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 68 patients, 4 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'.

  • 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 3 subtypes that correlate to 'Time to Death'.

  • 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 5 subtypes that correlate to 'Time to Death'.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 2 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, 4 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE
Statistical Tests logrank test t-test Fisher's exact test t-test
CN CNMF 0.00392 0.728 0.821 0.87
METHLYATION CNMF 0.0943 0.592 0.133 0.713
RPPA CNMF subtypes 0.0443 0.362 0.699 0.465
RPPA cHierClus subtypes 0.111 0.15 0.302 0.342
RNAseq CNMF subtypes 0.0281 0.453 0.38 0.68
RNAseq cHierClus subtypes 0.26 0.946 0.521 0.404
MIRseq CNMF subtypes 0.00184 0.545 0.942 0.573
MIRseq cHierClus subtypes 0.563 0.107 1
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 7 18 9 16 15
'CN CNMF' versus 'Time to Death'

P value = 0.00392 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 24 0.4 - 118.9 (7.8)
subtype1 7 3 6.7 - 26.1 (10.6)
subtype2 18 8 1.5 - 19.0 (7.1)
subtype3 9 3 0.5 - 75.3 (2.5)
subtype4 16 4 0.7 - 37.8 (16.1)
subtype5 15 6 0.4 - 118.9 (8.8)

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

'CN CNMF' versus 'AGE'

P value = 0.728 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 65 67.7 (10.5)
subtype1 7 69.6 (9.9)
subtype2 18 66.6 (10.9)
subtype3 9 71.8 (11.8)
subtype4 16 66.5 (11.5)
subtype5 15 66.8 (8.8)

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

'CN CNMF' versus 'GENDER'

P value = 0.821 (Chi-square test)

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

nPatients FEMALE MALE
ALL 22 43
subtype1 2 5
subtype2 5 13
subtype3 3 6
subtype4 5 11
subtype5 7 8

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

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

P value = 0.87 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 16 79.4 (17.7)
subtype1 1 80.0 (NA)
subtype2 1 90.0 (NA)
subtype3 1 70.0 (NA)
subtype4 8 80.0 (19.3)
subtype5 5 78.0 (21.7)

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 17 27 21
'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0943 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 24 0.4 - 118.9 (7.8)
subtype1 17 4 1.5 - 118.9 (8.3)
subtype2 27 13 0.4 - 75.3 (8.3)
subtype3 21 7 0.5 - 26.1 (7.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.592 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 65 68.0 (10.5)
subtype1 17 65.8 (10.2)
subtype2 27 69.1 (10.5)
subtype3 21 68.2 (10.7)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.133 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 23 42
subtype1 8 9
subtype2 11 16
subtype3 4 17

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

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

P value = 0.713 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 16 79.4 (17.7)
subtype1 3 86.7 (5.8)
subtype2 4 75.0 (23.8)
subtype3 9 78.9 (18.3)

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 13 16
'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0443 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 51 20 0.4 - 118.9 (8.3)
subtype1 12 4 5.7 - 49.2 (10.6)
subtype2 10 5 1.8 - 100.5 (5.5)
subtype3 13 2 0.5 - 19.5 (6.6)
subtype4 16 9 0.4 - 118.9 (16.1)

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 = 0.362 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 51 67.6 (9.7)
subtype1 12 63.9 (9.2)
subtype2 10 69.5 (9.3)
subtype3 13 66.5 (10.2)
subtype4 16 70.0 (9.9)

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

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.699 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 21 30
subtype1 6 6
subtype2 5 5
subtype3 5 8
subtype4 5 11

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 20
'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.111 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 51 20 0.4 - 118.9 (8.3)
subtype1 14 5 1.9 - 49.2 (8.3)
subtype2 17 10 0.4 - 118.9 (8.2)
subtype3 20 5 0.5 - 100.5 (8.3)

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 = 0.15 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 51 67.6 (9.7)
subtype1 14 63.5 (9.6)
subtype2 17 70.2 (9.0)
subtype3 20 68.2 (9.9)

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

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.302 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 21 30
subtype1 8 6
subtype2 7 10
subtype3 6 14

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
Number of samples 27 16 22
'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0281 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 23 0.4 - 118.9 (7.8)
subtype1 27 7 0.5 - 100.5 (7.2)
subtype2 16 9 1.8 - 118.9 (6.7)
subtype3 22 7 0.4 - 75.3 (8.7)

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 = 0.453 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 65 67.5 (10.5)
subtype1 27 66.1 (10.3)
subtype2 16 70.3 (9.5)
subtype3 22 67.1 (11.6)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.38 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 22 43
subtype1 7 20
subtype2 5 11
subtype3 10 12

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 = 0.68 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 17 80.0 (17.3)
subtype1 7 84.3 (11.3)
subtype2 3 80.0 (10.0)
subtype3 7 75.7 (24.4)

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 23 7 35
'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.26 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 23 0.4 - 118.9 (7.8)
subtype1 23 8 0.4 - 75.3 (8.6)
subtype2 7 5 5.1 - 118.9 (6.7)
subtype3 35 10 0.5 - 100.5 (7.0)

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 = 0.946 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 65 67.5 (10.5)
subtype1 23 66.9 (11.6)
subtype2 7 67.9 (9.0)
subtype3 35 67.8 (10.3)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.521 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 22 43
subtype1 9 14
subtype2 1 6
subtype3 12 23

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 = 0.404 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 17 80.0 (17.3)
subtype1 6 73.3 (25.8)
subtype2 2 85.0 (7.1)
subtype3 9 83.3 (11.2)

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 4 5
Number of samples 12 29 11 3 12
'MIRseq CNMF subtypes' versus 'Time to Death'

P value = 0.00184 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 67 25 0.4 - 118.9 (7.8)
subtype1 12 4 1.5 - 118.9 (9.5)
subtype2 29 14 0.4 - 49.2 (8.2)
subtype3 11 3 0.7 - 37.8 (13.7)
subtype4 3 2 5.1 - 6.6 (6.2)
subtype5 12 2 0.5 - 100.5 (6.8)

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 = 0.545 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 67 67.9 (10.3)
subtype1 12 70.6 (8.5)
subtype2 29 67.7 (10.5)
subtype3 11 63.5 (11.2)
subtype4 3 71.0 (10.6)
subtype5 12 68.8 (11.0)

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

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.942 (Chi-square test)

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

nPatients FEMALE MALE
ALL 23 44
subtype1 3 9
subtype2 10 19
subtype3 4 7
subtype4 1 2
subtype5 5 7

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 = 0.573 (ANOVA)

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

nPatients Mean (Std.Dev)
ALL 16 79.4 (17.7)
subtype2 8 76.2 (22.6)
subtype3 5 82.0 (13.0)
subtype4 1 90.0 (NA)
subtype5 2 80.0 (14.1)

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 2 12 53
'MIRseq cHierClus subtypes' versus 'Time to Death'

P value = 0.563 (logrank test)

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

nPatients nDeath Duration Range (Median), Month
ALL 65 24 0.4 - 118.9 (8.2)
subtype2 12 3 1.5 - 118.9 (7.8)
subtype3 53 21 0.4 - 100.5 (8.2)

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 = 0.107 (t-test)

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

nPatients Mean (Std.Dev)
ALL 65 68.1 (10.4)
subtype2 12 72.0 (8.5)
subtype3 53 67.2 (10.7)

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

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 1 (Fisher's exact test)

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

nPatients FEMALE MALE
ALL 23 42
subtype2 4 8
subtype3 19 34

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

Methods & Data
Input

  • Cluster data file = BLCA.mergedcluster.txt

  • Clinical data file = BLCA.clin.merged.picked.txt

  • Number of patients = 68

  • 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

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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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)
[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)
[7] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
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