), Q value = 1
Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 5 | 1 | 31 |
subtype2 | 3 | 8 | 77 |
subtype3 | 14 | 3 | 61 |
Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'
![](D9V11.png)
Table S109. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 39 | 111 | 66 |
P value = 0.806 (logrank test), Q value = 1
Table S110. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 210 | 59 | 0.1 - 140.8 (8.3) |
subtype1 | 36 | 10 | 0.1 - 130.9 (6.6) |
subtype2 | 111 | 33 | 0.4 - 140.8 (8.9) |
subtype3 | 63 | 16 | 0.1 - 123.8 (7.8) |
Figure S100. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D10V1.png)
P value = 0.00771 (Kruskal-Wallis (anova)), Q value = 0.71
Table S111. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 215 | 67.6 (10.7) |
subtype1 | 38 | 68.3 (11.2) |
subtype2 | 111 | 69.2 (10.3) |
subtype3 | 66 | 64.4 (10.4) |
Figure S101. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D10V2.png)
P value = 0.0173 (Fisher's exact test), Q value = 1
Table S112. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
nPatients | STAGE 0A | STAGE I | STAGE II | STAGE III | STAGE IV |
---|---|---|---|---|---|
ALL | 1 | 2 | 69 | 72 | 68 |
subtype1 | 0 | 1 | 9 | 13 | 15 |
subtype2 | 0 | 0 | 29 | 41 | 39 |
subtype3 | 1 | 1 | 31 | 18 | 14 |
Figure S102. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'
![](D10V3.png)
P value = 0.027 (Fisher's exact test), Q value = 1
Table S113. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
nPatients | T0+T1+T2 | T3 | T4 |
---|---|---|---|
ALL | 62 | 102 | 32 |
subtype1 | 10 | 17 | 8 |
subtype2 | 25 | 63 | 16 |
subtype3 | 27 | 22 | 8 |
Figure S103. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'
![](D10V4.png)
P value = 0.336 (Fisher's exact test), Q value = 1
Table S114. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
nPatients | N0 | N1 | N2 | N3 |
---|---|---|---|---|
ALL | 131 | 22 | 39 | 7 |
subtype1 | 20 | 7 | 7 | 2 |
subtype2 | 68 | 11 | 24 | 3 |
subtype3 | 43 | 4 | 8 | 2 |
Figure S104. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'
![](D10V5.png)
P value = 0.533 (Fisher's exact test), Q value = 1
Table S115. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 109 | 5 | 101 |
subtype1 | 21 | 1 | 16 |
subtype2 | 51 | 2 | 58 |
subtype3 | 37 | 2 | 27 |
Figure S105. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'
![](D10V6.png)
P value = 0.13 (Fisher's exact test), Q value = 1
Table S116. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 50 | 166 |
subtype1 | 6 | 33 |
subtype2 | 32 | 79 |
subtype3 | 12 | 54 |
Figure S106. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'
![](D10V7.png)
P value = 0.552 (Kruskal-Wallis (anova)), Q value = 1
Table S117. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 61 | 79.8 (15.2) |
subtype1 | 9 | 84.4 (8.8) |
subtype2 | 21 | 80.5 (16.3) |
subtype3 | 31 | 78.1 (16.0) |
Figure S107. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D10V8.png)
P value = 0.814 (Kruskal-Wallis (anova)), Q value = 1
Table S118. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 125 | 38.2 (28.3) |
subtype1 | 21 | 37.9 (31.7) |
subtype2 | 61 | 40.3 (30.6) |
subtype3 | 43 | 35.3 (22.9) |
Figure S108. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'
![](D10V9.png)
P value = 0.304 (Kruskal-Wallis (anova)), Q value = 1
Table S119. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 163 | 1.5 (3.2) |
subtype1 | 29 | 2.2 (4.7) |
subtype2 | 95 | 1.6 (3.1) |
subtype3 | 39 | 0.9 (1.8) |
Figure S109. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'
![](D10V10.png)
P value = 0.00084 (Fisher's exact test), Q value = 0.087
Table S120. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'
nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE |
---|---|---|---|
ALL | 22 | 12 | 169 |
subtype1 | 6 | 1 | 27 |
subtype2 | 3 | 9 | 95 |
subtype3 | 13 | 2 | 47 |
Figure S110. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'
![](D10V11.png)
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Cluster data file = BLCA-TP.mergedcluster.txt
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Clinical data file = BLCA-TP.merged_data.txt
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Number of patients = 218
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Number of clustering approaches = 10
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Number of selected clinical features = 11
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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 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.