Head and Neck Squamous Cell Carcinoma: Correlation between molecular cancer subtypes and selected clinical features
Maintained by TCGA GDAC Team (Broad Institute/Dana-Farber Cancer Institute/Harvard Medical School)
Overview
Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 8 different clustering approaches and 8 clinical features across 311 patients, 9 significant findings detected with P value < 0.05.

- 5 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.

- 3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER' and 'PATHOLOGY.T'.

- CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGY.N', and 'NEOADJUVANT.THERAPY'.

- Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'NEOADJUVANT.THERAPY'.

- CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

- Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 4 subtypes that correlate to 'NEOADJUVANT.THERAPY'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 9 significant findings detected.

Clinical Features | Time to Death | AGE | GENDER | PATHOLOGY T | PATHOLOGY N | TUMOR STAGE | RADIATIONS RADIATION REGIMENINDICATION | NEOADJUVANT THERAPY
Statistical Tests | logrank test | ANOVA | Fisher's exact test | Chi-square test | Chi-square test | Chi-square test | Fisher's exact test | Fisher's exact test
CN CNMF | 0.00367 | 0.0214 | 0.178 | 0.686 | 0.153 | 0.253 | 0.436 | 0.288
METHLYATION CNMF | 0.26 | 0.071 | 0.00615 | 0.0407 | 0.549 | 0.569 | 0.65 | 0.922
RPPA CNMF subtypes | 0.0351 | 0.434 | 0.139 | 0.216 | 0.0063 | 0.0532 | 0.301 | 0.023
RPPA cHierClus subtypes | 0.103 | 0.303 | 0.842 | 0.194 | 0.0534 | 0.103 | 0.595 | 0.0821
RNAseq CNMF subtypes | 0.15 | 0.846 | 0.307 | 0.789 | 0.182 | 0.457 | 0.71 | 0.218
RNAseq cHierClus subtypes | 0.119 | 0.33 | 0.372 | 0.172 | 0.0884 | 0.361 | 0.441 | 0.0304
MIRseq CNMF subtypes | 0.72 | 0.723 | 0.0643 | 0.121 | 0.109 | 0.451 | 0.533 | 0.689
MIRseq cHierClus subtypes | 0.161 | 0.688 | 0.637 | 0.395 | 0.191 | 0.321 | 0.551 | 0.0179

Clustering Approach #1: 'CN CNMF'

Table S1. Get Full Table Description of clustering approach #1: 'CN CNMF'

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 66 | 75 | 36 | 101 | 10

'CN CNMF' versus 'Time to Death'

P value = 0.00367 (logrank test)

Table S2. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 285 | 119 | 0.1 - 210.9 (14.3)
subtype1 | 66 | 35 | 0.2 - 142.5 (12.1)
subtype2 | 74 | 32 | 0.1 - 129.2 (18.0)
subtype3 | 36 | 15 | 0.1 - 111.1 (11.6)
subtype4 | 99 | 33 | 0.8 - 210.9 (17.1)
subtype5 | 10 | 4 | 3.1 - 89.8 (11.2)

Figure S1. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #1: 'Time to Death'

'CN CNMF' versus 'AGE'

P value = 0.0214 (ANOVA)

Table S3. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 288 | 61.2 (12.2)
subtype1 | 66 | 61.8 (11.8)
subtype2 | 75 | 57.7 (13.2)
subtype3 | 36 | 60.8 (10.0)
subtype4 | 101 | 63.8 (12.3)
subtype5 | 10 | 59.5 (7.4)

Figure S2. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #2: 'AGE'

'CN CNMF' versus 'GENDER'

P value = 0.178 (Chi-square test)

Table S4. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 81 | 207
subtype1 | 15 | 51
subtype2 | 16 | 59
subtype3 | 10 | 26
subtype4 | 37 | 64
subtype5 | 3 | 7

Figure S3. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #3: 'GENDER'

'CN CNMF' versus 'PATHOLOGY.T'

P value = 0.686 (Chi-square test)

Table S5. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 21 | 74 | 60 | 98
subtype1 | 5 | 13 | 12 | 25
subtype2 | 3 | 21 | 21 | 24
subtype3 | 2 | 7 | 7 | 13
subtype4 | 11 | 30 | 17 | 32
subtype5 | 0 | 3 | 3 | 4

Figure S4. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'CN CNMF' versus 'PATHOLOGY.N'

P value = 0.153 (Chi-square test)

Table S6. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 95 | 31 | 94 | 4
subtype1 | 13 | 5 | 31 | 2
subtype2 | 27 | 10 | 24 | 1
subtype3 | 11 | 3 | 11 | 1
subtype4 | 41 | 11 | 25 | 0
subtype5 | 3 | 2 | 3 | 0

Figure S5. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'CN CNMF' versus 'TUMOR.STAGE'

P value = 0.253 (Chi-square test)

Table S7. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 15 | 45 | 40 | 148
subtype1 | 4 | 5 | 5 | 40
subtype2 | 3 | 12 | 16 | 38
subtype3 | 2 | 4 | 3 | 20
subtype4 | 6 | 21 | 15 | 44
subtype5 | 0 | 3 | 1 | 6

Figure S6. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

'CN CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.436 (Chi-square test)

Table S8. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 76 | 212
subtype1 | 23 | 43
subtype2 | 20 | 55
subtype3 | 9 | 27
subtype4 | 22 | 79
subtype5 | 2 | 8

Figure S7. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'CN CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.288 (Chi-square test)

Table S9. Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 47 | 241
subtype1 | 16 | 50
subtype2 | 13 | 62
subtype3 | 5 | 31
subtype4 | 12 | 89
subtype5 | 1 | 9

Figure S8. Get High-res Image Clustering Approach #1: 'CN CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 94 | 101 | 97

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.26 (logrank test)

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 289 | 120 | 0.1 - 210.9 (14.3)
subtype1 | 93 | 41 | 0.1 - 126.1 (13.0)
subtype2 | 101 | 39 | 0.1 - 142.5 (16.6)
subtype3 | 95 | 40 | 0.2 - 210.9 (13.3)

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.071 (ANOVA)

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 292 | 61.3 (12.2)
subtype1 | 94 | 59.0 (13.2)
subtype2 | 101 | 61.9 (10.8)
subtype3 | 97 | 62.9 (12.3)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00615 (Fisher's exact test)

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 82 | 210
subtype1 | 20 | 74
subtype2 | 23 | 78
subtype3 | 39 | 58

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.0407 (Chi-square test)

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 21 | 76 | 61 | 99
subtype1 | 3 | 22 | 25 | 38
subtype2 | 5 | 26 | 22 | 26
subtype3 | 13 | 28 | 14 | 35

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.549 (Chi-square test)

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 96 | 31 | 97 | 4
subtype1 | 38 | 9 | 31 | 0
subtype2 | 28 | 8 | 31 | 2
subtype3 | 30 | 14 | 35 | 2

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.569 (Chi-square test)

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 15 | 46 | 40 | 151
subtype1 | 3 | 16 | 15 | 54
subtype2 | 3 | 15 | 13 | 45
subtype3 | 9 | 15 | 12 | 52

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'TUMOR.STAGE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.65 (Fisher's exact test)

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 78 | 214
subtype1 | 22 | 72
subtype2 | 28 | 73
subtype3 | 28 | 69

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.922 (Fisher's exact test)

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 48 | 244
subtype1 | 15 | 79
subtype2 | 18 | 83
subtype3 | 15 | 82

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S19. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 82 | 85 | 45

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0351 (logrank test)

Table S20. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 212 | 107 | 0.1 - 210.9 (13.2)
subtype1 | 82 | 42 | 1.5 - 129.2 (12.1)
subtype2 | 85 | 47 | 0.1 - 210.9 (13.1)
subtype3 | 45 | 18 | 2.1 - 156.5 (20.5)

Figure S17. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.434 (ANOVA)

Table S21. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 62.1 (12.2)
subtype1 | 82 | 61.1 (12.1)
subtype2 | 85 | 63.4 (12.1)
subtype3 | 45 | 61.4 (12.7)

Figure S18. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.139 (Fisher's exact test)

Table S22. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 150
subtype1 | 19 | 63
subtype2 | 25 | 60
subtype3 | 18 | 27

Figure S19. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.216 (Chi-square test)

Table S23. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 13 | 59 | 53 | 79
subtype1 | 3 | 24 | 16 | 34
subtype2 | 6 | 18 | 27 | 32
subtype3 | 4 | 17 | 10 | 13

Figure S20. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.0063 (Chi-square test)

Table S24. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 73 | 20 | 79 | 4
subtype1 | 27 | 9 | 30 | 1
subtype2 | 19 | 8 | 40 | 2
subtype3 | 27 | 3 | 9 | 1

Figure S21. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RPPA CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0532 (Chi-square test)

Table S25. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 9 | 39 | 31 | 121
subtype1 | 1 | 17 | 12 | 47
subtype2 | 4 | 9 | 12 | 55
subtype3 | 4 | 13 | 7 | 19

Figure S22. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.301 (Fisher's exact test)

Table S26. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 56 | 156
subtype1 | 25 | 57
subtype2 | 23 | 62
subtype3 | 8 | 37

Figure S23. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.023 (Fisher's exact test)

Table S27. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 36 | 176
subtype1 | 18 | 64
subtype2 | 16 | 69
subtype3 | 2 | 43

Figure S24. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S28. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 115 | 81 | 16

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.103 (logrank test)

Table S29. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 212 | 107 | 0.1 - 210.9 (13.2)
subtype1 | 115 | 56 | 0.1 - 210.9 (13.2)
subtype2 | 81 | 42 | 1.5 - 156.5 (13.6)
subtype3 | 16 | 9 | 3.3 - 52.3 (11.6)

Figure S25. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.303 (ANOVA)

Table S30. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 212 | 62.1 (12.2)
subtype1 | 115 | 63.3 (11.2)
subtype2 | 81 | 61.1 (13.1)
subtype3 | 16 | 59.4 (14.3)

Figure S26. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.842 (Fisher's exact test)

Table S31. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 62 | 150
subtype1 | 35 | 80
subtype2 | 22 | 59
subtype3 | 5 | 11

Figure S27. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.194 (Chi-square test)

Table S32. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 13 | 59 | 53 | 79
subtype1 | 7 | 25 | 36 | 42
subtype2 | 5 | 30 | 13 | 30
subtype3 | 1 | 4 | 4 | 7

Figure S28. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0534 (Chi-square test)

Table S33. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 73 | 20 | 79 | 4
subtype1 | 39 | 8 | 46 | 3
subtype2 | 32 | 11 | 22 | 1
subtype3 | 2 | 1 | 11 | 0

Figure S29. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RPPA cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.103 (Chi-square test)

Table S34. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 9 | 39 | 31 | 121
subtype1 | 6 | 16 | 18 | 69
subtype2 | 3 | 21 | 13 | 39
subtype3 | 0 | 2 | 0 | 13

Figure S30. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.595 (Fisher's exact test)

Table S35. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 56 | 156
subtype1 | 30 | 85
subtype2 | 20 | 61
subtype3 | 6 | 10

Figure S31. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0821 (Fisher's exact test)

Table S36. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 36 | 176
subtype1 | 19 | 96
subtype2 | 11 | 70
subtype3 | 6 | 10

Figure S32. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S37. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 107 | 88 | 91

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.15 (logrank test)

Table S38. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 283 | 119 | 0.1 - 210.9 (14.8)
subtype1 | 107 | 41 | 0.1 - 135.3 (13.3)
subtype2 | 86 | 40 | 0.2 - 142.5 (15.7)
subtype3 | 90 | 38 | 0.1 - 210.9 (15.3)

Figure S33. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.846 (ANOVA)

Table S39. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 286 | 61.3 (12.2)
subtype1 | 107 | 61.8 (11.1)
subtype2 | 88 | 61.1 (12.7)
subtype3 | 91 | 60.9 (13.2)

Figure S34. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.307 (Fisher's exact test)

Table S40. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 78 | 208
subtype1 | 24 | 83
subtype2 | 25 | 63
subtype3 | 29 | 62

Figure S35. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.789 (Chi-square test)

Table S41. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 20 | 76 | 58 | 98
subtype1 | 5 | 24 | 23 | 33
subtype2 | 9 | 25 | 18 | 29
subtype3 | 6 | 27 | 17 | 36

Figure S36. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.182 (Chi-square test)

Table S42. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 95 | 31 | 94 | 4
subtype1 | 33 | 9 | 29 | 2
subtype2 | 24 | 10 | 41 | 1
subtype3 | 38 | 12 | 24 | 1

Figure S37. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.457 (Chi-square test)

Table S43. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 14 | 46 | 39 | 148
subtype1 | 2 | 15 | 16 | 49
subtype2 | 7 | 12 | 10 | 51
subtype3 | 5 | 19 | 13 | 48

Figure S38. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.71 (Fisher's exact test)

Table S44. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 211
subtype1 | 26 | 81
subtype2 | 26 | 62
subtype3 | 23 | 68

Figure S39. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.218 (Fisher's exact test)

Table S45. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 46 | 240
subtype1 | 18 | 89
subtype2 | 18 | 70
subtype3 | 10 | 81

Figure S40. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S46. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 86 | 101 | 99

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.119 (logrank test)

Table S47. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 283 | 119 | 0.1 - 210.9 (14.8)
subtype1 | 86 | 30 | 0.1 - 135.3 (14.6)
subtype2 | 100 | 44 | 0.1 - 210.9 (14.4)
subtype3 | 97 | 45 | 1.5 - 142.5 (15.1)

Figure S41. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.33 (ANOVA)

Table S48. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 286 | 61.3 (12.2)
subtype1 | 86 | 62.6 (10.8)
subtype2 | 101 | 61.5 (12.4)
subtype3 | 99 | 59.9 (13.2)

Figure S42. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.372 (Fisher's exact test)

Table S49. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 78 | 208
subtype1 | 19 | 67
subtype2 | 28 | 73
subtype3 | 31 | 68

Figure S43. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.172 (Chi-square test)

Table S50. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 20 | 76 | 58 | 98
subtype1 | 4 | 20 | 16 | 26
subtype2 | 3 | 29 | 19 | 41
subtype3 | 13 | 27 | 23 | 31

Figure S44. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0884 (Chi-square test)

Table S51. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 95 | 31 | 94 | 4
subtype1 | 22 | 7 | 25 | 2
subtype2 | 44 | 12 | 24 | 1
subtype3 | 29 | 12 | 45 | 1

Figure S45. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.361 (Chi-square test)

Table S52. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 14 | 46 | 39 | 148
subtype1 | 2 | 12 | 9 | 40
subtype2 | 3 | 20 | 17 | 51
subtype3 | 9 | 14 | 13 | 57

Figure S46. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.441 (Fisher's exact test)

Table S53. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 75 | 211
subtype1 | 24 | 62
subtype2 | 22 | 79
subtype3 | 29 | 70

Figure S47. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0304 (Fisher's exact test)

Table S54. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 46 | 240
subtype1 | 15 | 71
subtype2 | 9 | 92
subtype3 | 22 | 77

Figure S48. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S55. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 83 | 131 | 94

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.72 (logrank test)

Table S56. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 305 | 122 | 0.1 - 210.9 (15.0)
subtype1 | 82 | 30 | 0.1 - 210.9 (12.1)
subtype2 | 131 | 52 | 0.1 - 142.5 (14.3)
subtype3 | 92 | 40 | 1.8 - 126.1 (18.1)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.723 (ANOVA)

Table S57. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 308 | 61.1 (12.1)
subtype1 | 83 | 60.4 (12.5)
subtype2 | 131 | 61.7 (11.4)
subtype3 | 94 | 60.9 (12.7)

Figure S50. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.0643 (Fisher's exact test)

Table S58. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 222
subtype1 | 16 | 67
subtype2 | 37 | 94
subtype3 | 33 | 61

Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.121 (Chi-square test)

Table S59. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 78 | 62 | 103
subtype1 | 5 | 23 | 11 | 30
subtype2 | 7 | 26 | 29 | 47
subtype3 | 12 | 29 | 22 | 26

Figure S52. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.109 (Chi-square test)

Table S60. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 99 | 32 | 101 | 5
subtype1 | 30 | 8 | 21 | 2
subtype2 | 44 | 9 | 41 | 3
subtype3 | 25 | 15 | 39 | 0

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.451 (Chi-square test)

Table S61. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 17 | 46 | 41 | 158
subtype1 | 4 | 15 | 8 | 41
subtype2 | 4 | 16 | 18 | 68
subtype3 | 9 | 15 | 15 | 49

Figure S54. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.533 (Fisher's exact test)

Table S62. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 77 | 231
subtype1 | 21 | 62
subtype2 | 29 | 102
subtype3 | 27 | 67

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.689 (Fisher's exact test)

Table S63. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 48 | 260
subtype1 | 11 | 72
subtype2 | 20 | 111
subtype3 | 17 | 77

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 13 | 12 | 167 | 116

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.161 (logrank test)

Table S65. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 305 | 122 | 0.1 - 210.9 (15.0)
subtype1 | 13 | 5 | 0.2 - 56.7 (12.0)
subtype2 | 12 | 5 | 9.0 - 84.4 (12.8)
subtype3 | 166 | 61 | 0.1 - 210.9 (14.0)
subtype4 | 114 | 51 | 0.5 - 156.5 (17.1)

Figure S57. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.688 (ANOVA)

Table S66. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 308 | 61.1 (12.1)
subtype1 | 13 | 58.4 (13.2)
subtype2 | 12 | 61.0 (11.8)
subtype3 | 167 | 61.8 (11.5)
subtype4 | 116 | 60.4 (12.9)

Figure S58. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.637 (Fisher's exact test)

Table S67. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 86 | 222
subtype1 | 4 | 9
subtype2 | 4 | 8
subtype3 | 42 | 125
subtype4 | 36 | 80

Figure S59. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.395 (Chi-square test)

Table S68. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 24 | 78 | 62 | 103
subtype1 | 2 | 5 | 2 | 3
subtype2 | 0 | 3 | 2 | 5
subtype3 | 8 | 38 | 30 | 60
subtype4 | 14 | 32 | 28 | 35

Figure S60. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.191 (Chi-square test)

Table S69. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 99 | 32 | 101 | 5
subtype1 | 8 | 1 | 3 | 0
subtype2 | 5 | 0 | 5 | 0
subtype3 | 54 | 17 | 42 | 4
subtype4 | 32 | 14 | 51 | 1

Figure S61. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

'MIRseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.321 (Chi-square test)

Table S70. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

nPatients | I | II | III | IV
--- | --- | --- | --- | ---
ALL | 17 | 46 | 41 | 158
subtype1 | 2 | 4 | 2 | 4
subtype2 | 0 | 1 | 1 | 8
subtype3 | 5 | 24 | 23 | 80
subtype4 | 10 | 17 | 15 | 66

Figure S62. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'TUMOR.STAGE'

'MIRseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.551 (Fisher's exact test)

Table S71. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 77 | 231
subtype1 | 3 | 10
subtype2 | 4 | 8
subtype3 | 37 | 130
subtype4 | 33 | 83

Figure S63. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0179 (Fisher's exact test)

Table S72. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 48 | 260
subtype1 | 0 | 13
subtype2 | 3 | 9
subtype3 | 19 | 148
subtype4 | 26 | 90

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = HNSC.mergedcluster.txt

- Clinical data file = HNSC.clin.merged.picked.txt

- Number of patients = 311

- Number of clustering approaches = 8

- Number of selected clinical features = 8

- Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches
CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

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

ANOVA analysis

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

Chi-square test

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

Fisher's exact test

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

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)
[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
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