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 4 different clustering approaches and 8 clinical features across 259 patients, one significant finding detected with P value < 0.05.

- 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 'PATHOLOGY.N'.

- 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 3 subtypes that do not correlate to any clinical features.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 4 different clustering approaches and 8 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.

Clinical Features | Statistical Tests | RNAseq CNMF subtypes | RNAseq cHierClus subtypes | MIRseq CNMF subtypes | MIRseq cHierClus subtypes
Time to Death | logrank test | 0.182 | 0.0678 | 0.538 | 0.481
AGE | ANOVA | 0.483 | 0.245 | 0.511 | 0.247
GENDER | Fisher's exact test | 0.31 | 0.124 | 0.275 | 0.237
PATHOLOGY T | Chi-square test | 0.5 | 0.073 | 0.603 | 0.21
PATHOLOGY N | Chi-square test | 0.136 | 0.0148 | 0.652 | 0.612
PATHOLOGICSPREAD(M) | Fisher's exact test | 1 | 0.54 | 1 | 1
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.727 | 0.209 | 0.173 | 0.842
NEOADJUVANT THERAPY | Fisher's exact test | 0.311 | 0.0851 | 0.507 | 0.631

Clustering Approach #1: 'RNAseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 98 | 69 | 85

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.182 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 250 | 98 | 0.1 - 210.9 (13.8)
subtype1 | 98 | 35 | 0.1 - 135.3 (12.4)
subtype2 | 68 | 30 | 0.2 - 142.5 (15.4)
subtype3 | 84 | 33 | 0.1 - 210.9 (16.9)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.483 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 252 | 61.0 (12.1)
subtype1 | 98 | 62.2 (11.2)
subtype2 | 69 | 60.1 (12.4)
subtype3 | 85 | 60.4 (12.7)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.31 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 185 | 67
subtype1 | 77 | 21
subtype2 | 49 | 20
subtype3 | 59 | 26

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.5 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 16 | 70 | 65 | 91
subtype1 | 3 | 27 | 30 | 31
subtype2 | 6 | 21 | 15 | 26
subtype3 | 7 | 22 | 20 | 34

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.136 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 100 | 40 | 92 | 3
subtype1 | 40 | 11 | 36 | 1
subtype2 | 20 | 12 | 33 | 1
subtype3 | 40 | 17 | 23 | 1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S7. Clustering Approach #1: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 249 | 2
subtype1 | 97 | 1
subtype2 | 68 | 0
subtype3 | 84 | 1

Figure S6. Get High-res Image Clustering Approach #1: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.727 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 184 | 68
subtype1 | 72 | 26
subtype2 | 48 | 21
subtype3 | 64 | 21

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

'RNAseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.311 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 42 | 210
subtype1 | 18 | 80
subtype2 | 14 | 55
subtype3 | 10 | 75

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

Clustering Approach #2: 'RNAseq cHierClus subtypes'

Table S10. Get Full Table Description of clustering approach #2: 'RNAseq cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 78 | 84 | 90

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0678 (logrank test)

Table S11. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 250 | 98 | 0.1 - 210.9 (13.8)
subtype1 | 78 | 25 | 0.1 - 135.3 (13.1)
subtype2 | 83 | 34 | 0.1 - 210.9 (17.9)
subtype3 | 89 | 39 | 0.2 - 142.5 (13.3)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.245 (ANOVA)

Table S12. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 252 | 61.0 (12.1)
subtype1 | 78 | 62.7 (11.2)
subtype2 | 84 | 61.1 (11.7)
subtype3 | 90 | 59.5 (13.0)

Figure S10. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.124 (Fisher's exact test)

Table S13. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 185 | 67
subtype1 | 63 | 15
subtype2 | 62 | 22
subtype3 | 60 | 30

Figure S11. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.073 (Chi-square test)

Table S14. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T'

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 16 | 70 | 65 | 91
subtype1 | 2 | 25 | 21 | 24
subtype2 | 3 | 20 | 20 | 38
subtype3 | 11 | 25 | 24 | 29

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0148 (Chi-square test)

Table S15. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N'

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 100 | 40 | 92 | 3
subtype1 | 27 | 8 | 33 | 1
subtype2 | 44 | 18 | 18 | 1
subtype3 | 29 | 14 | 41 | 1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.54 (Fisher's exact test)

Table S16. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 249 | 2
subtype1 | 77 | 1
subtype2 | 83 | 1
subtype3 | 89 | 0

Figure S14. Get High-res Image Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.209 (Fisher's exact test)

Table S17. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 184 | 68
subtype1 | 53 | 25
subtype2 | 67 | 17
subtype3 | 64 | 26

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

'RNAseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.0851 (Fisher's exact test)

Table S18. Clustering Approach #2: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 42 | 210
subtype1 | 15 | 63
subtype2 | 8 | 76
subtype3 | 19 | 71

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

Clustering Approach #3: 'MIRseq CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 32 | 31 | 26

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.538 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 89 | 27 | 1.5 - 135.3 (12.5)
subtype1 | 32 | 8 | 1.5 - 87.5 (10.7)
subtype2 | 31 | 10 | 4.6 - 135.3 (16.1)
subtype3 | 26 | 9 | 3.2 - 89.8 (13.2)

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

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.511 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 89 | 60.3 (12.6)
subtype1 | 32 | 60.8 (14.8)
subtype2 | 31 | 61.7 (12.1)
subtype3 | 26 | 58.0 (10.1)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.275 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 66 | 23
subtype1 | 23 | 9
subtype2 | 26 | 5
subtype3 | 17 | 9

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.603 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 3 | 22 | 25 | 38
subtype1 | 2 | 7 | 7 | 16
subtype2 | 0 | 9 | 11 | 10
subtype3 | 1 | 6 | 7 | 12

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

'MIRseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.652 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 30 | 16 | 39 | 3
subtype1 | 13 | 6 | 12 | 1
subtype2 | 11 | 3 | 15 | 1
subtype3 | 6 | 7 | 12 | 1

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

'MIRseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S25. Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 88 | 1
subtype1 | 31 | 1
subtype2 | 31 | 0
subtype3 | 26 | 0

Figure S22. Get High-res Image Clustering Approach #3: 'MIRseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.173 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 49 | 40
subtype1 | 21 | 11
subtype2 | 13 | 18
subtype3 | 15 | 11

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

'MIRseq CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.507 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 28 | 61
subtype1 | 8 | 24
subtype2 | 12 | 19
subtype3 | 8 | 18

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

Clustering Approach #4: 'MIRseq cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 35 | 48 | 6

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.481 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 89 | 27 | 1.5 - 135.3 (12.5)
subtype1 | 35 | 12 | 3.2 - 89.8 (13.1)
subtype2 | 48 | 13 | 1.5 - 135.3 (12.4)
subtype3 | 6 | 2 | 4.7 - 57.1 (8.6)

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

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.247 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 89 | 60.3 (12.6)
subtype1 | 35 | 58.0 (12.4)
subtype2 | 48 | 62.4 (12.8)
subtype3 | 6 | 57.2 (10.6)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.237 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 66 | 23
subtype1 | 25 | 10
subtype2 | 38 | 10
subtype3 | 3 | 3

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.21 (Chi-square test)

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

nPatients | T1 | T2 | T3 | T4
--- | --- | --- | --- | ---
ALL | 3 | 22 | 25 | 38
subtype1 | 2 | 9 | 8 | 16
subtype2 | 0 | 13 | 15 | 20
subtype3 | 1 | 0 | 2 | 2

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.612 (Chi-square test)

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

nPatients | N0 | N1 | N2 | N3
--- | --- | --- | --- | ---
ALL | 30 | 16 | 39 | 3
subtype1 | 9 | 7 | 18 | 1
subtype2 | 20 | 7 | 19 | 2
subtype3 | 1 | 2 | 2 | 0

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

'MIRseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 1 (Fisher's exact test)

Table S34. Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

nPatients | M0 | M1
--- | --- | ---
ALL | 88 | 1
subtype1 | 35 | 0
subtype2 | 47 | 1
subtype3 | 6 | 0

Figure S30. Get High-res Image Clustering Approach #4: 'MIRseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGICSPREAD(M)'

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

P value = 0.842 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 49 | 40
subtype1 | 18 | 17
subtype2 | 27 | 21
subtype3 | 4 | 2

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

'MIRseq cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.631 (Fisher's exact test)

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

nPatients | NO | YES
--- | --- | ---
ALL | 28 | 61
subtype1 | 13 | 22
subtype2 | 13 | 35
subtype3 | 2 | 4

Figure S32. Get High-res Image Clustering Approach #4: '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 = 259

- Number of clustering approaches = 4

- 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

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

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

Download Results

This is an experimental feature. Location of data archives could not be determined.

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] 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)
[6] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
Meta

- Maintainer = TCGA GDAC Team

Copyright © 2011 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
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