Bladder Urothelial 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 4 clinical features across 102 patients, one significant finding detected with P value < 0.05.

3 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
CNMF clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death'.
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 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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 8 different clustering approaches and 4 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, one significant finding detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA
CN CNMF	0.679	0.969	0.313	0.409
METHLYATION CNMF	0.131	0.759	0.457	0.279
RPPA CNMF subtypes	0.0407	0.473	0.633	0.465
RPPA cHierClus subtypes	0.093	0.164	0.196	0.342
RNAseq CNMF subtypes	0.191	0.285	0.25	0.477
RNAseq cHierClus subtypes	0.0732	0.996	0.341	0.909
MIRseq CNMF subtypes	0.243	0.364	0.302	0.504
MIRseq cHierClus subtypes	0.63	0.195	0.795	0.795

Clustering Approach #1: 'CN CNMF'

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

Cluster Labels	1	2	3
Number of samples	27	41	20

'CN CNMF' versus 'Time to Death'

P value = 0.679 (logrank test)

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	84	26	0.4 - 118.9 (7.3)
subtype1	25	7	2.1 - 75.3 (6.7)
subtype2	40	13	0.5 - 37.8 (7.2)
subtype3	19	6	0.4 - 118.9 (7.8)

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

'CN CNMF' versus 'AGE'

P value = 0.969 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	87	67.2 (11.0)
subtype1	26	67.3 (11.3)
subtype2	41	66.9 (11.9)
subtype3	20	67.7 (9.3)

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

'CN CNMF' versus 'GENDER'

P value = 0.313 (Fisher's exact test)

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

nPatients	FEMALE	MALE
ALL	27	61
subtype1	7	20
subtype2	11	30
subtype3	9	11

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

'CN CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.409 (ANOVA)

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

	nPatients	Mean (Std.Dev)
ALL	23	77.8 (18.6)
subtype1	9	84.4 (8.8)
subtype2	9	73.3 (24.0)
subtype3	5	74.0 (20.7)

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S6. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	32	36	34

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.131 (logrank test)

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	98	30	0.4 - 118.9 (7.2)
subtype1	31	7	0.8 - 118.9 (7.6)
subtype2	35	14	0.4 - 75.3 (7.8)
subtype3	32	9	0.7 - 37.8 (6.7)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.759 (ANOVA)

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	101	67.1 (10.6)
subtype1	31	67.0 (9.7)
subtype2	36	68.1 (9.8)
subtype3	34	66.2 (12.2)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.457 (Fisher's exact test)

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	73
subtype1	10	22
subtype2	12	24
subtype3	7	27

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.279 (ANOVA)

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	26	79.2 (17.9)
subtype1	8	87.5 (4.6)
subtype2	4	72.5 (23.6)
subtype3	14	76.4 (20.2)

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S11. Get Full Table Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	12	10	14	17

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0407 (logrank test)

Table S12. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	12	4	5.7 - 61.9 (10.6)
subtype2	10	5	1.8 - 100.5 (5.5)
subtype3	13	2	0.5 - 19.5 (6.6)
subtype4	17	9	0.4 - 118.9 (14.9)

Figure S9. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.473 (ANOVA)

Table S13. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	12	63.9 (9.2)
subtype2	10	69.5 (9.3)
subtype3	13	66.5 (10.2)
subtype4	17	69.1 (10.3)

Figure S10. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.633 (Fisher's exact test)

Table S14. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	32
subtype1	6	6
subtype2	5	5
subtype3	5	9
subtype4	5	12

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

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

P value = 0.465 (ANOVA)

Table S15. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	5	88.0 (4.5)
subtype2	1	70.0 (NA)
subtype3	1	60.0 (NA)
subtype4	4	77.5 (25.0)

Figure S12. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S16. Get Full Table Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	14	17	22

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.093 (logrank test)

Table S17. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	52	20	0.4 - 118.9 (8.3)
subtype1	14	5	1.9 - 61.9 (8.3)
subtype2	17	10	0.4 - 118.9 (8.2)
subtype3	21	5	0.5 - 100.5 (7.7)

Figure S13. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.164 (ANOVA)

Table S18. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	52	67.3 (9.8)
subtype1	14	63.5 (9.6)
subtype2	17	70.2 (9.0)
subtype3	21	67.5 (10.2)

Figure S14. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.196 (Fisher's exact test)

Table S19. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	21	32
subtype1	8	6
subtype2	7	10
subtype3	6	16

Figure S15. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.342 (ANOVA)

Table S20. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	80.0 (16.7)
subtype1	4	90.0 (0.0)
subtype2	4	72.5 (23.6)
subtype3	3	76.7 (15.3)

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

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	35	17	25	10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.191 (logrank test)

Table S22. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	83	25	0.4 - 118.9 (7.2)
subtype1	32	9	0.5 - 100.5 (7.1)
subtype2	17	8	4.1 - 118.9 (6.7)
subtype3	25	6	0.4 - 75.3 (8.2)
subtype4	9	2	1.8 - 11.1 (5.1)

Figure S17. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.285 (ANOVA)

Table S23. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	86	67.0 (11.1)
subtype1	34	67.6 (11.2)
subtype2	17	63.2 (9.8)
subtype3	25	66.9 (12.1)
subtype4	10	71.6 (9.9)

Figure S18. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.25 (Fisher's exact test)

Table S24. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	60
subtype1	8	27
subtype2	4	13
subtype3	10	15
subtype4	5	5

Figure S19. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.477 (ANOVA)

Table S25. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	24	78.3 (18.3)
subtype1	10	77.0 (19.5)
subtype2	7	85.7 (7.9)
subtype3	6	73.3 (25.8)
subtype4	1	70.0 (NA)

Figure S20. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S26. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	25	42	20

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0732 (logrank test)

Table S27. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	83	25	0.4 - 118.9 (7.2)
subtype1	25	6	0.4 - 75.3 (8.3)
subtype2	39	10	0.5 - 100.5 (6.7)
subtype3	19	9	1.8 - 118.9 (5.9)

Figure S21. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.996 (ANOVA)

Table S28. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	86	67.0 (11.1)
subtype1	25	67.0 (12.0)
subtype2	41	67.1 (10.9)
subtype3	20	66.8 (11.0)

Figure S22. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.341 (Fisher's exact test)

Table S29. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	27	60
subtype1	10	15
subtype2	10	32
subtype3	7	13

Figure S23. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.909 (ANOVA)

Table S30. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	24	78.3 (18.3)
subtype1	7	75.7 (24.4)
subtype2	12	79.2 (18.3)
subtype3	5	80.0 (10.0)

Figure S24. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq CNMF subtypes'

Table S31. Get Full Table Description of clustering approach #7: 'MIRseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	22	52	26

'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.243 (logrank test)

Table S32. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	96	30	0.4 - 118.9 (7.3)
subtype1	19	3	0.8 - 118.9 (6.2)
subtype2	51	20	0.4 - 61.9 (7.3)
subtype3	26	7	0.5 - 100.5 (8.5)

Figure S25. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq CNMF subtypes' versus 'AGE'

P value = 0.364 (ANOVA)

Table S33. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	99	67.4 (10.5)
subtype1	21	69.5 (9.8)
subtype2	52	66.0 (10.9)
subtype3	26	68.4 (10.3)

Figure S26. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.302 (Fisher's exact test)

Table S34. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	71
subtype1	4	18
subtype2	15	37
subtype3	10	16

Figure S27. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.504 (ANOVA)

Table S35. Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	25	78.8 (18.1)
subtype1	5	86.0 (8.9)
subtype2	16	75.6 (21.3)
subtype3	4	82.5 (9.6)

Figure S28. Get High-res Image Clustering Approach #7: 'MIRseq CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'MIRseq cHierClus subtypes'

Table S36. Get Full Table Description of clustering approach #8: 'MIRseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	21	67	12

'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.63 (logrank test)

Table S37. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	96	30	0.4 - 118.9 (7.3)
subtype1	18	4	1.5 - 118.9 (6.1)
subtype2	66	22	0.4 - 100.5 (7.8)
subtype3	12	4	0.8 - 46.8 (5.6)

Figure S29. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq cHierClus subtypes' versus 'AGE'

P value = 0.195 (ANOVA)

Table S38. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	99	67.4 (10.5)
subtype1	20	70.8 (9.9)
subtype2	67	66.1 (11.0)
subtype3	12	68.5 (7.6)

Figure S30. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.795 (Fisher's exact test)

Table S39. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	29	71
subtype1	5	16
subtype2	21	46
subtype3	3	9

Figure S31. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.795 (ANOVA)

Table S40. Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	25	78.8 (18.1)
subtype1	3	83.3 (11.5)
subtype2	19	77.4 (20.0)
subtype3	3	83.3 (11.5)

Figure S32. Get High-res Image Clustering Approach #8: 'MIRseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data

Input

Cluster data file = BLCA.mergedcluster.txt
Clinical data file = BLCA.clin.merged.picked.txt
Number of patients = 102
Number of clustering approaches = 8
Number of selected clinical features = 4
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.

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)

Made with Nozzle