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 7 clinical features across 208 patients, 17 significant findings detected with P value < 0.05 and Q value < 0.25.
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CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', 'HISTOLOGICAL.TYPE', and 'HISTOLOGICCLASSIFICATION'.
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4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', 'HISTOLOGICCLASSIFICATION', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'HISTOLOGICCLASSIFICATION'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 7 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 17 significant findings detected.
Clinical Features |
Time to Death |
AGE | GENDER |
KARNOFSKY PERFORMANCE SCORE |
HISTOLOGICAL TYPE |
HISTOLOGICCLASSIFICATION |
RADIATIONS RADIATION REGIMENINDICATION |
Statistical Tests | logrank test | ANOVA | Fisher's exact test | ANOVA | Chi-square test | Fisher's exact test | Fisher's exact test |
mRNA CNMF subtypes |
0.247 (1.00) |
0.326 (1.00) |
0.101 (1.00) |
0.441 (1.00) |
0.0226 (0.835) |
0.864 (1.00) |
0.384 (1.00) |
mRNA cHierClus subtypes |
0.31 (1.00) |
0.467 (1.00) |
0.172 (1.00) |
0.441 (1.00) |
0.0136 (0.517) |
0.86 (1.00) |
0.757 (1.00) |
Copy Number Ratio CNMF subtypes |
1.62e-05 (0.000744) |
6.39e-08 (3.32e-06) |
0.00183 (0.0732) |
0.977 (1.00) |
2.47e-13 (1.36e-11) |
0.0013 (0.0546) |
0.0468 (1.00) |
METHLYATION CNMF |
4.96e-06 (0.000243) |
2.41e-07 (1.23e-05) |
0.0514 (1.00) |
0.695 (1.00) |
2.93e-15 (1.64e-13) |
3.13e-07 (1.56e-05) |
3.67e-05 (0.00161) |
RNAseq CNMF subtypes |
1.74e-05 (0.000782) |
0.00125 (0.0539) |
0.0091 (0.355) |
0.327 (1.00) |
8.84e-11 (4.78e-09) |
8.16e-06 (0.000392) |
0.0465 (1.00) |
RNAseq cHierClus subtypes |
0.0328 (1.00) |
0.0419 (1.00) |
0.444 (1.00) |
0.245 (1.00) |
2.28e-10 (1.21e-08) |
0.22 (1.00) |
0.0253 (0.91) |
MIRSEQ CNMF |
0.105 (1.00) |
0.524 (1.00) |
0.256 (1.00) |
0.597 (1.00) |
1.06e-05 (0.000496) |
0.0643 (1.00) |
0.101 (1.00) |
MIRSEQ CHIERARCHICAL |
0.0382 (1.00) |
0.68 (1.00) |
0.579 (1.00) |
0.497 (1.00) |
0.00175 (0.0718) |
0.298 (1.00) |
0.0688 (1.00) |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 9 | 10 | 8 |
P value = 0.247 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 27 | 9 | 0.1 - 134.3 (46.6) |
subtype1 | 9 | 4 | 10.6 - 130.8 (43.9) |
subtype2 | 10 | 3 | 0.1 - 78.2 (36.5) |
subtype3 | 8 | 2 | 14.4 - 134.3 (51.3) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.326 (ANOVA), Q value = 1
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 27 | 39.3 (9.1) |
subtype1 | 9 | 39.2 (6.2) |
subtype2 | 10 | 42.3 (7.6) |
subtype3 | 8 | 35.8 (12.6) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.101 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 9 | 18 |
subtype1 | 2 | 7 |
subtype2 | 6 | 4 |
subtype3 | 1 | 7 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 0.441 (ANOVA), Q value = 1
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 88.8 (12.2) |
subtype1 | 7 | 84.3 (16.2) |
subtype2 | 7 | 92.9 (7.6) |
subtype3 | 3 | 90.0 (10.0) |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D1V4.png)
P value = 0.0226 (Chi-square test), Q value = 0.83
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 10 | 9 | 8 |
subtype1 | 7 | 2 | 0 |
subtype2 | 2 | 3 | 5 |
subtype3 | 1 | 4 | 3 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D1V5.png)
P value = 0.864 (Fisher's exact test), Q value = 1
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 7 | 20 |
subtype1 | 3 | 6 |
subtype2 | 2 | 8 |
subtype3 | 2 | 6 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D1V6.png)
P value = 0.384 (Fisher's exact test), Q value = 1
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 19 | 8 |
subtype1 | 7 | 2 |
subtype2 | 8 | 2 |
subtype3 | 4 | 4 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V7.png)
Table S9. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 9 | 7 | 11 |
P value = 0.31 (logrank test), Q value = 1
Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 27 | 9 | 0.1 - 134.3 (46.6) |
subtype1 | 9 | 4 | 10.6 - 130.8 (43.9) |
subtype2 | 7 | 2 | 14.4 - 134.3 (52.4) |
subtype3 | 11 | 3 | 0.1 - 78.2 (41.1) |
Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.467 (ANOVA), Q value = 1
Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 27 | 39.3 (9.1) |
subtype1 | 9 | 39.2 (6.2) |
subtype2 | 7 | 36.0 (13.6) |
subtype3 | 11 | 41.5 (7.6) |
Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.172 (Fisher's exact test), Q value = 1
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 9 | 18 |
subtype1 | 2 | 7 |
subtype2 | 1 | 6 |
subtype3 | 6 | 5 |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.441 (ANOVA), Q value = 1
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 88.8 (12.2) |
subtype1 | 7 | 84.3 (16.2) |
subtype2 | 3 | 90.0 (10.0) |
subtype3 | 7 | 92.9 (7.6) |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D2V4.png)
P value = 0.0136 (Chi-square test), Q value = 0.52
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 10 | 9 | 8 |
subtype1 | 7 | 2 | 0 |
subtype2 | 0 | 4 | 3 |
subtype3 | 3 | 3 | 5 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D2V5.png)
P value = 0.86 (Fisher's exact test), Q value = 1
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 7 | 20 |
subtype1 | 3 | 6 |
subtype2 | 2 | 5 |
subtype3 | 2 | 9 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D2V6.png)
P value = 0.757 (Fisher's exact test), Q value = 1
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 19 | 8 |
subtype1 | 7 | 2 |
subtype2 | 4 | 3 |
subtype3 | 8 | 3 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V7.png)
Table S17. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 85 | 48 | 74 |
P value = 1.62e-05 (logrank test), Q value = 0.00074
Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 206 | 51 | 0.0 - 211.2 (13.4) |
subtype1 | 85 | 14 | 0.1 - 156.2 (13.4) |
subtype2 | 48 | 24 | 0.5 - 211.2 (11.9) |
subtype3 | 73 | 13 | 0.0 - 182.3 (14.3) |
Figure S15. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 6.39e-08 (ANOVA), Q value = 3.3e-06
Table S19. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 207 | 43.1 (13.4) |
subtype1 | 85 | 37.3 (11.8) |
subtype2 | 48 | 50.1 (12.9) |
subtype3 | 74 | 45.2 (12.9) |
Figure S16. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.00183 (Fisher's exact test), Q value = 0.073
Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 118 |
subtype1 | 28 | 57 |
subtype2 | 31 | 17 |
subtype3 | 30 | 44 |
Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.977 (ANOVA), Q value = 1
Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 93 | 88.4 (10.5) |
subtype1 | 37 | 88.6 (10.8) |
subtype2 | 26 | 88.1 (7.5) |
subtype3 | 30 | 88.3 (12.3) |
Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 2.47e-13 (Chi-square test), Q value = 1.4e-11
Table S22. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 63 | 54 | 89 |
subtype1 | 33 | 30 | 21 |
subtype2 | 24 | 15 | 9 |
subtype3 | 6 | 9 | 59 |
Figure S19. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D3V5.png)
P value = 0.0013 (Fisher's exact test), Q value = 0.055
Table S23. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 94 | 112 |
subtype1 | 44 | 40 |
subtype2 | 11 | 37 |
subtype3 | 39 | 35 |
Figure S20. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D3V6.png)
P value = 0.0468 (Fisher's exact test), Q value = 1
Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 104 | 103 |
subtype1 | 46 | 39 |
subtype2 | 29 | 19 |
subtype3 | 29 | 45 |
Figure S21. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V7.png)
Table S25. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 92 | 31 | 57 | 23 |
P value = 4.96e-06 (logrank test), Q value = 0.00024
Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 202 | 47 | 0.0 - 211.2 (13.4) |
subtype1 | 92 | 22 | 0.0 - 156.2 (17.4) |
subtype2 | 31 | 16 | 0.1 - 211.2 (8.4) |
subtype3 | 56 | 8 | 0.1 - 182.3 (12.9) |
subtype4 | 23 | 1 | 0.1 - 97.9 (7.4) |
Figure S22. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 2.41e-07 (ANOVA), Q value = 1.2e-05
Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 203 | 43.0 (13.4) |
subtype1 | 92 | 38.9 (11.8) |
subtype2 | 31 | 52.6 (12.8) |
subtype3 | 57 | 46.5 (12.7) |
subtype4 | 23 | 38.3 (13.8) |
Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0514 (Fisher's exact test), Q value = 1
Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 114 |
subtype1 | 33 | 59 |
subtype2 | 19 | 12 |
subtype3 | 24 | 33 |
subtype4 | 13 | 10 |
Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.695 (ANOVA), Q value = 1
Table S29. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 92 | 88.5 (10.5) |
subtype1 | 49 | 89.4 (10.3) |
subtype2 | 15 | 87.3 (5.9) |
subtype3 | 23 | 88.3 (13.4) |
subtype4 | 5 | 84.0 (8.9) |
Figure S25. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D4V4.png)
P value = 2.93e-15 (Chi-square test), Q value = 1.6e-13
Table S30. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 62 | 53 | 87 |
subtype1 | 36 | 34 | 21 |
subtype2 | 20 | 5 | 6 |
subtype3 | 1 | 6 | 50 |
subtype4 | 5 | 8 | 10 |
Figure S26. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D4V5.png)
P value = 3.13e-07 (Fisher's exact test), Q value = 1.6e-05
Table S31. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 93 | 109 |
subtype1 | 47 | 44 |
subtype2 | 1 | 30 |
subtype3 | 31 | 26 |
subtype4 | 14 | 9 |
Figure S27. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D4V6.png)
P value = 3.67e-05 (Fisher's exact test), Q value = 0.0016
Table S32. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 101 | 102 |
subtype1 | 61 | 31 |
subtype2 | 16 | 15 |
subtype3 | 18 | 39 |
subtype4 | 6 | 17 |
Figure S28. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V7.png)
Table S33. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 55 | 40 | 35 | 67 | 9 |
P value = 1.74e-05 (logrank test), Q value = 0.00078
Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 205 | 51 | 0.0 - 211.2 (13.4) |
subtype1 | 55 | 14 | 0.0 - 130.8 (16.5) |
subtype2 | 40 | 20 | 0.1 - 211.2 (9.6) |
subtype3 | 34 | 8 | 0.1 - 182.3 (15.8) |
subtype4 | 67 | 8 | 0.1 - 156.2 (14.5) |
subtype5 | 9 | 1 | 0.2 - 44.0 (14.3) |
Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.00125 (ANOVA), Q value = 0.054
Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 206 | 43.2 (13.4) |
subtype1 | 55 | 38.8 (11.7) |
subtype2 | 40 | 48.1 (14.0) |
subtype3 | 35 | 47.5 (12.5) |
subtype4 | 67 | 42.5 (13.8) |
subtype5 | 9 | 36.2 (10.5) |
Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0091 (Chi-square test), Q value = 0.36
Table S36. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 117 |
subtype1 | 19 | 36 |
subtype2 | 26 | 14 |
subtype3 | 18 | 17 |
subtype4 | 24 | 43 |
subtype5 | 2 | 7 |
Figure S31. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.327 (ANOVA), Q value = 1
Table S37. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 91 | 88.2 (10.5) |
subtype1 | 32 | 90.9 (9.3) |
subtype2 | 18 | 87.8 (5.5) |
subtype3 | 16 | 86.9 (15.4) |
subtype4 | 22 | 86.8 (9.9) |
subtype5 | 3 | 80.0 (17.3) |
Figure S32. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D5V4.png)
P value = 8.84e-11 (Chi-square test), Q value = 4.8e-09
Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 62 | 54 | 89 |
subtype1 | 20 | 20 | 15 |
subtype2 | 28 | 6 | 6 |
subtype3 | 2 | 5 | 28 |
subtype4 | 10 | 20 | 36 |
subtype5 | 2 | 3 | 4 |
Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D5V5.png)
P value = 8.16e-06 (Chi-square test), Q value = 0.00039
Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 94 | 111 |
subtype1 | 35 | 20 |
subtype2 | 4 | 36 |
subtype3 | 16 | 19 |
subtype4 | 34 | 32 |
subtype5 | 5 | 4 |
Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D5V6.png)
P value = 0.0465 (Chi-square test), Q value = 1
Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 102 | 104 |
subtype1 | 35 | 20 |
subtype2 | 22 | 18 |
subtype3 | 12 | 23 |
subtype4 | 30 | 37 |
subtype5 | 3 | 6 |
Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V7.png)
Table S41. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 32 | 108 | 66 |
P value = 0.0328 (logrank test), Q value = 1
Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 205 | 51 | 0.0 - 211.2 (13.4) |
subtype1 | 31 | 8 | 0.1 - 182.3 (15.1) |
subtype2 | 108 | 33 | 0.0 - 211.2 (12.9) |
subtype3 | 66 | 10 | 0.1 - 156.2 (14.5) |
Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.0419 (ANOVA), Q value = 1
Table S43. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 206 | 43.2 (13.4) |
subtype1 | 32 | 48.6 (12.4) |
subtype2 | 108 | 42.0 (13.2) |
subtype3 | 66 | 42.5 (13.7) |
Figure S37. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.444 (Fisher's exact test), Q value = 1
Table S44. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 117 |
subtype1 | 17 | 15 |
subtype2 | 46 | 62 |
subtype3 | 26 | 40 |
Figure S38. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.245 (ANOVA), Q value = 1
Table S45. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 91 | 88.2 (10.5) |
subtype1 | 16 | 86.9 (15.4) |
subtype2 | 52 | 89.8 (9.0) |
subtype3 | 23 | 85.7 (9.5) |
Figure S39. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D6V4.png)
P value = 2.28e-10 (Chi-square test), Q value = 1.2e-08
Table S46. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 62 | 54 | 89 |
subtype1 | 1 | 4 | 27 |
subtype2 | 51 | 31 | 25 |
subtype3 | 10 | 19 | 37 |
Figure S40. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D6V5.png)
P value = 0.22 (Fisher's exact test), Q value = 1
Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 94 | 111 |
subtype1 | 14 | 18 |
subtype2 | 44 | 63 |
subtype3 | 36 | 30 |
Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D6V6.png)
P value = 0.0253 (Fisher's exact test), Q value = 0.91
Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 102 | 104 |
subtype1 | 10 | 22 |
subtype2 | 62 | 46 |
subtype3 | 30 | 36 |
Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V7.png)
Table S49. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 55 | 32 | 77 | 43 |
P value = 0.105 (logrank test), Q value = 1
Table S50. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 206 | 50 | 0.0 - 211.2 (13.4) |
subtype1 | 55 | 14 | 0.0 - 117.4 (8.4) |
subtype2 | 32 | 6 | 0.1 - 182.3 (21.2) |
subtype3 | 76 | 13 | 0.1 - 138.2 (12.9) |
subtype4 | 43 | 17 | 0.1 - 211.2 (16.8) |
Figure S43. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.524 (ANOVA), Q value = 1
Table S51. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 207 | 43.2 (13.4) |
subtype1 | 55 | 43.1 (14.3) |
subtype2 | 32 | 43.2 (10.6) |
subtype3 | 77 | 44.6 (13.9) |
subtype4 | 43 | 40.8 (13.2) |
Figure S44. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.256 (Fisher's exact test), Q value = 1
Table S52. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 118 |
subtype1 | 22 | 33 |
subtype2 | 19 | 13 |
subtype3 | 31 | 46 |
subtype4 | 17 | 26 |
Figure S45. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.597 (ANOVA), Q value = 1
Table S53. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 92 | 88.5 (10.5) |
subtype1 | 24 | 90.8 (8.8) |
subtype2 | 19 | 88.4 (13.4) |
subtype3 | 29 | 86.9 (9.7) |
subtype4 | 20 | 88.0 (10.6) |
Figure S46. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V4.png)
P value = 1.06e-05 (Chi-square test), Q value = 5e-04
Table S54. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 63 | 54 | 89 |
subtype1 | 26 | 13 | 15 |
subtype2 | 3 | 7 | 22 |
subtype3 | 13 | 23 | 41 |
subtype4 | 21 | 11 | 11 |
Figure S47. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D7V5.png)
P value = 0.0643 (Fisher's exact test), Q value = 1
Table S55. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 94 | 112 |
subtype1 | 26 | 28 |
subtype2 | 17 | 15 |
subtype3 | 39 | 38 |
subtype4 | 12 | 31 |
Figure S48. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D7V6.png)
P value = 0.101 (Fisher's exact test), Q value = 1
Table S56. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 103 | 104 |
subtype1 | 27 | 28 |
subtype2 | 12 | 20 |
subtype3 | 36 | 41 |
subtype4 | 28 | 15 |
Figure S49. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V7.png)
Table S57. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 8 | 89 | 110 |
P value = 0.0382 (logrank test), Q value = 1
Table S58. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 206 | 50 | 0.0 - 211.2 (13.4) |
subtype1 | 8 | 1 | 7.1 - 52.4 (13.8) |
subtype2 | 88 | 13 | 0.1 - 211.2 (12.4) |
subtype3 | 110 | 36 | 0.0 - 182.3 (15.6) |
Figure S50. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'
![](D8V1.png)
P value = 0.68 (ANOVA), Q value = 1
Table S59. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 207 | 43.2 (13.4) |
subtype1 | 8 | 42.1 (14.0) |
subtype2 | 89 | 44.2 (13.8) |
subtype3 | 110 | 42.5 (13.1) |
Figure S51. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'
![](D8V2.png)
P value = 0.579 (Fisher's exact test), Q value = 1
Table S60. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 118 |
subtype1 | 3 | 5 |
subtype2 | 42 | 47 |
subtype3 | 44 | 66 |
Figure S52. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'
![](D8V3.png)
P value = 0.497 (ANOVA), Q value = 1
Table S61. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 92 | 88.5 (10.5) |
subtype1 | 4 | 87.5 (5.0) |
subtype2 | 36 | 86.9 (11.9) |
subtype3 | 52 | 89.6 (9.7) |
Figure S53. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D8V4.png)
P value = 0.00175 (Chi-square test), Q value = 0.072
Table S62. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | ASTROCYTOMA | OLIGOASTROCYTOMA | OLIGODENDROGLIOMA |
---|---|---|---|
ALL | 63 | 54 | 89 |
subtype1 | 4 | 2 | 2 |
subtype2 | 15 | 23 | 51 |
subtype3 | 44 | 29 | 36 |
Figure S54. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D8V5.png)
P value = 0.298 (Fisher's exact test), Q value = 1
Table S63. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
nPatients | GRADE II | GRADE III |
---|---|---|
ALL | 94 | 112 |
subtype1 | 2 | 6 |
subtype2 | 45 | 44 |
subtype3 | 47 | 62 |
Figure S55. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'HISTOLOGICCLASSIFICATION'
![](D8V6.png)
P value = 0.0688 (Fisher's exact test), Q value = 1
Table S64. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 103 | 104 |
subtype1 | 6 | 2 |
subtype2 | 37 | 52 |
subtype3 | 60 | 50 |
Figure S56. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D8V7.png)
-
Cluster data file = LGG-TP.mergedcluster.txt
-
Clinical data file = LGG-TP.clin.merged.picked.txt
-
Number of patients = 208
-
Number of clustering approaches = 8
-
Number of selected clinical features = 7
-
Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R
For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R
For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R
For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R
For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.