Correlation between aggregated molecular cancer subtypes and selected clinical features

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 13 clinical features across 869 patients, 76 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that correlate to 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that correlate to 'PATHOLOGY_T_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', 'HISTOLOGICAL_TYPE', 'NUMBER_PACK_YEARS_SMOKED', 'YEAR_OF_TOBACCO_SMOKING_ONSET', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'HISTOLOGICAL_TYPE', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'HISTOLOGICAL_TYPE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and 'HISTOLOGICAL_TYPE'.
5 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'NEOPLASM_DISEASESTAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', and '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 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 76 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	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	0.318 (0.388)	0.149 (0.208)	0.00233 (0.0048)	4.13e-05 (0.000112)	0 (0)	6.97e-13 (4.53e-11)	1.11e-07 (3.6e-06)	7.81e-09 (3.38e-07)	2.35e-06 (3.02e-05)	7.63e-07 (1.65e-05)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.222 (0.286)	0.214 (0.279)	0.224 (0.286)	3.76e-07 (9.78e-06)	0.000197 (0.000465)	1.05e-05 (3.1e-05)	0.323 (0.388)	4.58e-06 (3.02e-05)	0.00264 (0.00527)	0.0106 (0.0202)
NEOPLASM DISEASESTAGE	Fisher's exact test	0.0324 (0.0577)	0.0637 (0.108)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)
PATHOLOGY T STAGE	Fisher's exact test	0.0111 (0.0209)	0.0246 (0.045)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)
PATHOLOGY N STAGE	Fisher's exact test	0.0767 (0.121)	0.321 (0.388)	0.124 (0.178)	0.00073 (0.00164)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	3e-05 (8.48e-05)	0.161 (0.22)	0.00063 (0.00146)	1e-05 (3.02e-05)
PATHOLOGY M STAGE	Fisher's exact test	0.18 (0.236)	0.245 (0.307)	0.00251 (0.0051)	0.00807 (0.0157)	0.00014 (0.000337)	1e-05 (3.02e-05)	0.0336 (0.0591)	0.00149 (0.00312)	0.038 (0.0658)	0.0142 (0.0264)
GENDER	Fisher's exact test	0.736 (0.797)	0.00013 (0.000319)	5e-05 (0.00013)	4e-05 (0.000111)	0.00011 (0.000275)	1e-05 (3.02e-05)	0.178 (0.236)	0.00071 (0.00162)	0.00102 (0.00225)	0.00134 (0.00286)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)			0.0452 (0.0773)	0.101 (0.155)	0.469 (0.544)	0.111 (0.164)	0.858 (0.907)	0.161 (0.22)	0.077 (0.121)	0.138 (0.198)
HISTOLOGICAL TYPE	Fisher's exact test	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)			0.0292 (0.0528)	0.809 (0.862)	0.993 (1.00)	0.759 (0.815)	0.562 (0.636)	0.646 (0.717)	0.638 (0.715)	0.481 (0.554)
YEAR OF TOBACCO SMOKING ONSET	Kruskal-Wallis (anova)			0.0074 (0.0146)	0.0775 (0.121)	0.404 (0.478)	0.175 (0.235)	0.12 (0.175)	0.106 (0.161)	0.148 (0.208)	0.175 (0.235)
RACE	Fisher's exact test	1e-05 (3.02e-05)	3e-05 (8.48e-05)	5e-05 (0.00013)	0.00126 (0.00273)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	1e-05 (3.02e-05)	7e-05 (0.000178)	0.72 (0.787)	0.0763 (0.121)
ETHNICITY	Fisher's exact test	0.109 (0.163)	0.0675 (0.111)	0.402 (0.478)	0.238 (0.3)	0.41 (0.481)	0.0816 (0.126)	0.556 (0.634)	0.256 (0.317)	0.694 (0.765)	0.0663 (0.111)

Clustering Approach #1: 'mRNA CNMF subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	34	24	13	17

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.318 (logrank test), Q value = 0.39

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	16	0.5 - 101.1 (33.2)
subtype1	34	4	1.4 - 101.1 (32.0)
subtype2	24	8	0.5 - 93.3 (38.2)
subtype3	13	1	12.1 - 84.4 (40.0)
subtype4	17	3	0.5 - 76.6 (26.4)

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

'mRNA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.222 (Kruskal-Wallis (anova)), Q value = 0.29

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

	nPatients	Mean (Std.Dev)
ALL	87	60.1 (12.2)
subtype1	33	60.2 (13.8)
subtype2	24	59.2 (10.8)
subtype3	13	65.6 (10.5)
subtype4	17	56.9 (11.2)

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

'mRNA CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0324 (Fisher's exact test), Q value = 0.058

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	45	16	15	6
subtype1	23	4	6	1
subtype2	8	4	8	4
subtype3	8	5	0	0
subtype4	6	3	1	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0111 (Fisher's exact test), Q value = 0.021

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

nPatients	T1	T2	T3
ALL	48	21	19
subtype1	23	4	7
subtype2	9	5	10
subtype3	8	5	0
subtype4	8	7	2

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0767 (Fisher's exact test), Q value = 0.12

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

nPatients	N0	N1
ALL	37	4
subtype1	18	0
subtype2	11	3
subtype3	5	0
subtype4	3	1

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

'mRNA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.18 (Fisher's exact test), Q value = 0.24

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

nPatients	0	1
ALL	77	6
subtype1	33	1
subtype2	20	4
subtype3	13	0
subtype4	11	1

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

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.736 (Fisher's exact test), Q value = 0.8

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

nPatients	FEMALE	MALE
ALL	33	55
subtype1	15	19
subtype2	9	15
subtype3	4	9
subtype4	5	12

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

'mRNA CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	72	16
subtype1	34	0
subtype2	23	1
subtype3	12	1
subtype4	3	14

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	71
subtype1	0	0	32
subtype2	0	1	21
subtype3	0	2	11
subtype4	1	9	7

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'RACE'

'mRNA CNMF subtypes' versus 'ETHNICITY'

P value = 0.109 (Fisher's exact test), Q value = 0.16

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	58
subtype1	5	19
subtype2	2	11
subtype3	0	12
subtype4	0	16

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S12. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	21	24	18	13	12

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.149 (logrank test), Q value = 0.21

Table S13. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	88	16	0.5 - 101.1 (33.2)
subtype1	21	1	1.4 - 59.8 (31.1)
subtype2	24	8	0.5 - 93.3 (38.2)
subtype3	18	4	0.5 - 76.6 (25.3)
subtype4	13	3	11.1 - 101.1 (48.4)
subtype5	12	0	12.1 - 84.4 (40.4)

Figure S11. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.214 (Kruskal-Wallis (anova)), Q value = 0.28

Table S14. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	87	60.1 (12.2)
subtype1	20	57.6 (13.7)
subtype2	24	59.2 (10.8)
subtype3	18	57.0 (10.9)
subtype4	13	64.1 (13.5)
subtype5	12	66.2 (10.8)

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

'mRNA cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 0.0637 (Fisher's exact test), Q value = 0.11

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	45	16	15	6
subtype1	16	2	3	0
subtype2	8	4	8	4
subtype3	7	3	1	1
subtype4	7	2	3	1
subtype5	7	5	0	0

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.0246 (Fisher's exact test), Q value = 0.045

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3
ALL	48	21	19
subtype1	16	2	3
subtype2	9	5	10
subtype3	9	7	2
subtype4	7	2	4
subtype5	7	5	0

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

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.321 (Fisher's exact test), Q value = 0.39

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1
ALL	37	4
subtype1	9	0
subtype2	11	3
subtype3	4	1
subtype4	9	0
subtype5	4	0

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.245 (Fisher's exact test), Q value = 0.31

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	77	6
subtype1	21	0
subtype2	20	4
subtype3	12	1
subtype4	12	1
subtype5	12	0

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.00013 (Fisher's exact test), Q value = 0.00032

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	33	55
subtype1	3	18
subtype2	9	15
subtype3	5	13
subtype4	12	1
subtype5	4	8

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	72	16
subtype1	21	0
subtype2	23	1
subtype3	4	14
subtype4	13	0
subtype5	11	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 3e-05 (Fisher's exact test), Q value = 8.5e-05

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	1	12	71
subtype1	0	0	19
subtype2	0	1	21
subtype3	1	10	7
subtype4	0	0	13
subtype5	0	1	11

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0675 (Fisher's exact test), Q value = 0.11

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	7	58
subtype1	2	12
subtype2	2	11
subtype3	0	16
subtype4	3	7
subtype5	0	12

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

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

Table S23. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	267	304	288

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

P value = 0.00233 (logrank test), Q value = 0.0048

Table S24. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	843	211	0.1 - 194.8 (30.5)
subtype1	255	40	0.1 - 194.8 (26.4)
subtype2	301	78	0.1 - 120.6 (31.2)
subtype3	287	93	0.1 - 141.7 (36.5)

Figure S21. 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 'YEARS_TO_BIRTH'

P value = 0.224 (Kruskal-Wallis (anova)), Q value = 0.29

Table S25. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	853	60.1 (12.6)
subtype1	265	59.4 (13.3)
subtype2	302	59.7 (12.1)
subtype3	286	61.2 (12.4)

Figure S22. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S26. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	448	103	190	100
subtype1	156	38	43	15
subtype2	176	32	60	33
subtype3	116	33	87	52

Figure S23. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S27. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	462	123	251	15
subtype1	162	45	48	6
subtype2	180	39	80	3
subtype3	120	39	123	6

Figure S24. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.124 (Fisher's exact test), Q value = 0.18

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	317	43	6
subtype1	76	12	3
subtype2	116	9	2
subtype3	125	22	1

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00251 (Fisher's exact test), Q value = 0.0051

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	542	89
subtype1	114	8
subtype2	221	32
subtype3	207	49

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 5e-05 (Fisher's exact test), Q value = 0.00013

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	287	572
subtype1	67	200
subtype2	131	173
subtype3	89	199

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0452 (Kruskal-Wallis (anova)), Q value = 0.077

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	115	90.3 (17.6)
subtype1	49	94.5 (7.4)
subtype2	30	91.7 (12.3)
subtype3	36	83.3 (27.0)

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	524	269
subtype1	46	29	192
subtype2	6	247	51
subtype3	14	248	26

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0292 (Kruskal-Wallis (anova)), Q value = 0.053

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	91	30.8 (26.2)
subtype1	52	30.1 (19.0)
subtype2	22	41.9 (41.1)
subtype3	17	18.4 (13.1)

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0074 (Kruskal-Wallis (anova)), Q value = 0.015

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	67	1973.4 (16.4)
subtype1	38	1967.9 (14.0)
subtype2	18	1980.2 (18.6)
subtype3	11	1981.2 (14.0)

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00013

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	116	702
subtype1	2	8	44	199
subtype2	0	2	52	247
subtype3	0	5	20	256

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.402 (Fisher's exact test), Q value = 0.48

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	597
subtype1	10	201
subtype2	16	208
subtype3	16	188

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #4: 'METHLYATION CNMF'

Table S37. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	125	301	210

'METHLYATION CNMF' versus 'Time to Death'

P value = 4.13e-05 (logrank test), Q value = 0.00011

Table S38. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	620	141	0.1 - 194.8 (26.2)
subtype1	121	29	0.1 - 152.0 (42.4)
subtype2	300	93	0.1 - 120.6 (28.9)
subtype3	199	19	0.1 - 194.8 (21.3)

Figure S34. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 3.76e-07 (Kruskal-Wallis (anova)), Q value = 9.8e-06

Table S39. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	631	60.5 (12.6)
subtype1	124	54.2 (14.2)
subtype2	299	61.8 (11.8)
subtype3	208	62.3 (11.7)

Figure S35. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S40. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	338	74	137	75
subtype1	52	30	27	15
subtype2	139	28	80	53
subtype3	147	16	30	7

Figure S36. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S41. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	348	88	180	12
subtype1	53	30	35	6
subtype2	143	38	115	5
subtype3	152	20	30	1

Figure S37. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00073 (Fisher's exact test), Q value = 0.0016

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	214	33	6
subtype1	54	13	3
subtype2	124	10	0
subtype3	36	10	3

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.00807 (Fisher's exact test), Q value = 0.016

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	349	63
subtype1	66	9
subtype2	221	51
subtype3	62	3

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 4e-05 (Fisher's exact test), Q value = 0.00011

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	209	427
subtype1	58	67
subtype2	102	199
subtype3	49	161

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.101 (Kruskal-Wallis (anova)), Q value = 0.16

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	107	92.4 (9.7)
subtype1	19	88.4 (14.6)
subtype2	33	91.2 (9.6)
subtype3	55	94.5 (6.9)

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	315	255
subtype1	66	25	34
subtype2	0	286	15
subtype3	0	4	206

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.809 (Kruskal-Wallis (anova)), Q value = 0.86

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	89	31.1 (26.4)
subtype1	15	27.3 (21.0)
subtype2	19	35.4 (40.3)
subtype3	55	30.7 (21.7)

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.0775 (Kruskal-Wallis (anova)), Q value = 0.12

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	67	1973.1 (16.5)
subtype1	11	1976.9 (14.6)
subtype2	13	1981.1 (21.1)
subtype3	43	1969.8 (14.6)

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00126 (Fisher's exact test), Q value = 0.0027

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	8	102	504
subtype1	0	5	23	93
subtype2	0	2	36	258
subtype3	2	1	43	153

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.238 (Fisher's exact test), Q value = 0.3

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	26	493
subtype1	7	77
subtype2	9	239
subtype3	10	177

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S51. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	118	196	298	253

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0 (logrank test), Q value = 0

Table S52. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	849	211	0.1 - 194.8 (30.6)
subtype1	115	18	0.2 - 152.0 (47.2)
subtype2	195	96	0.1 - 109.9 (28.9)
subtype3	297	69	0.1 - 120.6 (37.4)
subtype4	242	28	0.1 - 194.8 (21.6)

Figure S47. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.000197 (Kruskal-Wallis (anova)), Q value = 0.00046

Table S53. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	859	60.2 (12.6)
subtype1	117	54.8 (14.4)
subtype2	196	60.5 (12.0)
subtype3	297	60.7 (12.5)
subtype4	249	61.8 (11.6)

Figure S48. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S54. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	454	103	189	101
subtype1	56	33	19	10
subtype2	57	20	70	48
subtype3	173	31	61	33
subtype4	168	19	39	10

Figure S49. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S55. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	468	123	251	15
subtype1	56	33	27	2
subtype2	62	24	99	10
subtype3	174	38	84	2
subtype4	176	28	41	1

Figure S50. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S56. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	323	42	6
subtype1	60	5	3
subtype2	85	20	1
subtype3	130	2	0
subtype4	48	15	2

Figure S51. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.00014 (Fisher's exact test), Q value = 0.00034

Table S57. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	547	90
subtype1	72	7
subtype2	139	44
subtype3	248	32
subtype4	88	7

Figure S52. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.00011 (Fisher's exact test), Q value = 0.00027

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	286	579
subtype1	47	71
subtype2	51	145
subtype3	122	176
subtype4	66	187

Figure S53. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.469 (Kruskal-Wallis (anova)), Q value = 0.54

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	115	91.1 (15.4)
subtype1	17	91.8 (8.8)
subtype2	16	84.4 (27.3)
subtype3	25	91.2 (9.7)
subtype4	57	92.8 (14.4)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	529	270
subtype1	64	38	16
subtype2	2	182	12
subtype3	0	296	2
subtype4	0	13	240

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.993 (Kruskal-Wallis (anova)), Q value = 1

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	92	30.6 (26.1)
subtype1	13	39.3 (48.6)
subtype2	6	28.8 (19.2)
subtype3	9	28.2 (20.0)
subtype4	64	29.3 (20.9)

Figure S56. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.404 (Kruskal-Wallis (anova)), Q value = 0.48

Table S62. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	68	1973.2 (16.4)
subtype1	10	1973.5 (17.0)
subtype2	4	1986.0 (14.6)
subtype3	5	1976.0 (23.5)
subtype4	49	1971.8 (15.6)

Figure S57. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	116	708
subtype1	0	3	19	93
subtype2	0	4	13	176
subtype3	0	2	27	264
subtype4	2	6	57	175

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.41 (Fisher's exact test), Q value = 0.48

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	603
subtype1	4	76
subtype2	7	125
subtype3	19	192
subtype4	12	210

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S65. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	88	138	208	159	111	161

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 6.97e-13 (logrank test), Q value = 4.5e-11

Table S66. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	849	211	0.1 - 194.8 (30.6)
subtype1	87	12	2.5 - 152.0 (52.3)
subtype2	134	37	0.1 - 194.8 (19.6)
subtype3	208	45	0.1 - 112.8 (35.7)
subtype4	158	76	0.1 - 109.9 (32.6)
subtype5	110	29	0.1 - 120.6 (46.5)
subtype6	152	12	0.1 - 129.9 (23.6)

Figure S60. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.05e-05 (Kruskal-Wallis (anova)), Q value = 3.1e-05

Table S67. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	859	60.2 (12.6)
subtype1	88	53.8 (14.1)
subtype2	136	61.7 (13.0)
subtype3	207	59.0 (12.8)
subtype4	158	61.6 (11.3)
subtype5	111	62.9 (12.0)
subtype6	159	60.6 (11.5)

Figure S61. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S68. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	454	103	189	101
subtype1	38	30	15	5
subtype2	59	8	42	21
subtype3	122	21	40	25
subtype4	39	20	61	39
subtype5	72	9	22	8
subtype6	124	15	9	3

Figure S62. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S69. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	468	123	251	15
subtype1	38	30	19	1
subtype2	64	12	50	7
subtype3	123	27	56	2
subtype4	43	24	87	5
subtype5	72	10	29	0
subtype6	128	20	10	0

Figure S63. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S70. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	323	42	6
subtype1	51	2	2
subtype2	33	26	3
subtype3	77	2	0
subtype4	76	11	0
subtype5	59	1	0
subtype6	27	0	1

Figure S64. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S71. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	547	90
subtype1	55	3
subtype2	62	14
subtype3	177	24
subtype4	109	40
subtype5	93	8
subtype6	51	1

Figure S65. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	286	579
subtype1	33	55
subtype2	56	82
subtype3	12	196
subtype4	48	111
subtype5	108	3
subtype6	29	132

Figure S66. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.111 (Kruskal-Wallis (anova)), Q value = 0.16

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	115	91.1 (15.4)
subtype1	12	89.2 (9.0)
subtype2	21	87.1 (24.1)
subtype3	16	94.4 (8.1)
subtype4	11	82.7 (28.7)
subtype5	13	90.0 (10.8)
subtype6	42	95.0 (7.1)

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	529	270
subtype1	64	19	5
subtype2	2	32	104
subtype3	0	208	0
subtype4	0	155	4
subtype5	0	111	0
subtype6	0	4	157

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.759 (Kruskal-Wallis (anova)), Q value = 0.82

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	92	30.6 (26.1)
subtype1	12	27.2 (22.1)
subtype2	24	38.5 (39.0)
subtype3	6	34.7 (20.6)
subtype4	7	26.7 (19.4)
subtype5	2	9.5 (0.7)
subtype6	41	28.1 (19.0)

Figure S69. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.175 (Kruskal-Wallis (anova)), Q value = 0.24

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	68	1973.2 (16.4)
subtype1	9	1976.0 (15.9)
subtype2	21	1974.0 (15.2)
subtype3	5	1976.0 (23.5)
subtype4	4	1989.8 (16.7)
subtype6	29	1969.0 (15.2)

Figure S70. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S77. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	116	708
subtype1	0	2	10	74
subtype2	0	5	36	92
subtype3	0	3	14	186
subtype4	0	2	11	145
subtype5	0	1	12	97
subtype6	2	2	33	114

Figure S71. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0816 (Fisher's exact test), Q value = 0.13

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	42	603
subtype1	4	51
subtype2	8	105
subtype3	9	131
subtype4	5	110
subtype5	11	67
subtype6	5	139

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S79. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	316	188	345

'MIRSEQ CNMF' versus 'Time to Death'

P value = 1.11e-07 (logrank test), Q value = 3.6e-06

Table S80. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	833	209	0.1 - 194.8 (30.5)
subtype1	303	38	0.1 - 194.8 (26.4)
subtype2	186	66	0.1 - 129.9 (26.4)
subtype3	344	105	0.2 - 120.6 (37.2)

Figure S73. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.323 (Kruskal-Wallis (anova)), Q value = 0.39

Table S81. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	844	60.1 (12.6)
subtype1	312	59.4 (13.2)
subtype2	188	59.6 (12.3)
subtype3	344	61.1 (12.0)

Figure S74. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S82. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	441	101	189	100
subtype1	183	47	55	15
subtype2	85	17	51	33
subtype3	173	37	83	52

Figure S75. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S83. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	455	121	250	15
subtype1	191	56	59	4
subtype2	87	18	72	9
subtype3	177	47	119	2

Figure S76. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 8.5e-05

Table S84. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	311	43	6
subtype1	88	20	3
subtype2	73	16	3
subtype3	150	7	0

Figure S77. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.0336 (Fisher's exact test), Q value = 0.059

Table S85. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	531	89
subtype1	128	11
subtype2	123	26
subtype3	280	52

Figure S78. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.178 (Fisher's exact test), Q value = 0.24

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	280	569
subtype1	92	224
subtype2	67	121
subtype3	121	224

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.858 (Kruskal-Wallis (anova)), Q value = 0.91

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	116	90.3 (17.5)
subtype1	63	91.9 (14.1)
subtype2	23	86.5 (23.9)
subtype3	30	90.0 (18.6)

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	512	271
subtype1	59	25	232
subtype2	6	145	37
subtype3	1	342	2

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.562 (Kruskal-Wallis (anova)), Q value = 0.64

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	30.3 (26.1)
subtype1	70	29.9 (27.8)
subtype2	15	33.6 (20.8)
subtype3	8	27.2 (21.5)

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.12 (Kruskal-Wallis (anova)), Q value = 0.18

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	69	1973.1 (16.3)
subtype1	53	1970.8 (15.2)
subtype2	11	1979.8 (15.3)
subtype3	5	1982.2 (25.0)

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CNMF' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	116	692
subtype1	2	8	58	233
subtype2	0	3	37	145
subtype3	0	4	21	314

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.556 (Fisher's exact test), Q value = 0.63

Table S92. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	40	594
subtype1	14	241
subtype2	7	124
subtype3	19	229

Figure S85. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S93. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	77	574	198

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 7.81e-09 (logrank test), Q value = 3.4e-07

Table S94. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	833	209	0.1 - 194.8 (30.5)
subtype1	76	12	2.5 - 152.0 (60.3)
subtype2	570	183	0.1 - 129.9 (32.2)
subtype3	187	14	0.1 - 194.8 (21.3)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 4.58e-06 (Kruskal-Wallis (anova)), Q value = 3e-05

Table S95. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	844	60.1 (12.6)
subtype1	77	53.0 (13.9)
subtype2	571	60.6 (12.3)
subtype3	196	61.7 (11.7)

Figure S87. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S96. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	441	101	189	100
subtype1	28	29	15	5
subtype2	273	55	153	91
subtype3	140	17	21	4

Figure S88. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S97. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	455	121	250	15
subtype1	28	29	19	1
subtype2	280	68	211	13
subtype3	147	24	20	1

Figure S89. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.161 (Fisher's exact test), Q value = 0.22

Table S98. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	311	43	6
subtype1	49	2	2
subtype2	228	36	4
subtype3	34	5	0

Figure S90. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.00149 (Fisher's exact test), Q value = 0.0031

Table S99. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	531	89
subtype1	50	3
subtype2	421	84
subtype3	60	2

Figure S91. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00071 (Fisher's exact test), Q value = 0.0016

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	280	569
subtype1	28	49
subtype2	208	366
subtype3	44	154

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.161 (Kruskal-Wallis (anova)), Q value = 0.22

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	116	90.3 (17.5)
subtype1	8	92.5 (7.1)
subtype2	62	86.9 (22.6)
subtype3	46	94.6 (7.2)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	66	512	271
subtype1	60	16	1
subtype2	6	491	77
subtype3	0	5	193

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.646 (Kruskal-Wallis (anova)), Q value = 0.72

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	93	30.3 (26.1)
subtype1	10	25.6 (23.0)
subtype2	30	34.6 (34.3)
subtype3	53	28.8 (21.0)

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.106 (Kruskal-Wallis (anova)), Q value = 0.16

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	69	1973.1 (16.3)
subtype1	7	1975.0 (16.2)
subtype2	21	1979.1 (18.7)
subtype3	41	1969.7 (14.3)

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00018

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	15	116	692
subtype1	0	2	6	67
subtype2	0	10	65	489
subtype3	2	3	45	136

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.256 (Fisher's exact test), Q value = 0.32

Table S106. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	40	594
subtype1	4	41
subtype2	29	386
subtype3	7	167

Figure S98. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S107. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	145	68	59	126

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

P value = 2.35e-06 (logrank test), Q value = 3e-05

Table S108. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	386	80	0.1 - 194.8 (26.0)
subtype1	138	13	0.1 - 194.8 (22.9)
subtype2	66	9	1.2 - 152.0 (41.6)
subtype3	57	20	0.1 - 129.9 (15.2)
subtype4	125	38	0.1 - 120.6 (33.0)

Figure S99. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00264 (Kruskal-Wallis (anova)), Q value = 0.0053

Table S109. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	394	59.8 (12.7)
subtype1	144	61.5 (11.8)
subtype2	66	53.8 (14.5)
subtype3	59	61.3 (12.8)
subtype4	125	60.2 (11.9)

Figure S100. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S110. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	215	48	79	45
subtype1	102	11	19	4
subtype2	27	17	18	6
subtype3	23	4	15	15
subtype4	63	16	27	20

Figure S101. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S111. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	223	53	106	9
subtype1	106	13	20	1
subtype2	27	17	23	1
subtype3	24	5	23	5
subtype4	66	18	40	2

Figure S102. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00063 (Fisher's exact test), Q value = 0.0015

Table S112. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	122	29	6
subtype1	26	6	1
subtype2	33	6	2
subtype3	13	12	3
subtype4	50	5	0

Figure S103. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.038 (Fisher's exact test), Q value = 0.066

Table S113. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	33
subtype1	45	2
subtype2	36	4
subtype3	25	8
subtype4	89	19

Figure S104. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.00102 (Fisher's exact test), Q value = 0.0022

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	128	270
subtype1	31	114
subtype2	32	36
subtype3	23	36
subtype4	42	84

Figure S105. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.077 (Kruskal-Wallis (anova)), Q value = 0.12

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	91.3 (14.7)
subtype1	39	95.4 (6.0)
subtype2	8	87.5 (10.4)
subtype3	14	82.1 (28.6)
subtype4	17	91.2 (11.1)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	48	142	208
subtype1	0	1	144
subtype2	46	4	18
subtype3	2	17	40
subtype4	0	120	6

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.638 (Kruskal-Wallis (anova)), Q value = 0.71

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	71	31.5 (28.3)
subtype1	42	30.7 (21.1)
subtype2	13	24.6 (21.6)
subtype3	9	33.6 (25.2)
subtype4	7	46.1 (64.5)

Figure S108. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 0.21

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	55	1972.2 (17.4)
subtype1	33	1967.6 (14.6)
subtype2	11	1979.1 (17.6)
subtype3	6	1979.0 (16.9)
subtype4	5	1979.8 (28.6)

Figure S109. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.72 (Fisher's exact test), Q value = 0.79

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	9	68	305
subtype1	2	2	24	112
subtype2	0	2	11	52
subtype3	0	2	14	41
subtype4	0	3	19	100

Figure S110. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.694 (Fisher's exact test), Q value = 0.76

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	20	289
subtype1	7	123
subtype2	4	34
subtype3	3	43
subtype4	6	89

Figure S111. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S121. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	51	82	88	131	46

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

P value = 7.63e-07 (logrank test), Q value = 1.7e-05

Table S122. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	386	80	0.1 - 194.8 (26.0)
subtype1	48	4	0.1 - 194.8 (19.7)
subtype2	78	4	0.5 - 123.6 (25.8)
subtype3	85	28	0.1 - 100.9 (19.7)
subtype4	130	37	0.1 - 120.6 (35.4)
subtype5	45	7	2.5 - 152.0 (59.1)

Figure S112. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0106 (Kruskal-Wallis (anova)), Q value = 0.02

Table S123. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	394	59.8 (12.7)
subtype1	51	60.3 (12.0)
subtype2	81	61.1 (11.4)
subtype3	86	61.5 (13.9)
subtype4	130	59.9 (11.7)
subtype5	46	53.1 (14.6)

Figure S113. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM_DISEASESTAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S124. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	215	48	79	45
subtype1	43	3	2	1
subtype2	56	7	12	1
subtype3	37	4	27	17
subtype4	66	17	26	22
subtype5	13	17	12	4

Figure S114. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM_DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S125. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	223	53	106	9
subtype1	44	2	2	1
subtype2	59	9	12	0
subtype3	39	6	35	5
subtype4	68	19	41	3
subtype5	13	17	16	0

Figure S115. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S126. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2
ALL	122	29	6
subtype1	10	1	0
subtype2	13	3	0
subtype3	16	19	4
subtype4	54	4	0
subtype5	29	2	2

Figure S116. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0142 (Fisher's exact test), Q value = 0.026

Table S127. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	33
subtype1	16	0
subtype2	23	0
subtype3	37	10
subtype4	91	21
subtype5	28	2

Figure S117. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.00134 (Fisher's exact test), Q value = 0.0029

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	128	270
subtype1	13	38
subtype2	14	68
subtype3	38	50
subtype4	43	88
subtype5	20	26

Figure S118. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.138 (Kruskal-Wallis (anova)), Q value = 0.2

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	91.3 (14.7)
subtype1	14	95.7 (5.1)
subtype2	21	94.8 (6.8)
subtype3	20	83.0 (24.1)
subtype4	20	92.5 (10.7)
subtype5	3	93.3 (11.5)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 1e-05 (Fisher's exact test), Q value = 3e-05

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	KIDNEY CHROMOPHOBE	KIDNEY CLEAR CELL RENAL CARCINOMA	KIDNEY PAPILLARY RENAL CELL CARCINOMA
ALL	48	142	208
subtype1	0	0	51
subtype2	0	1	81
subtype3	6	11	71
subtype4	0	127	4
subtype5	42	3	1

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.481 (Kruskal-Wallis (anova)), Q value = 0.55

Table S131. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	71	31.5 (28.3)
subtype1	19	29.4 (21.4)
subtype2	19	34.2 (25.3)
subtype3	16	37.4 (41.9)
subtype4	8	26.0 (23.6)
subtype5	9	24.6 (24.2)

Figure S121. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAR_OF_TOBACCO_SMOKING_ONSET'

P value = 0.175 (Kruskal-Wallis (anova)), Q value = 0.24

Table S132. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

	nPatients	Mean (Std.Dev)
ALL	55	1972.2 (17.4)
subtype1	14	1969.6 (15.2)
subtype2	15	1966.4 (15.6)
subtype3	15	1974.0 (17.0)
subtype4	5	1988.8 (24.0)
subtype5	6	1974.7 (17.7)

Figure S122. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'YEAR_OF_TOBACCO_SMOKING_ONSET'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.0763 (Fisher's exact test), Q value = 0.12

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	9	68	305
subtype1	0	1	7	41
subtype2	2	1	15	61
subtype3	0	3	22	59
subtype4	0	3	22	103
subtype5	0	1	2	41

Figure S123. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.0663 (Fisher's exact test), Q value = 0.11

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	20	289
subtype1	4	44
subtype2	1	70
subtype3	5	65
subtype4	6	92
subtype5	4	18

Figure S124. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/KIPAN-TP/15165233/KIPAN-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/KIPAN-TP/15081103/KIPAN-TP.merged_data.txt
Number of patients = 869
Number of clustering approaches = 10
Number of selected clinical features = 13
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

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.

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] 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)

[5] 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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