This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 7 different clustering approaches and 5 clinical features across 852 patients, 13 significant findings detected with P value < 0.05.
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CNMF clustering analysis on array-based mRNA expression data identified 8 subtypes that correlate to 'Time to Death', 'AGE', and 'GENDER'.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
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7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', and 'GENDER'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOADJUVANT.THERAPY'.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 5 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 13 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
RADIATIONS RADIATION REGIMENINDICATION |
NEOADJUVANT THERAPY |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Fisher's exact test | Fisher's exact test |
mRNA CNMF subtypes | 7.48e-05 | 5.88e-06 | 0.0354 | 0.848 | 0.436 |
mRNA cHierClus subtypes | 0.445 | 0.00183 | 0.323 | 0.224 | 0.404 |
METHLYATION CNMF | 0.000294 | 0.00109 | 0.0199 | 0.115 | 0.991 |
RNAseq CNMF subtypes | 0.137 | 0.0413 | 0.148 | 0.457 | 0.145 |
RNAseq cHierClus subtypes | 0.187 | 0.12 | 0.158 | 0.129 | 0.00648 |
MIRseq CNMF subtypes | 0.00773 | 0.00953 | 0.222 | 0.696 | 0.322 |
MIRseq cHierClus subtypes | 0.0258 | 0.0129 | 0.13 | 0.455 | 0.311 |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
---|---|---|---|---|---|---|---|---|
Number of samples | 20 | 34 | 117 | 103 | 120 | 73 | 20 | 42 |
P value = 7.48e-05 (logrank test)
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 497 | 65 | 0.1 - 223.4 (24.1) |
subtype1 | 18 | 3 | 0.3 - 92.0 (14.2) |
subtype2 | 33 | 3 | 0.1 - 157.4 (43.4) |
subtype3 | 109 | 17 | 0.1 - 177.4 (25.1) |
subtype4 | 98 | 12 | 0.1 - 211.5 (21.9) |
subtype5 | 115 | 10 | 0.3 - 223.4 (19.0) |
subtype6 | 64 | 14 | 0.1 - 189.0 (24.6) |
subtype7 | 19 | 2 | 0.2 - 97.5 (36.3) |
subtype8 | 41 | 4 | 0.3 - 112.4 (20.0) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 5.88e-06 (ANOVA)
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 529 | 57.9 (13.2) |
subtype1 | 20 | 59.6 (14.1) |
subtype2 | 34 | 49.9 (10.1) |
subtype3 | 117 | 58.0 (14.3) |
subtype4 | 103 | 53.8 (12.6) |
subtype5 | 120 | 62.0 (12.7) |
subtype6 | 73 | 58.2 (12.7) |
subtype7 | 20 | 60.4 (9.9) |
subtype8 | 42 | 59.9 (12.8) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.0354 (Chi-square test)
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 523 | 6 |
subtype1 | 19 | 1 |
subtype2 | 34 | 0 |
subtype3 | 113 | 4 |
subtype4 | 103 | 0 |
subtype5 | 120 | 0 |
subtype6 | 73 | 0 |
subtype7 | 19 | 1 |
subtype8 | 42 | 0 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 0.848 (Chi-square test)
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 382 | 147 |
subtype1 | 17 | 3 |
subtype2 | 25 | 9 |
subtype3 | 86 | 31 |
subtype4 | 69 | 34 |
subtype5 | 86 | 34 |
subtype6 | 54 | 19 |
subtype7 | 15 | 5 |
subtype8 | 30 | 12 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V4.png)
P value = 0.436 (Chi-square test)
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 308 | 221 |
subtype1 | 15 | 5 |
subtype2 | 17 | 17 |
subtype3 | 66 | 51 |
subtype4 | 55 | 48 |
subtype5 | 71 | 49 |
subtype6 | 47 | 26 |
subtype7 | 14 | 6 |
subtype8 | 23 | 19 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D1V5.png)
Table S7. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 129 | 136 | 264 |
P value = 0.445 (logrank test)
Table S8. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 497 | 65 | 0.1 - 223.4 (24.1) |
subtype1 | 118 | 15 | 0.1 - 211.5 (21.6) |
subtype2 | 134 | 15 | 0.3 - 157.4 (27.6) |
subtype3 | 245 | 35 | 0.1 - 223.4 (21.1) |
Figure S6. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.00183 (ANOVA)
Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 529 | 57.9 (13.2) |
subtype1 | 129 | 55.1 (12.6) |
subtype2 | 136 | 56.8 (13.0) |
subtype3 | 264 | 59.8 (13.4) |
Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.323 (Fisher's exact test)
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 523 | 6 |
subtype1 | 129 | 0 |
subtype2 | 135 | 1 |
subtype3 | 259 | 5 |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.224 (Fisher's exact test)
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 382 | 147 |
subtype1 | 86 | 43 |
subtype2 | 98 | 38 |
subtype3 | 198 | 66 |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V4.png)
P value = 0.404 (Fisher's exact test)
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 308 | 221 |
subtype1 | 70 | 59 |
subtype2 | 77 | 59 |
subtype3 | 161 | 103 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D2V5.png)
Table S13. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
---|---|---|---|---|---|---|---|
Number of samples | 82 | 138 | 54 | 97 | 29 | 52 | 87 |
P value = 0.000294 (logrank test)
Table S14. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 510 | 61 | 0.0 - 223.4 (17.9) |
subtype1 | 77 | 12 | 0.2 - 211.5 (19.2) |
subtype2 | 130 | 12 | 0.3 - 223.4 (12.6) |
subtype3 | 50 | 10 | 0.0 - 162.0 (14.0) |
subtype4 | 93 | 7 | 0.1 - 177.4 (23.1) |
subtype5 | 29 | 3 | 0.1 - 157.4 (20.8) |
subtype6 | 50 | 9 | 0.0 - 109.9 (17.9) |
subtype7 | 81 | 8 | 0.0 - 194.3 (27.9) |
Figure S11. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.00109 (ANOVA)
Table S15. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 538 | 57.6 (13.1) |
subtype1 | 82 | 54.8 (11.9) |
subtype2 | 138 | 58.9 (12.3) |
subtype3 | 54 | 59.0 (12.3) |
subtype4 | 97 | 55.3 (15.0) |
subtype5 | 29 | 57.1 (15.8) |
subtype6 | 52 | 63.9 (11.5) |
subtype7 | 86 | 56.1 (12.4) |
Figure S12. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.0199 (Chi-square test)
Table S16. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 533 | 6 |
subtype1 | 82 | 0 |
subtype2 | 137 | 1 |
subtype3 | 54 | 0 |
subtype4 | 96 | 1 |
subtype5 | 27 | 2 |
subtype6 | 50 | 2 |
subtype7 | 87 | 0 |
Figure S13. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.115 (Chi-square test)
Table S17. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 406 | 133 |
subtype1 | 54 | 28 |
subtype2 | 107 | 31 |
subtype3 | 36 | 18 |
subtype4 | 73 | 24 |
subtype5 | 25 | 4 |
subtype6 | 43 | 9 |
subtype7 | 68 | 19 |
Figure S14. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V4.png)
P value = 0.991 (Chi-square test)
Table S18. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 337 | 202 |
subtype1 | 49 | 33 |
subtype2 | 85 | 53 |
subtype3 | 33 | 21 |
subtype4 | 61 | 36 |
subtype5 | 19 | 10 |
subtype6 | 33 | 19 |
subtype7 | 57 | 30 |
Figure S15. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D3V5.png)
Table S19. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 229 | 130 | 392 |
P value = 0.137 (logrank test)
Table S20. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 697 | 86 | 0.0 - 223.4 (18.1) |
subtype1 | 208 | 31 | 0.0 - 211.5 (18.4) |
subtype2 | 124 | 10 | 0.3 - 157.4 (26.3) |
subtype3 | 365 | 45 | 0.0 - 223.4 (17.0) |
Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.0413 (ANOVA)
Table S21. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 750 | 57.7 (13.3) |
subtype1 | 229 | 56.6 (12.9) |
subtype2 | 129 | 56.2 (12.4) |
subtype3 | 392 | 58.9 (13.6) |
Figure S17. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.148 (Fisher's exact test)
Table S22. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 744 | 7 |
subtype1 | 229 | 0 |
subtype2 | 129 | 1 |
subtype3 | 386 | 6 |
Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.457 (Fisher's exact test)
Table S23. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 564 | 187 |
subtype1 | 170 | 59 |
subtype2 | 93 | 37 |
subtype3 | 301 | 91 |
Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V4.png)
P value = 0.145 (Fisher's exact test)
Table S24. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 458 | 293 |
subtype1 | 139 | 90 |
subtype2 | 70 | 60 |
subtype3 | 249 | 143 |
Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D4V5.png)
Table S25. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 195 | 380 | 176 |
P value = 0.187 (logrank test)
Table S26. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 697 | 86 | 0.0 - 223.4 (18.1) |
subtype1 | 179 | 26 | 0.0 - 211.5 (16.5) |
subtype2 | 348 | 44 | 0.0 - 223.4 (16.8) |
subtype3 | 170 | 16 | 0.1 - 157.4 (25.5) |
Figure S21. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.12 (ANOVA)
Table S27. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 750 | 57.7 (13.3) |
subtype1 | 195 | 56.3 (13.0) |
subtype2 | 380 | 58.6 (13.6) |
subtype3 | 175 | 57.3 (12.7) |
Figure S22. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.158 (Fisher's exact test)
Table S28. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 744 | 7 |
subtype1 | 195 | 0 |
subtype2 | 374 | 6 |
subtype3 | 175 | 1 |
Figure S23. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.129 (Fisher's exact test)
Table S29. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 564 | 187 |
subtype1 | 142 | 53 |
subtype2 | 297 | 83 |
subtype3 | 125 | 51 |
Figure S24. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V4.png)
P value = 0.00648 (Fisher's exact test)
Table S30. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 458 | 293 |
subtype1 | 117 | 78 |
subtype2 | 250 | 130 |
subtype3 | 91 | 85 |
Figure S25. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D5V5.png)
Table S31. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 194 | 403 | 210 |
P value = 0.00773 (logrank test)
Table S32. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 748 | 92 | 0.0 - 223.4 (19.0) |
subtype1 | 186 | 21 | 0.3 - 159.1 (24.5) |
subtype2 | 372 | 39 | 0.0 - 223.4 (18.0) |
subtype3 | 190 | 32 | 0.0 - 211.5 (17.6) |
Figure S26. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.00953 (ANOVA)
Table S33. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 806 | 58.2 (13.2) |
subtype1 | 193 | 56.1 (12.6) |
subtype2 | 403 | 59.5 (13.6) |
subtype3 | 210 | 57.6 (12.9) |
Figure S27. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.222 (Fisher's exact test)
Table S34. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 798 | 9 |
subtype1 | 194 | 0 |
subtype2 | 396 | 7 |
subtype3 | 208 | 2 |
Figure S28. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.696 (Fisher's exact test)
Table S35. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 620 | 187 |
subtype1 | 153 | 41 |
subtype2 | 305 | 98 |
subtype3 | 162 | 48 |
Figure S29. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V4.png)
P value = 0.322 (Fisher's exact test)
Table S36. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 509 | 298 |
subtype1 | 117 | 77 |
subtype2 | 251 | 152 |
subtype3 | 141 | 69 |
Figure S30. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D6V5.png)
Table S37. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 223 | 423 | 161 |
P value = 0.0258 (logrank test)
Table S38. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 748 | 92 | 0.0 - 223.4 (19.0) |
subtype1 | 194 | 26 | 0.0 - 194.3 (17.0) |
subtype2 | 401 | 40 | 0.1 - 223.4 (19.1) |
subtype3 | 153 | 26 | 0.1 - 211.5 (20.0) |
Figure S31. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.0129 (ANOVA)
Table S39. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 806 | 58.2 (13.2) |
subtype1 | 223 | 59.4 (13.4) |
subtype2 | 422 | 58.5 (13.2) |
subtype3 | 161 | 55.5 (12.8) |
Figure S32. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.13 (Fisher's exact test)
Table S40. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 798 | 9 |
subtype1 | 218 | 5 |
subtype2 | 419 | 4 |
subtype3 | 161 | 0 |
Figure S33. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.455 (Fisher's exact test)
Table S41. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 620 | 187 |
subtype1 | 178 | 45 |
subtype2 | 321 | 102 |
subtype3 | 121 | 40 |
Figure S34. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V4.png)
P value = 0.311 (Fisher's exact test)
Table S42. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 509 | 298 |
subtype1 | 150 | 73 |
subtype2 | 259 | 164 |
subtype3 | 100 | 61 |
Figure S35. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'NEOADJUVANT.THERAPY'
![](D7V5.png)
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Cluster data file = BRCA.mergedcluster.txt
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Clinical data file = BRCA.clin.merged.picked.txt
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Number of patients = 852
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Number of clustering approaches = 7
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Number of selected clinical features = 5
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R
For 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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.