Correlation between aggregated molecular cancer subtypes and selected clinical features

Brain Lower Grade Glioma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

Maintainer Information

Citation Information

doi:10.7908/C1571921

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1571921

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 10 different clustering approaches and 6 clinical features across 211 patients, 16 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'AGE', 'GENDER', and 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'AGE', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'AGE', and 'HISTOLOGICAL.TYPE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'HISTOLOGICAL.TYPE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 10 different clustering approaches and 6 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 16 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	KARNOFSKY PERFORMANCE SCORE	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION
Statistical Tests	logrank test	ANOVA	Fisher's exact test	ANOVA	Chi-square test	Fisher's exact test
mRNA CNMF subtypes	0.247 (1.00)	0.326 (1.00)	0.101 (1.00)	0.441 (1.00)	0.0226 (0.902)	0.893 (1.00)
mRNA cHierClus subtypes	0.0482 (1.00)	0.35 (1.00)	0.122 (1.00)	0.205 (1.00)	0.0109 (0.448)	0.788 (1.00)
Copy Number Ratio CNMF subtypes	1.62e-05 (0.000841)	6.39e-08 (3.58e-06)	0.00183 (0.0823)	0.977 (1.00)	2.47e-13 (1.45e-11)	0.28 (1.00)
METHLYATION CNMF	6.62e-06 (0.000357)	4.31e-07 (2.37e-05)	0.075 (1.00)	0.617 (1.00)	2.76e-14 (1.65e-12)	0.000952 (0.0457)
RNAseq CNMF subtypes	1.74e-05 (0.000886)	0.00125 (0.0589)	0.0091 (0.391)	0.327 (1.00)	8.84e-11 (5.13e-09)	0.705 (1.00)
RNAseq cHierClus subtypes	0.0328 (1.00)	0.0419 (1.00)	0.444 (1.00)	0.245 (1.00)	2.28e-10 (1.3e-08)	0.19 (1.00)
MIRSEQ CNMF	0.105 (1.00)	0.524 (1.00)	0.256 (1.00)	0.597 (1.00)	1.06e-05 (0.00056)	0.342 (1.00)
MIRSEQ CHIERARCHICAL	0.0382 (1.00)	0.68 (1.00)	0.579 (1.00)	0.497 (1.00)	0.00175 (0.0806)	0.934 (1.00)
MIRseq Mature CNMF subtypes	0.00745 (0.328)	0.0104 (0.439)	0.288 (1.00)	0.351 (1.00)	0.000187 (0.00937)	0.643 (1.00)
MIRseq Mature cHierClus subtypes	0.024 (0.935)	0.449 (1.00)	0.29 (1.00)	0.659 (1.00)	0.000268 (0.0131)	0.524 (1.00)

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	9	10	8

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.247 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	9	0.1 - 134.3 (46.6)
subtype1	9	4	10.6 - 130.8 (43.9)
subtype2	10	3	0.1 - 78.2 (36.5)
subtype3	8	2	14.4 - 134.3 (51.3)

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 = 0.326 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	9	39.2 (6.2)
subtype2	10	42.3 (7.6)
subtype3	8	35.8 (12.6)

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.101 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	9	18
subtype1	2	7
subtype2	6	4
subtype3	1	7

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 = 0.441 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	7	84.3 (16.2)
subtype2	7	92.9 (7.6)
subtype3	3	90.0 (10.0)

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0226 (Chi-square test), Q value = 0.9

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	7	2	0
subtype2	2	3	5
subtype3	1	4	3

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

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

P value = 0.893 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	12
subtype1	5	4
subtype2	5	5
subtype3	5	3

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

Clustering Approach #2: 'mRNA cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	7	6	10	4

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.0482 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	27	9	0.1 - 134.3 (46.6)
subtype1	7	1	0.1 - 78.2 (31.8)
subtype2	6	1	14.4 - 134.3 (52.7)
subtype3	10	5	10.6 - 130.8 (37.1)
subtype4	4	2	20.0 - 47.9 (43.9)

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 = 0.35 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	27	39.3 (9.1)
subtype1	7	43.9 (8.6)
subtype2	6	34.8 (14.6)
subtype3	10	39.6 (6.0)
subtype4	4	37.5 (3.4)

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

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.122 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	9	18
subtype1	5	2
subtype2	1	5
subtype3	2	8
subtype4	1	3

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 = 0.205 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	17	88.8 (12.2)
subtype1	5	94.0 (8.9)
subtype2	2	90.0 (14.1)
subtype3	8	85.0 (15.1)
subtype4	2	90.0 (0.0)

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

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0109 (Chi-square test), Q value = 0.45

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	10	9	8
subtype1	1	1	5
subtype2	0	4	2
subtype3	7	2	1
subtype4	2	2	0

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

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

P value = 0.788 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	15	12
subtype1	3	4
subtype2	3	3
subtype3	6	4
subtype4	3	1

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

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S15. Get Full Table Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	85	48	74

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 1.62e-05 (logrank test), Q value = 0.00084

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	51	0.0 - 211.2 (13.4)
subtype1	85	14	0.1 - 156.2 (13.4)
subtype2	48	24	0.5 - 211.2 (11.9)
subtype3	73	13	0.0 - 182.3 (14.3)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 6.39e-08 (ANOVA), Q value = 3.6e-06

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

	nPatients	Mean (Std.Dev)
ALL	207	43.1 (13.4)
subtype1	85	37.3 (11.8)
subtype2	48	50.1 (12.9)
subtype3	74	45.2 (12.9)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00183 (Fisher's exact test), Q value = 0.082

Table S18. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	28	57
subtype2	31	17
subtype3	30	44

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.977 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	93	88.4 (10.5)
subtype1	37	88.6 (10.8)
subtype2	26	88.1 (7.5)
subtype3	30	88.3 (12.3)

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

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.47e-13 (Chi-square test), Q value = 1.5e-11

Table S20. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	33	30	21
subtype2	24	15	9
subtype3	6	9	59

Figure S17. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.28 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	127
subtype1	38	47
subtype2	18	30
subtype3	24	50

Figure S18. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S22. Get Full Table Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	92	31	27	56

'METHLYATION CNMF' versus 'Time to Death'

P value = 6.62e-06 (logrank test), Q value = 0.00036

Table S23. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	47	0.0 - 211.2 (13.3)
subtype1	92	21	0.0 - 156.2 (16.9)
subtype2	31	16	0.1 - 211.2 (8.4)
subtype3	27	2	0.1 - 97.9 (10.6)
subtype4	55	8	0.1 - 182.3 (12.4)

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

'METHLYATION CNMF' versus 'AGE'

P value = 4.31e-07 (ANOVA), Q value = 2.4e-05

Table S24. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	206	42.9 (13.3)
subtype1	92	38.8 (11.7)
subtype2	31	52.6 (12.8)
subtype3	27	39.4 (13.2)
subtype4	56	46.1 (12.9)

Figure S20. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.075 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	90	116
subtype1	33	59
subtype2	19	12
subtype3	14	13
subtype4	24	32

Figure S21. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

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

P value = 0.617 (ANOVA), Q value = 1

Table S26. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	47	89.6 (10.4)
subtype2	15	87.3 (5.9)
subtype3	7	84.3 (7.9)
subtype4	23	88.3 (13.4)

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

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 2.76e-14 (Chi-square test), Q value = 1.7e-12

Table S27. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	55	87
subtype1	35	35	21
subtype2	20	5	6
subtype3	7	8	12
subtype4	1	7	48

Figure S23. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.000952 (Fisher's exact test), Q value = 0.046

Table S28. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	77	129
subtype1	48	44
subtype2	10	21
subtype3	6	21
subtype4	13	43

Figure S24. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #5: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	55	40	35	67	9

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.74e-05 (logrank test), Q value = 0.00089

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	51	0.0 - 211.2 (13.4)
subtype1	55	14	0.0 - 130.8 (16.5)
subtype2	40	20	0.1 - 211.2 (9.6)
subtype3	34	8	0.1 - 182.3 (15.8)
subtype4	67	8	0.1 - 156.2 (14.5)
subtype5	9	1	0.2 - 44.0 (14.3)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.00125 (ANOVA), Q value = 0.059

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

	nPatients	Mean (Std.Dev)
ALL	206	43.2 (13.4)
subtype1	55	38.8 (11.7)
subtype2	40	48.1 (14.0)
subtype3	35	47.5 (12.5)
subtype4	67	42.5 (13.8)
subtype5	9	36.2 (10.5)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0091 (Chi-square test), Q value = 0.39

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

nPatients	FEMALE	MALE
ALL	89	117
subtype1	19	36
subtype2	26	14
subtype3	18	17
subtype4	24	43
subtype5	2	7

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

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

P value = 0.327 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	91	88.2 (10.5)
subtype1	32	90.9 (9.3)
subtype2	18	87.8 (5.5)
subtype3	16	86.9 (15.4)
subtype4	22	86.8 (9.9)
subtype5	3	80.0 (17.3)

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

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 8.84e-11 (Chi-square test), Q value = 5.1e-09

Table S34. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	62	54	89
subtype1	20	20	15
subtype2	28	6	6
subtype3	2	5	28
subtype4	10	20	36
subtype5	2	3	4

Figure S29. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.705 (Chi-square test), Q value = 1

Table S35. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	78	128
subtype1	24	31
subtype2	15	25
subtype3	10	25
subtype4	26	41
subtype5	3	6

Figure S30. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S36. Get Full Table Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	32	108	66

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0328 (logrank test), Q value = 1

Table S37. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	205	51	0.0 - 211.2 (13.4)
subtype1	31	8	0.1 - 182.3 (15.1)
subtype2	108	33	0.0 - 211.2 (12.9)
subtype3	66	10	0.1 - 156.2 (14.5)

Figure S31. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0419 (ANOVA), Q value = 1

Table S38. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	206	43.2 (13.4)
subtype1	32	48.6 (12.4)
subtype2	108	42.0 (13.2)
subtype3	66	42.5 (13.7)

Figure S32. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.444 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	117
subtype1	17	15
subtype2	46	62
subtype3	26	40

Figure S33. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.245 (ANOVA), Q value = 1

Table S40. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	91	88.2 (10.5)
subtype1	16	86.9 (15.4)
subtype2	52	89.8 (9.0)
subtype3	23	85.7 (9.5)

Figure S34. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 2.28e-10 (Chi-square test), Q value = 1.3e-08

Table S41. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	62	54	89
subtype1	1	4	27
subtype2	51	31	25
subtype3	10	19	37

Figure S35. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.19 (Fisher's exact test), Q value = 1

Table S42. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	78	128
subtype1	8	24
subtype2	46	62
subtype3	24	42

Figure S36. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S43. Get Full Table Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	55	32	77	43

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.105 (logrank test), Q value = 1

Table S44. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	55	14	0.0 - 117.4 (8.4)
subtype2	32	6	0.1 - 182.3 (21.2)
subtype3	76	13	0.1 - 138.2 (12.9)
subtype4	43	17	0.1 - 211.2 (16.8)

Figure S37. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.524 (ANOVA), Q value = 1

Table S45. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	55	43.1 (14.3)
subtype2	32	43.2 (10.6)
subtype3	77	44.6 (13.9)
subtype4	43	40.8 (13.2)

Figure S38. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.256 (Fisher's exact test), Q value = 1

Table S46. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	22	33
subtype2	19	13
subtype3	31	46
subtype4	17	26

Figure S39. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.597 (ANOVA), Q value = 1

Table S47. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	24	90.8 (8.8)
subtype2	19	88.4 (13.4)
subtype3	29	86.9 (9.7)
subtype4	20	88.0 (10.6)

Figure S40. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 1.06e-05 (Chi-square test), Q value = 0.00056

Table S48. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	26	13	15
subtype2	3	7	22
subtype3	13	23	41
subtype4	21	11	11

Figure S41. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.342 (Fisher's exact test), Q value = 1

Table S49. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	127
subtype1	21	34
subtype2	9	23
subtype3	29	48
subtype4	21	22

Figure S42. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S50. Get Full Table Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	8	89	110

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0382 (logrank test), Q value = 1

Table S51. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	8	1	7.1 - 52.4 (13.8)
subtype2	88	13	0.1 - 211.2 (12.4)
subtype3	110	36	0.0 - 182.3 (15.6)

Figure S43. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.68 (ANOVA), Q value = 1

Table S52. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	8	42.1 (14.0)
subtype2	89	44.2 (13.8)
subtype3	110	42.5 (13.1)

Figure S44. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.579 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	3	5
subtype2	42	47
subtype3	44	66

Figure S45. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.497 (ANOVA), Q value = 1

Table S54. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	4	87.5 (5.0)
subtype2	36	86.9 (11.9)
subtype3	52	89.6 (9.7)

Figure S46. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00175 (Chi-square test), Q value = 0.081

Table S55. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	4	2	2
subtype2	15	23	51
subtype3	44	29	36

Figure S47. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.934 (Fisher's exact test), Q value = 1

Table S56. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	127
subtype1	3	5
subtype2	33	56
subtype3	44	66

Figure S48. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S57. Get Full Table Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	36	27	66	31	28	19

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.00745 (logrank test), Q value = 0.33

Table S58. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	36	15	0.2 - 117.4 (12.4)
subtype2	27	3	0.1 - 130.8 (17.5)
subtype3	65	11	0.1 - 156.2 (13.4)
subtype4	31	12	0.1 - 211.2 (17.9)
subtype5	28	7	0.1 - 133.7 (6.2)
subtype6	19	2	0.0 - 95.6 (6.5)

Figure S49. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.0104 (ANOVA), Q value = 0.44

Table S59. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	36	46.7 (14.5)
subtype2	27	43.9 (10.4)
subtype3	66	43.2 (13.9)
subtype4	31	47.3 (13.8)
subtype5	28	38.9 (12.6)
subtype6	19	35.5 (9.3)

Figure S50. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.288 (Chi-square test), Q value = 1

Table S60. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	15	21
subtype2	14	13
subtype3	29	37
subtype4	17	14
subtype5	9	19
subtype6	5	14

Figure S51. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.351 (ANOVA), Q value = 1

Table S61. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	19	92.6 (5.6)
subtype2	15	88.7 (12.5)
subtype3	26	85.4 (10.3)
subtype4	16	87.5 (12.9)
subtype5	10	90.0 (6.7)
subtype6	6	88.3 (14.7)

Figure S52. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000187 (Chi-square test), Q value = 0.0094

Table S62. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	20	9	6
subtype2	7	5	15
subtype3	11	18	37
subtype4	6	9	16
subtype5	15	8	5
subtype6	4	5	10

Figure S53. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.643 (Chi-square test), Q value = 1

Table S63. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	127
subtype1	13	23
subtype2	12	15
subtype3	23	43
subtype4	14	17
subtype5	13	15
subtype6	5	14

Figure S54. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S64. Get Full Table Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	6	109	92

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.024 (logrank test), Q value = 0.94

Table S65. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	206	50	0.0 - 211.2 (13.4)
subtype1	6	1	7.2 - 21.0 (13.8)
subtype2	109	36	0.0 - 182.3 (15.4)
subtype3	91	13	0.1 - 211.2 (12.4)

Figure S55. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.449 (ANOVA), Q value = 1

Table S66. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	207	43.2 (13.4)
subtype1	6	43.2 (13.4)
subtype2	109	42.1 (13.0)
subtype3	92	44.5 (13.8)

Figure S56. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.29 (Fisher's exact test), Q value = 1

Table S67. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	118
subtype1	2	4
subtype2	42	67
subtype3	45	47

Figure S57. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

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

P value = 0.659 (ANOVA), Q value = 1

Table S68. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	92	88.5 (10.5)
subtype1	4	87.5 (5.0)
subtype2	50	89.4 (9.8)
subtype3	38	87.4 (11.8)

Figure S58. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.000268 (Chi-square test), Q value = 0.013

Table S69. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients	ASTROCYTOMA	OLIGOASTROCYTOMA	OLIGODENDROGLIOMA
ALL	63	54	89
subtype1	3	0	3
subtype2	45	30	33
subtype3	15	24	53

Figure S59. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

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

P value = 0.524 (Fisher's exact test), Q value = 1

Table S70. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	80	127
subtype1	3	3
subtype2	45	64
subtype3	32	60

Figure S60. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'RADIATIONS.RADIATION.REGIMENINDICATION'

Methods & Data

Input

Cluster data file = LGG-TP.mergedcluster.txt
Clinical data file = LGG-TP.clin.merged.picked.txt
Number of patients = 211
Number of clustering approaches = 10
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

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

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

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)

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

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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