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 10 clinical features across 164 patients, 6 significant findings detected with P value < 0.05.
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CNMF clustering analysis on array-based mRNA expression data identified 4 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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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 sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'GENDER', 'PATHOLOGY.T', and 'PATHOLOGY.N'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that correlate to 'PATHOLOGICSPREAD(M)'.
Table 1. Get Full Table Overview of the association between subtypes identified by 7 different clustering approaches and 10 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 6 significant findings detected.
Clinical Features |
Statistical Tests |
mRNA CNMF subtypes |
mRNA cHierClus subtypes |
METHLYATION CNMF |
RNAseq CNMF subtypes |
RNAseq cHierClus subtypes |
MIRseq CNMF subtypes |
MIRseq cHierClus subtypes |
Time to Death | logrank test | 0.337 | 0.671 | 0.257 | 0.00518 | 0.08 | 0.703 | 0.585 |
AGE | ANOVA | 0.477 | 0.557 | 0.523 | 0.295 | 0.0861 | 0.248 | 0.0645 |
GENDER | Fisher's exact test | 0.272 | 0.383 | 0.405 | 0.0255 | 0.0538 | 0.437 | 0.371 |
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.207 | 0.43 | |||||
HISTOLOGICAL TYPE | Chi-square test | 0.3 | 0.274 | 0.252 | 0.24 | 0.402 | 0.0393 | 0.0939 |
PATHOLOGY T | Chi-square test | 0.489 | 0.479 | 0.477 | 0.0399 | 0.213 | 0.182 | 0.674 |
PATHOLOGY N | Chi-square test | 0.572 | 0.651 | 0.899 | 0.0177 | 0.532 | 0.587 | 0.617 |
PATHOLOGICSPREAD(M) | Chi-square test | 0.504 | 1 | 0.825 | 0.606 | 0.475 | 0.104 | 0.0218 |
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 1 | 1 | 0.505 | 0.866 | 0.949 | 0.458 | 1 |
NEOADJUVANT THERAPY | Fisher's exact test | 0.921 | 1 | 0.0871 | 0.667 | 0.403 | 0.686 | 0.629 |
Table S1. Get Full Table Description of clustering approach #1: 'mRNA CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 5 | 9 | 12 | 6 |
P value = 0.337 (logrank test)
Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 4 | 0.5 - 56.8 (8.3) |
subtype1 | 4 | 0 | 6.0 - 48.6 (9.7) |
subtype2 | 9 | 1 | 4.0 - 56.8 (8.3) |
subtype3 | 12 | 1 | 0.5 - 37.0 (3.3) |
subtype4 | 6 | 2 | 2.0 - 45.2 (30.9) |
Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D1V1.png)
P value = 0.477 (ANOVA)
Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 65.7 (10.8) |
subtype1 | 4 | 58.5 (15.5) |
subtype2 | 9 | 65.0 (9.1) |
subtype3 | 12 | 67.1 (11.1) |
subtype4 | 5 | 69.4 (9.0) |
Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D1V2.png)
P value = 0.272 (Fisher's exact test)
Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 14 | 18 |
subtype1 | 2 | 3 |
subtype2 | 6 | 3 |
subtype3 | 3 | 9 |
subtype4 | 3 | 3 |
Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D1V3.png)
P value = 0.3 (Chi-square test)
Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA |
---|---|---|---|
ALL | 1 | 30 | 1 |
subtype1 | 0 | 4 | 1 |
subtype2 | 0 | 9 | 0 |
subtype3 | 1 | 11 | 0 |
subtype4 | 0 | 6 | 0 |
Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D1V5.png)
P value = 0.489 (Chi-square test)
Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 12 | 19 | 1 |
subtype1 | 3 | 2 | 0 |
subtype2 | 4 | 4 | 1 |
subtype3 | 4 | 8 | 0 |
subtype4 | 1 | 5 | 0 |
Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D1V6.png)
P value = 0.572 (Chi-square test)
Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 23 | 4 | 4 |
subtype1 | 3 | 1 | 1 |
subtype2 | 8 | 1 | 0 |
subtype3 | 9 | 1 | 1 |
subtype4 | 3 | 1 | 2 |
Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D1V7.png)
P value = 0.504 (Fisher's exact test)
Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 30 | 2 |
subtype1 | 4 | 1 |
subtype2 | 9 | 0 |
subtype3 | 11 | 1 |
subtype4 | 6 | 0 |
Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D1V8.png)
P value = 1 (Fisher's exact test)
Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 31 | 1 |
subtype1 | 5 | 0 |
subtype2 | 9 | 0 |
subtype3 | 11 | 1 |
subtype4 | 6 | 0 |
Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D1V9.png)
P value = 0.921 (Fisher's exact test)
Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 26 | 6 |
subtype1 | 4 | 1 |
subtype2 | 8 | 1 |
subtype3 | 9 | 3 |
subtype4 | 5 | 1 |
Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D1V10.png)
Table S11. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 7 | 13 | 12 |
P value = 0.671 (logrank test)
Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 31 | 4 | 0.5 - 56.8 (8.3) |
subtype1 | 7 | 2 | 2.0 - 48.6 (38.7) |
subtype2 | 13 | 1 | 0.5 - 37.0 (3.4) |
subtype3 | 11 | 1 | 4.0 - 56.8 (8.1) |
Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D2V1.png)
P value = 0.557 (ANOVA)
Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 30 | 65.7 (10.8) |
subtype1 | 5 | 69.4 (9.0) |
subtype2 | 13 | 66.5 (10.9) |
subtype3 | 12 | 63.3 (11.6) |
Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D2V2.png)
P value = 0.383 (Fisher's exact test)
Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 14 | 18 |
subtype1 | 3 | 4 |
subtype2 | 4 | 9 |
subtype3 | 7 | 5 |
Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D2V3.png)
P value = 0.274 (Chi-square test)
Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA |
---|---|---|---|
ALL | 1 | 30 | 1 |
subtype1 | 0 | 6 | 1 |
subtype2 | 1 | 12 | 0 |
subtype3 | 0 | 12 | 0 |
Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D2V5.png)
P value = 0.479 (Chi-square test)
Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 |
---|---|---|---|
ALL | 12 | 19 | 1 |
subtype1 | 2 | 5 | 0 |
subtype2 | 4 | 9 | 0 |
subtype3 | 6 | 5 | 1 |
Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D2V6.png)
P value = 0.651 (Chi-square test)
Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 23 | 4 | 4 |
subtype1 | 4 | 1 | 2 |
subtype2 | 10 | 1 | 1 |
subtype3 | 9 | 2 | 1 |
Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D2V7.png)
P value = 1 (Fisher's exact test)
Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 30 | 2 |
subtype1 | 7 | 0 |
subtype2 | 12 | 1 |
subtype3 | 11 | 1 |
Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D2V8.png)
P value = 1 (Fisher's exact test)
Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 31 | 1 |
subtype1 | 7 | 0 |
subtype2 | 12 | 1 |
subtype3 | 12 | 0 |
Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D2V9.png)
P value = 1 (Fisher's exact test)
Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 26 | 6 |
subtype1 | 6 | 1 |
subtype2 | 10 | 3 |
subtype3 | 10 | 2 |
Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D2V10.png)
Table S21. Get Full Table Description of clustering approach #3: 'METHLYATION CNMF'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 27 | 14 | 10 | 15 |
P value = 0.257 (logrank test)
Table S22. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 61 | 21 | 0.5 - 85.3 (19.1) |
subtype1 | 25 | 8 | 0.5 - 85.3 (8.8) |
subtype2 | 12 | 6 | 4.0 - 56.8 (23.7) |
subtype3 | 10 | 4 | 2.0 - 46.7 (36.5) |
subtype4 | 14 | 3 | 0.5 - 47.6 (19.7) |
Figure S19. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
![](D3V1.png)
P value = 0.523 (ANOVA)
Table S23. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 64 | 65.4 (9.6) |
subtype1 | 27 | 66.6 (9.5) |
subtype2 | 13 | 64.5 (12.5) |
subtype3 | 9 | 67.8 (6.4) |
subtype4 | 15 | 62.7 (8.5) |
Figure S20. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
![](D3V2.png)
P value = 0.405 (Fisher's exact test)
Table S24. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 30 | 36 |
subtype1 | 10 | 17 |
subtype2 | 9 | 5 |
subtype3 | 5 | 5 |
subtype4 | 6 | 9 |
Figure S21. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
![](D3V3.png)
P value = 0.207 (ANOVA)
Table S25. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 7 | 80.0 (36.1) |
subtype2 | 4 | 65.0 (43.6) |
subtype4 | 3 | 100.0 (0.0) |
Figure S22. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D3V4.png)
P value = 0.252 (Chi-square test)
Table S26. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MICROPAPILLARY ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA |
---|---|---|---|---|---|
ALL | 15 | 48 | 1 | 1 | 1 |
subtype1 | 5 | 22 | 0 | 0 | 0 |
subtype2 | 5 | 8 | 1 | 0 | 0 |
subtype3 | 2 | 7 | 0 | 0 | 1 |
subtype4 | 3 | 11 | 0 | 1 | 0 |
Figure S23. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D3V5.png)
P value = 0.477 (Chi-square test)
Table S27. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 16 | 41 | 6 | 3 |
subtype1 | 6 | 18 | 3 | 0 |
subtype2 | 5 | 6 | 1 | 2 |
subtype3 | 2 | 8 | 0 | 0 |
subtype4 | 3 | 9 | 2 | 1 |
Figure S24. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D3V6.png)
P value = 0.899 (Chi-square test)
Table S28. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 39 | 15 | 10 |
subtype1 | 15 | 6 | 4 |
subtype2 | 7 | 4 | 3 |
subtype3 | 6 | 3 | 1 |
subtype4 | 11 | 2 | 2 |
Figure S25. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D3V7.png)
P value = 0.825 (Fisher's exact test)
Table S29. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 |
---|---|---|
ALL | 59 | 6 |
subtype1 | 24 | 3 |
subtype2 | 13 | 1 |
subtype3 | 9 | 0 |
subtype4 | 13 | 2 |
Figure S26. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D3V8.png)
P value = 0.505 (Fisher's exact test)
Table S30. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 62 | 4 |
subtype1 | 25 | 2 |
subtype2 | 14 | 0 |
subtype3 | 10 | 0 |
subtype4 | 13 | 2 |
Figure S27. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D3V9.png)
P value = 0.0871 (Fisher's exact test)
Table S31. Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 54 | 12 |
subtype1 | 21 | 6 |
subtype2 | 14 | 0 |
subtype3 | 9 | 1 |
subtype4 | 10 | 5 |
Figure S28. Get High-res Image Clustering Approach #3: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D3V10.png)
Table S32. Get Full Table Description of clustering approach #4: 'RNAseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 43 | 36 | 27 | 23 |
P value = 0.00518 (logrank test)
Table S33. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 115 | 29 | 0.0 - 85.3 (13.2) |
subtype1 | 38 | 4 | 0.1 - 85.3 (11.7) |
subtype2 | 33 | 12 | 0.0 - 56.8 (12.2) |
subtype3 | 24 | 6 | 0.1 - 83.8 (23.5) |
subtype4 | 20 | 7 | 0.0 - 45.2 (8.9) |
Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D4V1.png)
P value = 0.295 (ANOVA)
Table S34. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 118 | 65.7 (9.8) |
subtype1 | 38 | 67.2 (8.3) |
subtype2 | 33 | 64.4 (11.9) |
subtype3 | 25 | 63.4 (10.0) |
subtype4 | 22 | 67.6 (7.9) |
Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D4V2.png)
P value = 0.0255 (Fisher's exact test)
Table S35. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 54 | 75 |
subtype1 | 12 | 31 |
subtype2 | 13 | 23 |
subtype3 | 16 | 11 |
subtype4 | 13 | 10 |
Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D4V3.png)
P value = 0.24 (Chi-square test)
Table S36. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|
ALL | 2 | 28 | 86 | 1 | 3 | 2 | 2 | 3 | 1 | 1 |
subtype1 | 1 | 6 | 32 | 1 | 2 | 0 | 1 | 0 | 0 | 0 |
subtype2 | 0 | 6 | 28 | 0 | 0 | 1 | 0 | 1 | 0 | 0 |
subtype3 | 1 | 9 | 14 | 0 | 1 | 1 | 0 | 0 | 1 | 0 |
subtype4 | 0 | 7 | 12 | 0 | 0 | 0 | 1 | 2 | 0 | 1 |
Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D4V5.png)
P value = 0.0399 (Chi-square test)
Table S37. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 35 | 77 | 11 | 5 |
subtype1 | 16 | 25 | 1 | 0 |
subtype2 | 9 | 20 | 4 | 3 |
subtype3 | 7 | 19 | 1 | 0 |
subtype4 | 3 | 13 | 5 | 2 |
Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D4V6.png)
P value = 0.0177 (Chi-square test)
Table S38. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 80 | 26 | 20 |
subtype1 | 30 | 6 | 5 |
subtype2 | 20 | 10 | 5 |
subtype3 | 22 | 2 | 3 |
subtype4 | 8 | 8 | 7 |
Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D4V7.png)
P value = 0.606 (Chi-square test)
Table S39. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 97 | 6 | 24 |
subtype1 | 32 | 2 | 7 |
subtype2 | 29 | 0 | 7 |
subtype3 | 18 | 2 | 7 |
subtype4 | 18 | 2 | 3 |
Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D4V8.png)
P value = 0.866 (Fisher's exact test)
Table S40. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 122 | 7 |
subtype1 | 41 | 2 |
subtype2 | 34 | 2 |
subtype3 | 26 | 1 |
subtype4 | 21 | 2 |
Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D4V9.png)
P value = 0.667 (Fisher's exact test)
Table S41. Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 110 | 19 |
subtype1 | 38 | 5 |
subtype2 | 30 | 6 |
subtype3 | 24 | 3 |
subtype4 | 18 | 5 |
Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D4V10.png)
Table S42. Get Full Table Description of clustering approach #5: 'RNAseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 27 | 47 | 33 | 22 |
P value = 0.08 (logrank test)
Table S43. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 115 | 29 | 0.0 - 85.3 (13.2) |
subtype1 | 25 | 6 | 0.0 - 83.8 (23.1) |
subtype2 | 42 | 8 | 0.1 - 85.3 (14.5) |
subtype3 | 30 | 10 | 0.0 - 56.8 (8.2) |
subtype4 | 18 | 5 | 0.1 - 45.2 (8.9) |
Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D5V1.png)
P value = 0.0861 (ANOVA)
Table S44. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 118 | 65.7 (9.8) |
subtype1 | 25 | 62.8 (10.1) |
subtype2 | 40 | 66.8 (7.9) |
subtype3 | 32 | 64.2 (12.1) |
subtype4 | 21 | 69.4 (7.4) |
Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D5V2.png)
P value = 0.0538 (Fisher's exact test)
Table S45. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 54 | 75 |
subtype1 | 16 | 11 |
subtype2 | 15 | 32 |
subtype3 | 11 | 22 |
subtype4 | 12 | 10 |
Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D5V3.png)
P value = 0.402 (Chi-square test)
Table S46. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ACINAR ADENOCARCINOMA | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG MUCINOUS ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA |
---|---|---|---|---|---|---|---|---|---|---|
ALL | 2 | 28 | 86 | 1 | 3 | 2 | 2 | 3 | 1 | 1 |
subtype1 | 1 | 7 | 16 | 0 | 1 | 1 | 0 | 0 | 1 | 0 |
subtype2 | 1 | 6 | 34 | 1 | 2 | 1 | 1 | 1 | 0 | 0 |
subtype3 | 0 | 6 | 26 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
subtype4 | 0 | 9 | 10 | 0 | 0 | 0 | 1 | 1 | 0 | 1 |
Figure S41. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D5V5.png)
P value = 0.213 (Chi-square test)
Table S47. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 35 | 77 | 11 | 5 |
subtype1 | 6 | 20 | 1 | 0 |
subtype2 | 17 | 26 | 2 | 1 |
subtype3 | 8 | 18 | 4 | 3 |
subtype4 | 4 | 13 | 4 | 1 |
Figure S42. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D5V6.png)
P value = 0.532 (Chi-square test)
Table S48. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 80 | 26 | 20 |
subtype1 | 20 | 3 | 4 |
subtype2 | 31 | 9 | 5 |
subtype3 | 17 | 9 | 6 |
subtype4 | 12 | 5 | 5 |
Figure S43. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D5V7.png)
P value = 0.475 (Chi-square test)
Table S49. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 97 | 6 | 24 |
subtype1 | 18 | 2 | 7 |
subtype2 | 35 | 3 | 7 |
subtype3 | 25 | 0 | 8 |
subtype4 | 19 | 1 | 2 |
Figure S44. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D5V8.png)
P value = 0.949 (Fisher's exact test)
Table S50. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 122 | 7 |
subtype1 | 25 | 2 |
subtype2 | 45 | 2 |
subtype3 | 31 | 2 |
subtype4 | 21 | 1 |
Figure S45. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D5V9.png)
P value = 0.403 (Fisher's exact test)
Table S51. Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 110 | 19 |
subtype1 | 23 | 4 |
subtype2 | 43 | 4 |
subtype3 | 26 | 7 |
subtype4 | 18 | 4 |
Figure S46. Get High-res Image Clustering Approach #5: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D5V10.png)
Table S52. Get Full Table Description of clustering approach #6: 'MIRseq CNMF subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 35 | 31 | 29 |
P value = 0.703 (logrank test)
Table S53. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 80 | 27 | 0.0 - 76.2 (15.5) |
subtype1 | 28 | 10 | 0.1 - 49.0 (21.9) |
subtype2 | 26 | 9 | 0.0 - 60.0 (10.8) |
subtype3 | 26 | 8 | 0.5 - 76.2 (13.6) |
Figure S47. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'
![](D6V1.png)
P value = 0.248 (ANOVA)
Table S54. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 65.2 (9.7) |
subtype1 | 32 | 64.7 (9.6) |
subtype2 | 22 | 63.1 (10.4) |
subtype3 | 25 | 67.7 (9.0) |
Figure S48. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'
![](D6V2.png)
P value = 0.437 (Fisher's exact test)
Table S55. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 43 | 52 |
subtype1 | 15 | 20 |
subtype2 | 17 | 14 |
subtype3 | 11 | 18 |
Figure S49. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'
![](D6V3.png)
P value = 0.0393 (Chi-square test)
Table S56. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA |
---|---|---|---|---|---|---|
ALL | 19 | 68 | 2 | 1 | 3 | 2 |
subtype1 | 13 | 21 | 0 | 0 | 1 | 0 |
subtype2 | 3 | 25 | 1 | 0 | 0 | 2 |
subtype3 | 3 | 22 | 1 | 1 | 2 | 0 |
Figure S50. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D6V5.png)
P value = 0.182 (Chi-square test)
Table S57. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 27 | 59 | 6 | 3 |
subtype1 | 9 | 22 | 1 | 3 |
subtype2 | 8 | 19 | 4 | 0 |
subtype3 | 10 | 18 | 1 | 0 |
Figure S51. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D6V6.png)
P value = 0.587 (Chi-square test)
Table S58. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 55 | 22 | 16 |
subtype1 | 19 | 9 | 6 |
subtype2 | 16 | 9 | 6 |
subtype3 | 20 | 4 | 4 |
Figure S52. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D6V7.png)
P value = 0.104 (Chi-square test)
Table S59. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 76 | 5 | 13 |
subtype1 | 28 | 3 | 3 |
subtype2 | 23 | 0 | 8 |
subtype3 | 25 | 2 | 2 |
Figure S53. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D6V8.png)
P value = 0.458 (Fisher's exact test)
Table S60. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 91 | 4 |
subtype1 | 33 | 2 |
subtype2 | 31 | 0 |
subtype3 | 27 | 2 |
Figure S54. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D6V9.png)
P value = 0.686 (Fisher's exact test)
Table S61. Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 83 | 12 |
subtype1 | 29 | 6 |
subtype2 | 28 | 3 |
subtype3 | 26 | 3 |
Figure S55. Get High-res Image Clustering Approach #6: 'MIRseq CNMF subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D6V10.png)
Table S62. Get Full Table Description of clustering approach #7: 'MIRseq cHierClus subtypes'
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 25 | 38 | 32 |
P value = 0.585 (logrank test)
Table S63. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 80 | 27 | 0.0 - 76.2 (15.5) |
subtype1 | 19 | 7 | 0.0 - 47.0 (25.0) |
subtype2 | 33 | 10 | 0.5 - 76.2 (14.1) |
subtype3 | 28 | 10 | 0.0 - 60.0 (10.8) |
Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'
![](D7V1.png)
P value = 0.0645 (ANOVA)
Table S64. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 79 | 65.2 (9.7) |
subtype1 | 22 | 64.8 (10.0) |
subtype2 | 33 | 67.9 (8.7) |
subtype3 | 24 | 61.9 (10.0) |
Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'
![](D7V2.png)
P value = 0.371 (Fisher's exact test)
Table S65. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
nPatients | FEMALE | MALE |
---|---|---|
ALL | 43 | 52 |
subtype1 | 12 | 13 |
subtype2 | 14 | 24 |
subtype3 | 17 | 15 |
Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'
![](D7V3.png)
P value = 0.43 (ANOVA)
Table S66. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 6 | 75.0 (37.3) |
subtype2 | 3 | 60.0 (52.9) |
subtype3 | 3 | 90.0 (0.0) |
Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'
![](D7V4.png)
P value = 0.0939 (Chi-square test)
Table S67. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
nPatients | LUNG ADENOCARCINOMA MIXED SUBTYPE | LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS) | LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS | LUNG CLEAR CELL ADENOCARCINOMA | LUNG PAPILLARY ADENOCARCINOMA | MUCINOUS (COLLOID) ADENOCARCINOMA |
---|---|---|---|---|---|---|
ALL | 19 | 68 | 2 | 1 | 3 | 2 |
subtype1 | 10 | 14 | 0 | 0 | 1 | 0 |
subtype2 | 6 | 28 | 1 | 1 | 2 | 0 |
subtype3 | 3 | 26 | 1 | 0 | 0 | 2 |
Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'
![](D7V5.png)
P value = 0.674 (Chi-square test)
Table S68. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
nPatients | T1 | T2 | T3 | T4 |
---|---|---|---|---|
ALL | 27 | 59 | 6 | 3 |
subtype1 | 7 | 15 | 1 | 2 |
subtype2 | 12 | 23 | 2 | 1 |
subtype3 | 8 | 21 | 3 | 0 |
Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.T'
![](D7V6.png)
P value = 0.617 (Chi-square test)
Table S69. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
nPatients | N0 | N1 | N2+N3 |
---|---|---|---|
ALL | 55 | 22 | 16 |
subtype1 | 12 | 7 | 5 |
subtype2 | 24 | 9 | 4 |
subtype3 | 19 | 6 | 7 |
Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY.N'
![](D7V7.png)
P value = 0.0218 (Chi-square test)
Table S70. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
nPatients | M0 | M1 | MX |
---|---|---|---|
ALL | 76 | 5 | 13 |
subtype1 | 19 | 2 | 3 |
subtype2 | 34 | 3 | 1 |
subtype3 | 23 | 0 | 9 |
Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGICSPREAD(M)'
![](D7V8.png)
P value = 1 (Fisher's exact test)
Table S71. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
nPatients | NO | YES |
---|---|---|
ALL | 91 | 4 |
subtype1 | 24 | 1 |
subtype2 | 36 | 2 |
subtype3 | 31 | 1 |
Figure S64. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'
![](D7V9.png)
P value = 0.629 (Fisher's exact test)
Table S72. Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
nPatients | NO | YES |
---|---|---|
ALL | 83 | 12 |
subtype1 | 23 | 2 |
subtype2 | 32 | 6 |
subtype3 | 28 | 4 |
Figure S65. Get High-res Image Clustering Approach #7: 'MIRseq cHierClus subtypes' versus Clinical Feature #10: 'NEOADJUVANT.THERAPY'
![](D7V10.png)
-
Cluster data file = LUAD.mergedcluster.txt
-
Clinical data file = LUAD.clin.merged.picked.txt
-
Number of patients = 164
-
Number of clustering approaches = 7
-
Number of selected clinical features = 10
-
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
This is an experimental feature. Location of data archives could not be determined.