Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Renal Papillary Cell Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

Maintainer Information

Citation Information

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/C1125R02

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 10 clinical features across 129 patients, 12 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'PATHOLOGY.M.STAGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', and 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.M.STAGE', and 'GENDER'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 12 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.257 (1.00)	0.0119 (0.559)	0.236 (1.00)	0.000288 (0.0164)	0.332 (1.00)	0.532 (1.00)	0.802 (1.00)	0.000543 (0.0304)
AGE	ANOVA	0.68 (1.00)	0.387 (1.00)	0.476 (1.00)	0.243 (1.00)	0.572 (1.00)	0.724 (1.00)	0.427 (1.00)	0.0693 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.0116 (0.557)	9.5e-08 (5.98e-06)	4.4e-05 (0.00268)	6.6e-07 (4.09e-05)	0.00993 (0.486)	0.208 (1.00)	0.037 (1.00)	0.00845 (0.422)
PATHOLOGY T STAGE	Chi-square test	0.0469 (1.00)	8.77e-09 (5.61e-07)	7.5e-05 (0.0045)	0.00026 (0.0151)	0.121 (1.00)	0.0266 (1.00)	0.341 (1.00)	0.056 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.68 (1.00)	0.0664 (1.00)	0.0645 (1.00)	0.0133 (0.61)	0.502 (1.00)	0.22 (1.00)	0.27 (1.00)	0.101 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.123 (1.00)	0.00161 (0.0855)	0.00549 (0.28)	0.000216 (0.0128)	0.101 (1.00)	0.431 (1.00)	0.158 (1.00)	0.0291 (1.00)
GENDER	Fisher's exact test	0.117 (1.00)	0.507 (1.00)	0.00126 (0.0678)	0.000753 (0.0414)	0.00486 (0.253)	0.0225 (1.00)	0.118 (1.00)	0.119 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.581 (1.00)	0.144 (1.00)	0.0414 (1.00)	0.35 (1.00)	0.381 (1.00)	0.665 (1.00)	0.601 (1.00)	0.255 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA
YEAROFTOBACCOSMOKINGONSET	ANOVA

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	27	43	59

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

P value = 0.257 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	119	15	0.0 - 194.8 (13.7)
subtype1	24	2	0.0 - 194.8 (12.5)
subtype2	40	6	0.0 - 80.8 (9.9)
subtype3	55	7	0.0 - 129.9 (20.1)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

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

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

	nPatients	Mean (Std.Dev)
ALL	126	59.8 (12.3)
subtype1	26	60.3 (11.4)
subtype2	41	60.9 (12.6)
subtype3	59	58.8 (12.7)

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

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0116 (Chi-square test), Q value = 0.56

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	69	10	30	10
subtype1	18	2	3	1
subtype2	16	3	14	8
subtype3	35	5	13	1

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.0469 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	74	16	39
subtype1	19	3	5
subtype2	17	6	20
subtype3	38	7	14

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.68 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	26	12	4
subtype1	3	1	0
subtype2	12	8	3
subtype3	11	3	1

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.123 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	57	6	57
subtype1	12	0	12
subtype2	17	5	18
subtype3	28	1	27

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.117 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	40	89
subtype1	4	23
subtype2	16	27
subtype3	20	39

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.581 (ANOVA), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	3	96.7 (5.8)
subtype2	8	81.2 (33.1)
subtype3	11	90.0 (17.3)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S10. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	31	35	47

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0119 (logrank test), Q value = 0.56

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	103	13	0.0 - 194.8 (13.6)
subtype1	29	3	0.0 - 80.8 (24.5)
subtype2	33	9	0.2 - 194.8 (11.3)
subtype3	41	1	0.0 - 129.9 (11.6)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.387 (ANOVA), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	110	60.1 (12.5)
subtype1	30	62.0 (11.9)
subtype2	34	60.9 (15.5)
subtype3	46	58.2 (10.1)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 9.5e-08 (Chi-square test), Q value = 6e-06

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	64	7	29	9
subtype1	24	2	2	2
subtype2	6	2	20	7
subtype3	34	3	7	0

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 8.77e-09 (Chi-square test), Q value = 5.6e-07

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	67	9	37
subtype1	25	1	5
subtype2	6	3	26
subtype3	36	5	6

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.0664 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	24	11	4
subtype1	4	1	1
subtype2	11	10	3
subtype3	9	0	0

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.00161 (Chi-square test), Q value = 0.086

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	47	5	57
subtype1	17	1	12
subtype2	18	4	12
subtype3	12	0	33

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.507 (Fisher's exact test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	36	77
subtype1	9	22
subtype2	14	21
subtype3	13	34

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.144 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.9 (12.9)
subtype1	6	81.7 (20.4)
subtype2	2	95.0 (7.1)
subtype3	13	96.2 (5.1)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S19. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	44	31	22	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.236 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	109	15	0.0 - 194.8 (14.1)
subtype1	41	9	0.2 - 194.8 (11.1)
subtype2	27	2	0.0 - 53.8 (13.6)
subtype3	22	2	0.0 - 96.9 (22.3)
subtype4	19	2	3.8 - 129.9 (34.2)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.476 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	115	59.8 (12.7)
subtype1	43	61.0 (14.5)
subtype2	30	59.9 (11.2)
subtype3	22	61.0 (11.7)
subtype4	20	55.9 (11.6)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 4.4e-05 (Chi-square test), Q value = 0.0027

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	62	8	30	10
subtype1	11	4	18	9
subtype2	21	1	5	0
subtype3	13	3	6	0
subtype4	17	0	1	1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 7.5e-05 (Chi-square test), Q value = 0.0045

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

nPatients	T1	T2	T3+T4
ALL	66	14	38
subtype1	13	5	26
subtype2	22	5	4
subtype3	13	3	6
subtype4	18	1	2

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0645 (Chi-square test), Q value = 1

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

nPatients	N0	N1	N2
ALL	24	12	4
subtype1	10	11	3
subtype2	3	0	0
subtype3	9	1	0
subtype4	2	0	1

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.00549 (Chi-square test), Q value = 0.28

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	54	6	50
subtype1	23	6	13
subtype2	8	0	19
subtype3	13	0	9
subtype4	10	0	9

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00126 (Fisher's exact test), Q value = 0.068

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	81
subtype1	23	21
subtype2	8	23
subtype3	2	20
subtype4	4	17

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.0414 (ANOVA), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	18	86.1 (25.5)
subtype1	4	57.5 (46.5)
subtype2	7	97.1 (4.9)
subtype3	2	95.0 (7.1)
subtype4	5	90.0 (0.0)

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S28. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	21	58	39

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000288 (logrank test), Q value = 0.016

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	109	15	0.0 - 194.8 (14.1)
subtype1	19	7	0.2 - 80.8 (11.0)
subtype2	52	3	0.0 - 194.8 (13.9)
subtype3	38	5	0.0 - 123.6 (19.8)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.243 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	115	59.8 (12.7)
subtype1	20	58.0 (15.5)
subtype2	56	58.5 (11.1)
subtype3	39	62.6 (13.1)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 6.6e-07 (Chi-square test), Q value = 4.1e-05

Table S31. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	62	8	30	10
subtype1	8	3	3	7
subtype2	39	3	7	1
subtype3	15	2	20	2

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00026 (Chi-square test), Q value = 0.015

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

nPatients	T1	T2	T3+T4
ALL	66	14	38
subtype1	9	2	10
subtype2	42	9	7
subtype3	15	3	21

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0133 (Chi-square test), Q value = 0.61

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

nPatients	N0	N1	N2
ALL	24	12	4
subtype1	2	6	3
subtype2	7	1	0
subtype3	15	5	1

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

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.000216 (Chi-square test), Q value = 0.013

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	54	6	50
subtype1	11	4	6
subtype2	18	0	33
subtype3	25	2	11

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.000753 (Fisher's exact test), Q value = 0.041

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	37	81
subtype1	14	7
subtype2	12	46
subtype3	11	28

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.35 (ANOVA), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	18	86.1 (25.5)
subtype1	4	80.0 (27.1)
subtype2	12	95.0 (5.2)
subtype3	2	45.0 (63.6)

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

Clustering Approach #5: 'MIRSEQ CNMF'

Table S37. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4
Number of samples	32	39	34	18

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.332 (logrank test), Q value = 1

Table S38. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.0 - 194.8 (14.1)
subtype1	30	2	0.0 - 123.6 (15.5)
subtype2	37	7	0.2 - 194.8 (20.1)
subtype3	29	2	0.0 - 96.9 (11.4)
subtype4	17	4	0.5 - 86.7 (14.1)

Figure S33. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.572 (ANOVA), Q value = 1

Table S39. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	120	60.1 (12.2)
subtype1	31	61.2 (12.0)
subtype2	39	58.2 (14.5)
subtype3	32	61.9 (9.5)
subtype4	18	59.1 (11.7)

Figure S34. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00993 (Chi-square test), Q value = 0.49

Table S40. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	64	9	30	10
subtype1	17	3	10	0
subtype2	21	4	6	7
subtype3	21	2	6	0
subtype4	5	0	8	3

Figure S35. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.121 (Chi-square test), Q value = 1

Table S41. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	69	15	39
subtype1	18	3	11
subtype2	22	4	13
subtype3	23	6	5
subtype4	6	2	10

Figure S36. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.502 (Chi-square test), Q value = 1

Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	25	12	4
subtype1	7	2	0
subtype2	7	6	3
subtype3	5	1	0
subtype4	6	3	1

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.101 (Chi-square test), Q value = 1

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	55	6	53
subtype1	16	0	14
subtype2	18	3	18
subtype3	13	0	16
subtype4	8	3	5

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00486 (Fisher's exact test), Q value = 0.25

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	85
subtype1	3	29
subtype2	16	23
subtype3	10	24
subtype4	9	9

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

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

P value = 0.381 (ANOVA), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	6	95.0 (5.5)
subtype2	11	80.0 (31.3)
subtype3	3	96.7 (5.8)
subtype4	2	95.0 (7.1)

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S46. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	43	33	47

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.532 (logrank test), Q value = 1

Table S47. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	15	0.0 - 194.8 (14.1)
subtype1	41	6	0.0 - 96.9 (14.6)
subtype2	30	2	0.0 - 123.6 (13.6)
subtype3	42	7	0.1 - 194.8 (17.1)

Figure S41. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.724 (ANOVA), Q value = 1

Table S48. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	120	60.1 (12.2)
subtype1	42	61.2 (12.7)
subtype2	32	60.1 (9.7)
subtype3	46	59.1 (13.4)

Figure S42. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.208 (Chi-square test), Q value = 1

Table S49. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	64	9	30	10
subtype1	20	3	16	2
subtype2	17	2	6	1
subtype3	27	4	8	7

Figure S43. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.0266 (Chi-square test), Q value = 1

Table S50. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	69	15	39
subtype1	21	3	19
subtype2	20	8	5
subtype3	28	4	15

Figure S44. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.22 (Chi-square test), Q value = 1

Table S51. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	25	12	4
subtype1	10	5	1
subtype2	6	0	0
subtype3	9	7	3

Figure S45. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.431 (Chi-square test), Q value = 1

Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	55	6	53
subtype1	24	2	14
subtype2	12	1	14
subtype3	19	3	25

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0225 (Fisher's exact test), Q value = 1

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	38	85
subtype1	7	36
subtype2	11	22
subtype3	20	27

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

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

P value = 0.665 (ANOVA), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	22	87.7 (23.3)
subtype1	8	82.5 (33.7)
subtype2	3	96.7 (5.8)
subtype3	11	89.1 (17.0)

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S55. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	37	42	28

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

P value = 0.802 (logrank test), Q value = 1

Table S56. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	13	0.0 - 194.8 (14.1)
subtype1	33	4	0.0 - 123.6 (14.6)
subtype2	40	7	0.1 - 194.8 (16.6)
subtype3	24	2	0.0 - 63.7 (7.8)

Figure S49. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.427 (ANOVA), Q value = 1

Table S57. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	104	60.4 (12.3)
subtype1	36	61.9 (11.5)
subtype2	42	58.5 (14.5)
subtype3	26	61.5 (9.3)

Figure S50. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.037 (Chi-square test), Q value = 1

Table S58. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	59	6	29	9
subtype1	16	2	16	1
subtype2	26	3	6	7
subtype3	17	1	7	1

Figure S51. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.341 (Chi-square test), Q value = 1

Table S59. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	62	8	37
subtype1	17	3	17
subtype2	27	2	13
subtype3	18	3	7

Figure S52. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.27 (Chi-square test), Q value = 1

Table S60. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	23	11	4
subtype1	11	2	1
subtype2	7	6	3
subtype3	5	3	0

Figure S53. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.158 (Chi-square test), Q value = 1

Table S61. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	45	5	53
subtype1	19	1	14
subtype2	19	3	20
subtype3	7	1	19

Figure S54. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.118 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	73
subtype1	8	29
subtype2	18	24
subtype3	8	20

Figure S55. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.601 (ANOVA), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.9 (12.9)
subtype1	4	95.0 (5.8)
subtype2	11	89.1 (17.0)
subtype3	6	95.0 (5.5)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S64. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	10	45	52

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

P value = 0.000543 (logrank test), Q value = 0.03

Table S65. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	97	13	0.0 - 194.8 (14.1)
subtype1	10	4	0.9 - 43.2 (13.3)
subtype2	41	8	0.1 - 194.8 (14.1)
subtype3	46	1	0.0 - 123.6 (13.9)

Figure S57. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0693 (ANOVA), Q value = 1

Table S66. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	104	60.4 (12.3)
subtype1	10	67.2 (12.2)
subtype2	44	57.8 (14.0)
subtype3	50	61.4 (10.1)

Figure S58. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.00845 (Chi-square test), Q value = 0.42

Table S67. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	59	6	29	9
subtype1	3	0	7	0
subtype2	25	3	9	8
subtype3	31	3	13	1

Figure S59. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.056 (Chi-square test), Q value = 1

Table S68. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	62	8	37
subtype1	3	0	7
subtype2	26	2	17
subtype3	33	6	13

Figure S60. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.101 (Chi-square test), Q value = 1

Table S69. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2
ALL	23	11	4
subtype1	4	2	1
subtype2	8	8	3
subtype3	11	1	0

Figure S61. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.0291 (Chi-square test), Q value = 1

Table S70. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	45	5	53
subtype1	8	0	1
subtype2	18	4	23
subtype3	19	1	29

Figure S62. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.119 (Fisher's exact test), Q value = 1

Table S71. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	34	73
subtype1	3	7
subtype2	19	26
subtype3	12	40

Figure S63. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

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

P value = 0.255 (ANOVA), Q value = 1

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	21	91.9 (12.9)
subtype1	1	90.0 (NA)
subtype2	11	89.1 (17.0)
subtype3	9	95.6 (5.3)

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Methods & Data

Input

Cluster data file = KIRP-TP.mergedcluster.txt
Clinical data file = KIRP-TP.clin.merged.picked.txt
Number of patients = 129
Number of clustering approaches = 8
Number of selected clinical features = 10
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

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

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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] 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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