Glioblastoma Multiforme: 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 5 different clustering approaches and 6 clinical features across 527 patients, 7 significant findings detected with P value < 0.05.

- CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that correlate to 'AGE'.

- CNMF clustering analysis on array-based miR expression data identified 4 subtypes that correlate to 'Time to Death'.

- Consensus hierarchical clustering analysis on array-based miR expression data identified 3 subtypes that correlate to 'Time to Death' and 'AGE'.

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

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 5 different clustering approaches and 6 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, 7 significant findings detected.

Clinical Features | Statistical Tests | mRNA CNMF subtypes | mRNA cHierClus subtypes | miR CNMF subtypes | miR cHierClus subtypes | METHLYATION CNMF
Time to Death | logrank test | 0.157 | 0.056 | 0.000614 | 0.00455 | 0.00142
AGE | ANOVA | 0.0257 | 0.0276 | 0.101 | 0.000872 | 5.2e-08
GENDER | Fisher's exact test | 0.497 | 0.563 | 0.508 | 0.119 | 0.849
KARNOFSKY PERFORMANCE SCORE | ANOVA | 0.839 | 0.487 | 0.943 | 0.785 | 0.223
RADIATIONS RADIATION REGIMENINDICATION | Fisher's exact test | 0.259 | 0.122 | 0.638 | 0.758 | 0.176
NEOADJUVANT THERAPY | Fisher's exact test | 0.821 | 0.633 | 0.906 | 0.993 | 0.484

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 177 | 172 | 170

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.157 (logrank test)

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

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 519 | 403 | 0.1 - 127.6 (9.9)
subtype1 | 177 | 145 | 0.2 - 127.6 (10.0)
subtype2 | 172 | 129 | 0.2 - 108.8 (9.2)
subtype3 | 170 | 129 | 0.1 - 92.6 (10.7)

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

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.0257 (ANOVA)

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

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 519 | 57.7 (14.5)
subtype1 | 177 | 57.3 (12.8)
subtype2 | 172 | 55.8 (16.4)
subtype3 | 170 | 60.0 (13.7)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.497 (Fisher's exact test)

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

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 204 | 315
subtype1 | 70 | 107
subtype2 | 62 | 110
subtype3 | 72 | 98

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

'mRNA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.839 (ANOVA)

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 389 | 77.1 (14.4)
subtype1 | 137 | 77.5 (15.0)
subtype2 | 126 | 77.3 (13.0)
subtype3 | 126 | 76.5 (15.0)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.259 (Fisher's exact test)

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 348 | 171
subtype1 | 119 | 58
subtype2 | 108 | 64
subtype3 | 121 | 49

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.821 (Fisher's exact test)

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 270 | 249
subtype1 | 94 | 83
subtype2 | 86 | 86
subtype3 | 90 | 80

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S8. Get Full Table Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 223 | 127 | 169

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.056 (logrank test)

Table S9. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 519 | 403 | 0.1 - 127.6 (9.9)
subtype1 | 223 | 182 | 0.1 - 90.6 (10.4)
subtype2 | 127 | 94 | 0.1 - 92.6 (9.8)
subtype3 | 169 | 127 | 0.2 - 127.6 (9.4)

Figure S7. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.0276 (ANOVA)

Table S10. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 519 | 57.7 (14.5)
subtype1 | 223 | 57.7 (13.4)
subtype2 | 127 | 60.3 (13.8)
subtype3 | 169 | 55.7 (16.0)

Figure S8. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.563 (Fisher's exact test)

Table S11. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 204 | 315
subtype1 | 90 | 133
subtype2 | 53 | 74
subtype3 | 61 | 108

Figure S9. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'mRNA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.487 (ANOVA)

Table S12. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 389 | 77.1 (14.4)
subtype1 | 165 | 77.6 (14.4)
subtype2 | 98 | 75.6 (15.4)
subtype3 | 126 | 77.6 (13.5)

Figure S10. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.122 (Fisher's exact test)

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 348 | 171
subtype1 | 156 | 67
subtype2 | 89 | 38
subtype3 | 103 | 66

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.633 (Fisher's exact test)

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 270 | 249
subtype1 | 118 | 105
subtype2 | 69 | 58
subtype3 | 83 | 86

Figure S12. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #3: 'miR CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'miR CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 144 | 159 | 74 | 105

'miR CNMF subtypes' versus 'Time to Death'

P value = 0.000614 (logrank test)

Table S16. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 482 | 379 | 0.1 - 127.6 (10.3)
subtype1 | 144 | 116 | 0.1 - 51.3 (10.6)
subtype2 | 159 | 124 | 0.1 - 127.6 (10.6)
subtype3 | 74 | 57 | 0.1 - 53.8 (8.4)
subtype4 | 105 | 82 | 0.1 - 92.6 (10.8)

Figure S13. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'miR CNMF subtypes' versus 'AGE'

P value = 0.101 (ANOVA)

Table S17. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 482 | 57.5 (14.6)
subtype1 | 144 | 59.7 (11.4)
subtype2 | 159 | 55.5 (17.0)
subtype3 | 74 | 57.9 (15.3)
subtype4 | 105 | 57.4 (13.7)

Figure S14. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #2: 'AGE'

'miR CNMF subtypes' versus 'GENDER'

P value = 0.508 (Fisher's exact test)

Table S18. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 186 | 296
subtype1 | 56 | 88
subtype2 | 68 | 91
subtype3 | 26 | 48
subtype4 | 36 | 69

Figure S15. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'miR CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.943 (ANOVA)

Table S19. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 368 | 77.6 (14.1)
subtype1 | 112 | 77.6 (14.4)
subtype2 | 114 | 77.5 (14.1)
subtype3 | 62 | 76.9 (14.4)
subtype4 | 80 | 78.4 (13.8)

Figure S16. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.638 (Fisher's exact test)

Table S20. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 327 | 155
subtype1 | 102 | 42
subtype2 | 108 | 51
subtype3 | 46 | 28
subtype4 | 71 | 34

Figure S17. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR CNMF subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.906 (Fisher's exact test)

Table S21. Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 248 | 234
subtype1 | 75 | 69
subtype2 | 78 | 81
subtype3 | 39 | 35
subtype4 | 56 | 49

Figure S18. Get High-res Image Clustering Approach #3: 'miR CNMF subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #4: 'miR cHierClus subtypes'

Table S22. Get Full Table Description of clustering approach #4: 'miR cHierClus subtypes'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 170 | 180 | 132

'miR cHierClus subtypes' versus 'Time to Death'

P value = 0.00455 (logrank test)

Table S23. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 482 | 379 | 0.1 - 127.6 (10.3)
subtype1 | 170 | 137 | 0.1 - 92.6 (9.8)
subtype2 | 180 | 145 | 0.1 - 127.6 (10.0)
subtype3 | 132 | 97 | 0.1 - 108.8 (10.7)

Figure S19. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'miR cHierClus subtypes' versus 'AGE'

P value = 0.000872 (ANOVA)

Table S24. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 482 | 57.5 (14.6)
subtype1 | 170 | 59.2 (12.6)
subtype2 | 180 | 58.9 (13.3)
subtype3 | 132 | 53.5 (17.6)

Figure S20. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'miR cHierClus subtypes' versus 'GENDER'

P value = 0.119 (Fisher's exact test)

Table S25. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 186 | 296
subtype1 | 58 | 112
subtype2 | 80 | 100
subtype3 | 48 | 84

Figure S21. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'miR cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.785 (ANOVA)

Table S26. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 368 | 77.6 (14.1)
subtype1 | 129 | 78.3 (13.5)
subtype2 | 136 | 77.1 (15.5)
subtype3 | 103 | 77.4 (13.1)

Figure S22. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.758 (Fisher's exact test)

Table S27. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 327 | 155
subtype1 | 113 | 57
subtype2 | 121 | 59
subtype3 | 93 | 39

Figure S23. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'miR cHierClus subtypes' versus 'NEOADJUVANT.THERAPY'

P value = 0.993 (Fisher's exact test)

Table S28. Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 248 | 234
subtype1 | 88 | 82
subtype2 | 92 | 88
subtype3 | 68 | 64

Figure S24. Get High-res Image Clustering Approach #4: 'miR cHierClus subtypes' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Clustering Approach #5: 'METHLYATION CNMF'

Table S29. Get Full Table Description of clustering approach #5: 'METHLYATION CNMF'

Cluster Labels | 1 | 2 | 3
--- | --- | --- | ---
Number of samples | 79 | 123 | 78

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.00142 (logrank test)

Table S30. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 280 | 208 | 0.1 - 127.6 (10.0)
subtype1 | 79 | 57 | 0.1 - 92.6 (10.0)
subtype2 | 123 | 95 | 0.1 - 77.6 (9.4)
subtype3 | 78 | 56 | 0.2 - 127.6 (11.7)

Figure S25. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 5.2e-08 (ANOVA)

Table S31. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 280 | 57.5 (14.9)
subtype1 | 79 | 57.0 (11.9)
subtype2 | 123 | 62.4 (12.6)
subtype3 | 78 | 50.3 (17.7)

Figure S26. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.849 (Fisher's exact test)

Table S32. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients | FEMALE | MALE
--- | --- | ---
ALL | 114 | 166
subtype1 | 34 | 45
subtype2 | 50 | 73
subtype3 | 30 | 48

Figure S27. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.223 (ANOVA)

Table S33. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 213 | 75.4 (15.0)
subtype1 | 61 | 77.0 (17.1)
subtype2 | 93 | 73.3 (14.8)
subtype3 | 59 | 76.8 (12.5)

Figure S28. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

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

P value = 0.176 (Fisher's exact test)

Table S34. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients | NO | YES
--- | --- | ---
ALL | 206 | 74
subtype1 | 63 | 16
subtype2 | 84 | 39
subtype3 | 59 | 19

Figure S29. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #5: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NEOADJUVANT.THERAPY'

P value = 0.484 (Fisher's exact test)

Table S35. Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 164 | 116
subtype1 | 49 | 30
subtype2 | 67 | 56
subtype3 | 48 | 30

Figure S30. Get High-res Image Clustering Approach #5: 'METHLYATION CNMF' versus Clinical Feature #6: 'NEOADJUVANT.THERAPY'

Methods & Data
Input

- Cluster data file = GBM.mergedcluster.txt

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

- Number of patients = 527

- Number of clustering approaches = 5

- Number of selected clinical features = 6

- 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

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] 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)
Copyright © 2012 Broad Institute TCGA GDAC as part of the TCGA Research Network. All rights reserved.
Made with Nozzle