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 412 patients, 68 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'PATHOLOGY_N_STAGE' and 'ETHNICITY'.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
CNMF clustering analysis on RPPA data identified 3 subtypes that correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
Consensus hierarchical clustering analysis on RPPA data identified 4 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'PATHOLOGY_M_STAGE', 'GENDER', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', and 'NUMBER_OF_LYMPH_NODES'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'GENDER', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', and 'RACE'.
4 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'Time to Death', 'YEARS_TO_BIRTH', 'PATHOLOGIC_STAGE', 'PATHOLOGY_T_STAGE', 'PATHOLOGY_N_STAGE', 'RADIATION_THERAPY', 'KARNOFSKY_PERFORMANCE_SCORE', 'NUMBER_OF_LYMPH_NODES', and 'RACE'.

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, 68 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.903 (0.924)	0.163 (0.235)	0.0567 (0.107)	0.0192 (0.0462)	0.00106 (0.00392)	0.00213 (0.00693)	0.0874 (0.148)	0.304 (0.396)	0.00478 (0.0145)	0.0315 (0.0671)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.0626 (0.116)	0.00901 (0.026)	0.0703 (0.125)	0.0499 (0.0954)	0.534 (0.604)	0.000113 (0.000639)	0.112 (0.172)	0.000486 (0.00211)	0.000586 (0.00232)	0.000322 (0.00155)
PATHOLOGIC STAGE	Fisher's exact test	0.0804 (0.14)	0.00466 (0.0144)	0.00034 (0.00158)	4e-05 (0.000274)	1e-05 (1e-04)	1e-05 (1e-04)	2e-05 (0.000162)	0.00026 (0.0013)	1e-05 (1e-04)	3e-05 (0.000229)
PATHOLOGY T STAGE	Fisher's exact test	0.785 (0.837)	0.0117 (0.0305)	2e-05 (0.000162)	1e-05 (1e-04)	1e-05 (1e-04)	1e-05 (1e-04)	4e-05 (0.000274)	5e-05 (0.00031)	2e-05 (0.000162)	7e-05 (0.000414)
PATHOLOGY N STAGE	Fisher's exact test	0.0104 (0.0283)	0.366 (0.454)	0.0143 (0.0358)	0.00963 (0.0272)	0.0108 (0.0286)	1e-05 (1e-04)	0.00055 (0.00231)	0.0162 (0.0398)	1e-05 (1e-04)	0.00175 (0.00599)
PATHOLOGY M STAGE	Fisher's exact test	0.808 (0.847)	0.95 (0.964)	0.0916 (0.151)	0.0331 (0.0695)	0.176 (0.251)	0.305 (0.396)	0.763 (0.82)	0.0806 (0.14)	0.43 (0.508)	0.146 (0.218)
GENDER	Fisher's exact test	0.861 (0.896)	0.338 (0.427)	0.112 (0.172)	0.00206 (0.00687)	0.111 (0.172)	0.0125 (0.0318)	0.1 (0.163)	0.0345 (0.0712)	0.313 (0.402)	0.509 (0.585)
RADIATION THERAPY	Fisher's exact test	0.235 (0.322)	0.102 (0.164)	0.0497 (0.0954)	0.0678 (0.124)	0.475 (0.551)	0.0286 (0.062)	0.389 (0.468)	0.0275 (0.0617)	0.0199 (0.0464)	0.0204 (0.0464)
KARNOFSKY PERFORMANCE SCORE	Kruskal-Wallis (anova)	0.376 (0.461)	0.00163 (0.00574)	0.0202 (0.0464)	0.0368 (0.0736)	0.0392 (0.0773)	0.00707 (0.0209)	0.194 (0.269)	0.00998 (0.0276)	0.00155 (0.00559)	0.000415 (0.00186)
NUMBER PACK YEARS SMOKED	Kruskal-Wallis (anova)	0.731 (0.792)	0.606 (0.675)	0.161 (0.235)	0.523 (0.597)	0.383 (0.466)	0.714 (0.787)	0.608 (0.675)	0.731 (0.792)	0.803 (0.847)	0.402 (0.48)
NUMBER OF LYMPH NODES	Kruskal-Wallis (anova)	0.11 (0.172)	0.0694 (0.125)	0.274 (0.368)	0.146 (0.218)	0.195 (0.269)	2.26e-06 (1e-04)	0.000991 (0.00379)	0.0905 (0.151)	0.000241 (0.00125)	0.0361 (0.0734)
RACE	Fisher's exact test	0.189 (0.267)	0.00059 (0.00232)	1e-05 (1e-04)	1e-05 (1e-04)	1e-05 (1e-04)	1e-05 (1e-04)	0.155 (0.229)	0.00427 (0.0135)	5e-05 (0.00031)	0.00013 (0.000704)
ETHNICITY	Fisher's exact test	0.028 (0.0618)	0.0876 (0.148)	0.443 (0.519)	0.352 (0.44)	0.336 (0.427)	0.271 (0.367)	0.284 (0.377)	1 (1.00)	0.875 (0.902)	0.963 (0.97)

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	4
Number of samples	169	140	59	40

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

P value = 0.903 (logrank test), Q value = 0.92

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	406	177	0.1 - 166.0 (17.2)
subtype1	167	71	0.1 - 163.3 (16.5)
subtype2	140	59	0.5 - 166.0 (18.1)
subtype3	59	28	1.8 - 165.7 (15.8)
subtype4	40	19	1.1 - 112.8 (21.7)

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 'YEARS_TO_BIRTH'

P value = 0.0626 (Kruskal-Wallis (anova)), Q value = 0.12

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

	nPatients	Mean (Std.Dev)
ALL	407	68.1 (10.6)
subtype1	169	66.4 (11.0)
subtype2	139	69.5 (9.7)
subtype3	59	69.4 (9.4)
subtype4	40	68.3 (12.8)

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0804 (Fisher's exact test), Q value = 0.14

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	129	140	135
subtype1	1	61	65	41
subtype2	1	37	42	59
subtype3	0	19	17	23
subtype4	0	12	16	12

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.785 (Fisher's exact test), Q value = 0.84

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

nPatients	T0+T1+T2	T3	T4
ALL	123	195	58
subtype1	55	74	27
subtype2	40	68	21
subtype3	17	31	6
subtype4	11	22	4

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 = 0.0104 (Fisher's exact test), Q value = 0.028

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

nPatients	N0	N1	N2	N3
ALL	235	47	75	9
subtype1	117	16	20	2
subtype2	69	19	34	5
subtype3	28	6	16	1
subtype4	21	6	5	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 = 0.808 (Fisher's exact test), Q value = 0.85

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

nPatients	0	1
ALL	192	11
subtype1	92	5
subtype2	66	4
subtype3	20	2
subtype4	14	0

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 = 0.861 (Fisher's exact test), Q value = 0.9

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

nPatients	FEMALE	MALE
ALL	107	301
subtype1	42	127
subtype2	36	104
subtype3	17	42
subtype4	12	28

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 'RADIATION_THERAPY'

P value = 0.235 (Fisher's exact test), Q value = 0.32

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

nPatients	NO	YES
ALL	364	19
subtype1	151	12
subtype2	126	3
subtype3	50	3
subtype4	37	1

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

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.376 (Kruskal-Wallis (anova)), Q value = 0.46

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	135	83.0 (13.7)
subtype1	50	81.6 (13.1)
subtype2	47	84.5 (14.4)
subtype3	21	84.8 (9.3)
subtype4	17	81.2 (18.0)

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.731 (Kruskal-Wallis (anova)), Q value = 0.79

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	222	39.0 (53.1)
subtype1	87	36.2 (26.7)
subtype2	77	44.6 (83.5)
subtype3	31	38.5 (21.6)
subtype4	27	32.6 (22.6)

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'Copy Number Ratio CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.11 (Kruskal-Wallis (anova)), Q value = 0.17

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	297	2.1 (7.0)
subtype1	114	1.2 (3.4)
subtype2	109	3.0 (10.2)
subtype3	46	2.3 (5.1)
subtype4	28	1.8 (4.5)

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.189 (Fisher's exact test), Q value = 0.27

Table S13. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	43	22	327
subtype1	27	9	131
subtype2	11	8	112
subtype3	4	3	51
subtype4	1	2	33

Figure S12. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'RACE'

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.028 (Fisher's exact test), Q value = 0.062

Table S14. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	369
subtype1	1	161
subtype2	6	120
subtype3	0	55
subtype4	2	33

Figure S13. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S15. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	139	117	71	85

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.163 (logrank test), Q value = 0.24

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	410	179	0.1 - 166.0 (17.2)
subtype1	138	65	0.7 - 166.0 (15.8)
subtype2	117	43	0.4 - 130.9 (18.8)
subtype3	70	30	0.1 - 104.7 (15.5)
subtype4	85	41	0.5 - 86.3 (16.8)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.00901 (Kruskal-Wallis (anova)), Q value = 0.026

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

	nPatients	Mean (Std.Dev)
ALL	411	68.1 (10.6)
subtype1	139	69.4 (9.7)
subtype2	116	69.8 (10.0)
subtype3	71	65.7 (10.9)
subtype4	85	65.5 (11.8)

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

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.00466 (Fisher's exact test), Q value = 0.014

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	141	136
subtype1	0	30	65	44
subtype2	1	39	34	41
subtype3	1	26	18	26
subtype4	0	36	24	25

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0117 (Fisher's exact test), Q value = 0.03

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	196	59
subtype1	27	82	21
subtype2	38	51	13
subtype3	25	28	13
subtype4	34	35	12

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.366 (Fisher's exact test), Q value = 0.45

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	238	47	76	9
subtype1	83	16	24	4
subtype2	62	16	21	3
subtype3	39	4	20	1
subtype4	54	11	11	1

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.95 (Fisher's exact test), Q value = 0.96

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	196	11
subtype1	54	4
subtype2	58	3
subtype3	46	2
subtype4	38	2

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.338 (Fisher's exact test), Q value = 0.43

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	108	304
subtype1	40	99
subtype2	34	83
subtype3	13	58
subtype4	21	64

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.102 (Fisher's exact test), Q value = 0.16

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	366	19
subtype1	126	5
subtype2	106	2
subtype3	60	5
subtype4	74	7

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00163 (Kruskal-Wallis (anova)), Q value = 0.0057

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	136	83.1 (13.7)
subtype1	39	87.7 (10.1)
subtype2	40	85.5 (11.1)
subtype3	24	79.2 (11.8)
subtype4	33	77.6 (18.5)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHLYATION CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.606 (Kruskal-Wallis (anova)), Q value = 0.68

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	224	39.0 (52.9)
subtype1	81	37.3 (27.9)
subtype2	63	34.6 (25.4)
subtype3	34	32.5 (21.5)
subtype4	46	53.1 (104.9)

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'METHLYATION CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0694 (Kruskal-Wallis (anova)), Q value = 0.13

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	297	2.1 (7.0)
subtype1	113	1.3 (2.6)
subtype2	78	1.9 (4.9)
subtype3	44	3.4 (6.9)
subtype4	62	2.8 (12.6)

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00059 (Fisher's exact test), Q value = 0.0023

Table S27. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	327
subtype1	5	8	121
subtype2	11	7	93
subtype3	18	2	49
subtype4	10	6	64

Figure S25. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.0876 (Fisher's exact test), Q value = 0.15

Table S28. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	371
subtype1	2	127
subtype2	2	103
subtype3	0	67
subtype4	5	74

Figure S26. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #3: 'RPPA CNMF subtypes'

Table S29. Description of clustering approach #3: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	123	116	105

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0567 (logrank test), Q value = 0.11

Table S30. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	342	155	0.1 - 166.0 (17.4)
subtype1	123	68	0.7 - 166.0 (16.8)
subtype2	115	36	0.1 - 97.1 (16.3)
subtype3	104	51	0.9 - 165.7 (19.0)

Figure S27. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0703 (Kruskal-Wallis (anova)), Q value = 0.13

Table S31. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	343	67.9 (10.5)
subtype1	123	69.0 (9.0)
subtype2	116	65.8 (11.6)
subtype3	104	68.8 (10.7)

Figure S28. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00034 (Fisher's exact test), Q value = 0.0016

Table S32. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	105	119	116
subtype1	0	30	48	45
subtype2	2	53	31	29
subtype3	0	22	40	42

Figure S29. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00016

Table S33. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	100	169	48
subtype1	20	73	17
subtype2	52	39	11
subtype3	28	57	20

Figure S30. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.0143 (Fisher's exact test), Q value = 0.036

Table S34. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	200	39	66	8
subtype1	66	13	30	1
subtype2	79	11	14	1
subtype3	55	15	22	6

Figure S31. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0916 (Fisher's exact test), Q value = 0.15

Table S35. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	163	10
subtype1	41	6
subtype2	72	2
subtype3	50	2

Figure S32. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.112 (Fisher's exact test), Q value = 0.17

Table S36. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	259
subtype1	38	85
subtype2	27	89
subtype3	20	85

Figure S33. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0497 (Fisher's exact test), Q value = 0.095

Table S37. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	306	13
subtype1	109	4
subtype2	98	8
subtype3	99	1

Figure S34. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0202 (Kruskal-Wallis (anova)), Q value = 0.046

Table S38. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	83.7 (12.8)
subtype1	47	87.2 (6.5)
subtype2	39	78.7 (16.3)
subtype3	31	84.5 (13.4)

Figure S35. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.161 (Kruskal-Wallis (anova)), Q value = 0.24

Table S39. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	187	39.8 (56.9)
subtype1	74	49.0 (85.1)
subtype2	60	33.1 (27.4)
subtype3	53	34.7 (18.7)

Figure S36. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.274 (Kruskal-Wallis (anova)), Q value = 0.37

Table S40. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	253	2.2 (7.5)
subtype1	96	2.2 (4.7)
subtype2	69	2.6 (12.2)
subtype3	88	1.9 (4.5)

Figure S37. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S41. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	40	20	268
subtype1	5	6	106
subtype2	32	8	71
subtype3	3	6	91

Figure S38. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RPPA CNMF subtypes' versus 'ETHNICITY'

P value = 0.443 (Fisher's exact test), Q value = 0.52

Table S42. Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	305
subtype1	4	112
subtype2	1	103
subtype3	3	90

Figure S39. Get High-res Image Clustering Approach #3: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #4: 'RPPA cHierClus subtypes'

Table S43. Description of clustering approach #4: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	90	122	100	32

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.0192 (logrank test), Q value = 0.046

Table S44. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	342	155	0.1 - 166.0 (17.4)
subtype1	90	52	1.1 - 166.0 (16.7)
subtype2	121	38	0.1 - 97.1 (17.6)
subtype3	99	52	1.2 - 165.7 (16.3)
subtype4	32	13	0.7 - 125.5 (19.8)

Figure S40. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.0499 (Kruskal-Wallis (anova)), Q value = 0.095

Table S45. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	343	67.9 (10.5)
subtype1	90	69.4 (8.6)
subtype2	121	66.1 (11.5)
subtype3	100	69.3 (10.9)
subtype4	32	65.7 (9.1)

Figure S41. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RPPA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00027

Table S46. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	105	119	116
subtype1	0	14	36	40
subtype2	2	55	34	29
subtype3	0	22	39	39
subtype4	0	14	10	8

Figure S42. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S47. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	100	169	48
subtype1	15	56	18
subtype2	58	40	14
subtype3	22	59	15
subtype4	5	14	1

Figure S43. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.00963 (Fisher's exact test), Q value = 0.027

Table S48. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	200	39	66	8
subtype1	44	10	28	2
subtype2	82	10	17	1
subtype3	55	18	17	4
subtype4	19	1	4	1

Figure S44. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.0331 (Fisher's exact test), Q value = 0.07

Table S49. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	163	10
subtype1	34	3
subtype2	77	2
subtype3	42	2
subtype4	10	3

Figure S45. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.00206 (Fisher's exact test), Q value = 0.0069

Table S50. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	85	259
subtype1	33	57
subtype2	22	100
subtype3	18	82
subtype4	12	20

Figure S46. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0678 (Fisher's exact test), Q value = 0.12

Table S51. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	306	13
subtype1	79	2
subtype2	108	7
subtype3	92	1
subtype4	27	3

Figure S47. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RPPA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0368 (Kruskal-Wallis (anova)), Q value = 0.074

Table S52. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	117	83.7 (12.8)
subtype1	24	87.1 (6.9)
subtype2	41	78.8 (16.0)
subtype3	34	85.3 (12.6)
subtype4	18	87.2 (6.7)

Figure S48. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RPPA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.523 (Kruskal-Wallis (anova)), Q value = 0.6

Table S53. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	187	39.8 (56.9)
subtype1	55	40.5 (30.5)
subtype2	61	34.3 (26.1)
subtype3	52	48.3 (98.4)
subtype4	19	32.2 (23.2)

Figure S49. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RPPA cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.146 (Kruskal-Wallis (anova)), Q value = 0.22

Table S54. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	253	2.2 (7.5)
subtype1	77	2.5 (5.0)
subtype2	75	2.7 (11.8)
subtype3	82	1.8 (4.5)
subtype4	19	1.1 (2.8)

Figure S50. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RPPA cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S55. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	40	20	268
subtype1	3	3	79
subtype2	32	7	77
subtype3	5	7	84
subtype4	0	3	28

Figure S51. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RPPA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.352 (Fisher's exact test), Q value = 0.44

Table S56. Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	305
subtype1	3	80
subtype2	1	106
subtype3	4	87
subtype4	0	32

Figure S52. Get High-res Image Clustering Approach #4: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #5: 'RNAseq CNMF subtypes'

Table S57. Description of clustering approach #5: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	116	182	110

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00106 (logrank test), Q value = 0.0039

Table S58. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	406	177	0.1 - 166.0 (17.0)
subtype1	115	63	0.7 - 166.0 (17.9)
subtype2	181	55	0.1 - 112.4 (17.4)
subtype3	110	59	1.8 - 165.7 (15.5)

Figure S53. 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 = 0.534 (Kruskal-Wallis (anova)), Q value = 0.6

Table S59. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	407	68.1 (10.6)
subtype1	116	68.8 (8.9)
subtype2	181	67.4 (11.4)
subtype3	110	68.5 (10.9)

Figure S54. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S60. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	130	140	134
subtype1	0	18	49	49
subtype2	2	79	50	49
subtype3	0	33	41	36

Figure S55. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S61. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	123	194	58
subtype1	17	77	18
subtype2	75	63	24
subtype3	31	54	16

Figure S56. 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 = 0.0108 (Fisher's exact test), Q value = 0.029

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

nPatients	N0	N1	N2	N3
ALL	236	46	75	9
subtype1	61	16	30	2
subtype2	112	11	32	4
subtype3	63	19	13	3

Figure S57. 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 = 0.176 (Fisher's exact test), Q value = 0.25

Table S63. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	196	11
subtype1	39	5
subtype2	109	4
subtype3	48	2

Figure S58. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.111 (Fisher's exact test), Q value = 0.17

Table S64. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	301
subtype1	29	87
subtype2	41	141
subtype3	37	73

Figure S59. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.475 (Fisher's exact test), Q value = 0.55

Table S65. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	362	19
subtype1	102	5
subtype2	160	11
subtype3	100	3

Figure S60. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0392 (Kruskal-Wallis (anova)), Q value = 0.077

Table S66. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	134	83.0 (13.8)
subtype1	35	87.1 (8.9)
subtype2	63	80.5 (15.0)
subtype3	36	83.3 (14.7)

Figure S61. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.383 (Kruskal-Wallis (anova)), Q value = 0.47

Table S67. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	222	39.2 (53.1)
subtype1	73	37.3 (27.6)
subtype2	92	34.7 (26.7)
subtype3	57	48.8 (94.2)

Figure S62. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.195 (Kruskal-Wallis (anova)), Q value = 0.27

Table S68. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	293	2.1 (7.0)
subtype1	99	2.2 (4.4)
subtype2	112	2.7 (10.3)
subtype3	82	1.1 (2.9)

Figure S63. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S69. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	3	5	107
subtype2	38	10	127
subtype3	3	8	90

Figure S64. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.336 (Fisher's exact test), Q value = 0.43

Table S70. Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	368
subtype1	2	110
subtype2	2	164
subtype3	4	94

Figure S65. Get High-res Image Clustering Approach #5: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #6: 'RNAseq cHierClus subtypes'

Table S71. Description of clustering approach #6: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	119	113	176

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00213 (logrank test), Q value = 0.0069

Table S72. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	406	177	0.1 - 166.0 (17.0)
subtype1	118	59	1.1 - 130.9 (18.0)
subtype2	112	27	0.1 - 94.9 (16.6)
subtype3	176	91	0.5 - 166.0 (15.7)

Figure S66. 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 = 0.000113 (Kruskal-Wallis (anova)), Q value = 0.00064

Table S73. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	407	68.1 (10.6)
subtype1	119	71.3 (9.5)
subtype2	112	65.1 (10.8)
subtype3	176	67.8 (10.7)

Figure S67. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S74. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	130	140	134
subtype1	0	17	40	62
subtype2	2	65	27	17
subtype3	0	48	73	55

Figure S68. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S75. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	123	194	58
subtype1	21	68	25
subtype2	59	29	10
subtype3	43	97	23

Figure S69. 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 = 1e-04

Table S76. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	236	46	75	9
subtype1	52	17	40	4
subtype2	80	5	10	1
subtype3	104	24	25	4

Figure S70. 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 = 0.305 (Fisher's exact test), Q value = 0.4

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

nPatients	0	1
ALL	196	11
subtype1	49	4
subtype2	78	2
subtype3	69	5

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0125 (Fisher's exact test), Q value = 0.032

Table S78. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	301
subtype1	31	88
subtype2	19	94
subtype3	57	119

Figure S72. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0286 (Fisher's exact test), Q value = 0.062

Table S79. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	362	19
subtype1	104	3
subtype2	100	11
subtype3	158	5

Figure S73. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00707 (Kruskal-Wallis (anova)), Q value = 0.021

Table S80. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	134	83.0 (13.8)
subtype1	32	85.0 (12.2)
subtype2	43	78.4 (15.6)
subtype3	59	85.3 (12.5)

Figure S74. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.714 (Kruskal-Wallis (anova)), Q value = 0.79

Table S81. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	222	39.2 (53.1)
subtype1	58	39.0 (31.3)
subtype2	65	34.3 (25.0)
subtype3	99	42.4 (73.3)

Figure S75. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'RNAseq cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 2.26e-06 (Kruskal-Wallis (anova)), Q value = 1e-04

Table S82. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	293	2.1 (7.0)
subtype1	97	2.7 (4.7)
subtype2	60	2.8 (13.2)
subtype3	136	1.4 (3.9)

Figure S76. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S83. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	6	8	103
subtype2	32	4	72
subtype3	6	11	149

Figure S77. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.271 (Fisher's exact test), Q value = 0.37

Table S84. Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	8	368
subtype1	1	107
subtype2	1	105
subtype3	6	156

Figure S78. Get High-res Image Clustering Approach #6: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #7: 'MIRSEQ CNMF'

Table S85. Description of clustering approach #7: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	121	97	191

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0874 (logrank test), Q value = 0.15

Table S86. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	407	177	0.1 - 166.0 (17.2)
subtype1	121	62	1.9 - 166.0 (15.8)
subtype2	96	39	1.2 - 130.9 (16.6)
subtype3	190	76	0.1 - 165.7 (18.7)

Figure S79. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.112 (Kruskal-Wallis (anova)), Q value = 0.17

Table S87. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	408	68.1 (10.6)
subtype1	121	69.2 (9.7)
subtype2	96	68.8 (11.0)
subtype3	191	67.0 (10.9)

Figure S80. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00016

Table S88. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	139	135
subtype1	0	23	47	51
subtype2	1	24	31	40
subtype3	1	84	61	44

Figure S81. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 4e-05 (Fisher's exact test), Q value = 0.00027

Table S89. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	194	58
subtype1	21	79	15
subtype2	29	47	16
subtype3	74	68	27

Figure S82. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.00055 (Fisher's exact test), Q value = 0.0023

Table S90. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	236	46	76	9
subtype1	61	15	35	1
subtype2	49	14	21	4
subtype3	126	17	20	4

Figure S83. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.763 (Fisher's exact test), Q value = 0.82

Table S91. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	11
subtype1	44	3
subtype2	41	3
subtype3	110	5

Figure S84. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.1 (Fisher's exact test), Q value = 0.16

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

nPatients	FEMALE	MALE
ALL	107	302
subtype1	40	81
subtype2	20	77
subtype3	47	144

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.389 (Fisher's exact test), Q value = 0.47

Table S93. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	363	19
subtype1	105	4
subtype2	92	3
subtype3	166	12

Figure S86. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.194 (Kruskal-Wallis (anova)), Q value = 0.27

Table S94. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	135	83.0 (13.7)
subtype1	32	85.0 (12.4)
subtype2	27	84.4 (14.8)
subtype3	76	81.7 (13.9)

Figure S87. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CNMF' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.608 (Kruskal-Wallis (anova)), Q value = 0.68

Table S95. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	221	39.1 (53.2)
subtype1	67	49.1 (89.4)
subtype2	51	35.7 (27.4)
subtype3	103	34.4 (21.7)

Figure S88. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CNMF' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.000991 (Kruskal-Wallis (anova)), Q value = 0.0038

Table S96. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	295	2.1 (7.0)
subtype1	96	2.4 (5.2)
subtype2	77	3.4 (11.6)
subtype3	122	1.0 (3.4)

Figure S89. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.155 (Fisher's exact test), Q value = 0.23

Table S97. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	7	7	106
subtype2	10	5	78
subtype3	27	11	140

Figure S90. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #12: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.284 (Fisher's exact test), Q value = 0.38

Table S98. Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	368
subtype1	1	116
subtype2	4	85
subtype3	4	167

Figure S91. Get High-res Image Clustering Approach #7: 'MIRSEQ CNMF' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #8: 'MIRSEQ CHIERARCHICAL'

Table S99. Description of clustering approach #8: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	89	100	95	71	54

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.304 (logrank test), Q value = 0.4

Table S100. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	407	177	0.1 - 166.0 (17.2)
subtype1	89	48	1.1 - 166.0 (17.6)
subtype2	99	43	1.2 - 130.9 (17.2)
subtype3	94	34	0.1 - 104.7 (17.9)
subtype4	71	28	0.4 - 110.6 (18.0)
subtype5	54	24	2.1 - 163.3 (16.2)

Figure S92. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.000486 (Kruskal-Wallis (anova)), Q value = 0.0021

Table S101. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	408	68.1 (10.6)
subtype1	89	68.0 (11.2)
subtype2	99	70.2 (9.9)
subtype3	95	64.2 (10.7)
subtype4	71	68.5 (10.4)
subtype5	54	70.4 (9.5)

Figure S93. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.00026 (Fisher's exact test), Q value = 0.0013

Table S102. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	131	139	135
subtype1	0	22	39	28
subtype2	1	25	32	41
subtype3	1	48	22	23
subtype4	0	26	27	18
subtype5	0	10	19	25

Figure S94. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00031

Table S103. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	124	194	58
subtype1	18	51	12
subtype2	28	49	17
subtype3	49	25	12
subtype4	18	35	9
subtype5	11	34	8

Figure S95. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.0162 (Fisher's exact test), Q value = 0.04

Table S104. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	236	46	76	9
subtype1	49	15	11	2
subtype2	53	14	23	4
subtype3	59	6	14	1
subtype4	47	6	8	2
subtype5	28	5	20	0

Figure S96. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.0806 (Fisher's exact test), Q value = 0.14

Table S105. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	195	11
subtype1	39	0
subtype2	42	1
subtype3	60	3
subtype4	36	5
subtype5	18	2

Figure S97. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0345 (Fisher's exact test), Q value = 0.071

Table S106. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	302
subtype1	30	59
subtype2	21	79
subtype3	20	75
subtype4	15	56
subtype5	21	33

Figure S98. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0275 (Fisher's exact test), Q value = 0.062

Table S107. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	363	19
subtype1	79	4
subtype2	95	2
subtype3	78	10
subtype4	65	3
subtype5	46	0

Figure S99. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00998 (Kruskal-Wallis (anova)), Q value = 0.028

Table S108. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	135	83.0 (13.7)
subtype1	27	84.1 (14.7)
subtype2	28	86.8 (9.4)
subtype3	37	76.5 (17.2)
subtype4	27	84.8 (9.8)
subtype5	16	86.9 (10.8)

Figure S100. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.731 (Kruskal-Wallis (anova)), Q value = 0.79

Table S109. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	221	39.1 (53.2)
subtype1	46	52.6 (105.2)
subtype2	55	34.1 (23.3)
subtype3	53	34.4 (22.1)
subtype4	36	33.4 (24.2)
subtype5	31	43.0 (35.2)

Figure S101. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRSEQ CHIERARCHICAL' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0905 (Kruskal-Wallis (anova)), Q value = 0.15

Table S110. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	295	2.1 (7.0)
subtype1	65	1.1 (2.2)
subtype2	82	3.3 (11.3)
subtype3	52	1.9 (6.1)
subtype4	48	1.5 (5.1)
subtype5	48	2.2 (3.6)

Figure S102. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.00427 (Fisher's exact test), Q value = 0.014

Table S111. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	44	23	324
subtype1	4	4	76
subtype2	8	6	84
subtype3	22	5	64
subtype4	8	5	51
subtype5	2	3	49

Figure S103. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S112. Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	368
subtype1	2	80
subtype2	2	91
subtype3	2	85
subtype4	2	60
subtype5	1	52

Figure S104. Get High-res Image Clustering Approach #8: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #9: 'MIRseq Mature CNMF subtypes'

Table S113. Description of clustering approach #9: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	99	43	136	120

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

P value = 0.00478 (logrank test), Q value = 0.014

Table S114. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	396	175	0.4 - 166.0 (17.2)
subtype1	99	44	0.7 - 165.7 (16.7)
subtype2	42	27	1.5 - 130.9 (13.7)
subtype3	135	45	0.4 - 110.6 (18.1)
subtype4	120	59	1.1 - 166.0 (16.7)

Figure S105. 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 = 0.000586 (Kruskal-Wallis (anova)), Q value = 0.0023

Table S115. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	397	68.2 (10.6)
subtype1	99	67.9 (11.0)
subtype2	42	71.8 (8.2)
subtype3	136	65.4 (10.9)
subtype4	120	70.2 (9.7)

Figure S106. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S116. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	124	135	135
subtype1	0	32	36	31
subtype2	1	4	13	24
subtype3	1	65	41	28
subtype4	0	23	45	52

Figure S107. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 2e-05 (Fisher's exact test), Q value = 0.00016

Table S117. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	120	190	58
subtype1	27	52	11
subtype2	9	23	11
subtype3	60	41	19
subtype4	24	74	17

Figure S108. 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 = 1e-05 (Fisher's exact test), Q value = 1e-04

Table S118. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	229	46	76	9
subtype1	59	16	11	3
subtype2	16	7	14	3
subtype3	92	9	14	2
subtype4	62	14	37	1

Figure S109. 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 = 0.43 (Fisher's exact test), Q value = 0.51

Table S119. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	11
subtype1	38	2
subtype2	15	1
subtype3	86	3
subtype4	49	5

Figure S110. 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 = 0.313 (Fisher's exact test), Q value = 0.4

Table S120. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	291
subtype1	29	70
subtype2	10	33
subtype3	30	106
subtype4	38	82

Figure S111. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0199 (Fisher's exact test), Q value = 0.046

Table S121. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	354	19
subtype1	90	3
subtype2	43	0
subtype3	115	13
subtype4	106	3

Figure S112. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.00155 (Kruskal-Wallis (anova)), Q value = 0.0056

Table S122. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	130	83.1 (13.9)
subtype1	31	84.8 (13.1)
subtype2	13	86.9 (11.8)
subtype3	48	77.5 (16.3)
subtype4	38	87.4 (9.2)

Figure S113. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.803 (Kruskal-Wallis (anova)), Q value = 0.85

Table S123. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	216	39.6 (53.7)
subtype1	54	37.8 (22.8)
subtype2	29	37.6 (24.8)
subtype3	73	34.7 (24.0)
subtype4	60	48.1 (94.5)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature CNMF subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.000241 (Kruskal-Wallis (anova)), Q value = 0.0013

Table S124. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	291	2.1 (7.1)
subtype1	74	0.9 (1.8)
subtype2	38	3.4 (5.6)
subtype3	81	2.2 (11.3)
subtype4	98	2.5 (5.2)

Figure S115. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 5e-05 (Fisher's exact test), Q value = 0.00031

Table S125. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	41	22	317
subtype1	3	7	83
subtype2	1	3	38
subtype3	29	6	90
subtype4	8	6	106

Figure S116. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.875 (Fisher's exact test), Q value = 0.9

Table S126. Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	357
subtype1	3	87
subtype2	0	40
subtype3	3	117
subtype4	3	113

Figure S117. Get High-res Image Clustering Approach #9: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'ETHNICITY'

Clustering Approach #10: 'MIRseq Mature cHierClus subtypes'

Table S127. Description of clustering approach #10: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	75	47	95	66	31	84

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

P value = 0.0315 (logrank test), Q value = 0.067

Table S128. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	396	175	0.4 - 166.0 (17.2)
subtype1	75	34	1.9 - 163.3 (15.8)
subtype2	46	25	1.5 - 130.9 (15.6)
subtype3	95	30	0.4 - 110.6 (19.0)
subtype4	66	40	1.1 - 166.0 (18.0)
subtype5	30	6	0.7 - 88.9 (16.1)
subtype6	84	40	1.2 - 165.7 (16.5)

Figure S118. 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 = 0.000322 (Kruskal-Wallis (anova)), Q value = 0.0016

Table S129. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	397	68.2 (10.6)
subtype1	75	69.0 (9.7)
subtype2	46	71.3 (9.7)
subtype3	95	65.2 (10.5)
subtype4	66	71.2 (9.2)
subtype5	31	63.9 (11.1)
subtype6	84	68.2 (11.6)

Figure S119. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 3e-05 (Fisher's exact test), Q value = 0.00023

Table S130. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	2	124	135	135
subtype1	0	18	28	29
subtype2	1	9	14	22
subtype3	0	43	25	26
subtype4	0	12	27	27
subtype5	1	20	7	3
subtype6	0	22	34	28

Figure S120. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 7e-05 (Fisher's exact test), Q value = 0.00041

Table S131. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T0+T1+T2	T3	T4
ALL	120	190	58
subtype1	17	44	10
subtype2	12	24	10
subtype3	36	30	11
subtype4	13	40	11
subtype5	21	5	4
subtype6	21	47	12

Figure S121. 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 = 0.00175 (Fisher's exact test), Q value = 0.006

Table S132. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	N0	N1	N2	N3
ALL	229	46	76	9
subtype1	40	6	23	0
subtype2	21	7	13	3
subtype3	56	6	14	2
subtype4	37	11	15	1
subtype5	25	1	1	0
subtype6	50	15	10	3

Figure S122. 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 = 0.146 (Fisher's exact test), Q value = 0.22

Table S133. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	188	11
subtype1	31	2
subtype2	20	0
subtype3	52	6
subtype4	27	3
subtype5	26	0
subtype6	32	0

Figure S123. 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 = 0.509 (Fisher's exact test), Q value = 0.59

Table S134. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	107	291
subtype1	25	50
subtype2	12	35
subtype3	22	73
subtype4	19	47
subtype5	5	26
subtype6	24	60

Figure S124. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0204 (Fisher's exact test), Q value = 0.046

Table S135. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	354	19
subtype1	67	1
subtype2	44	1
subtype3	82	9
subtype4	60	3
subtype5	26	4
subtype6	75	1

Figure S125. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.000415 (Kruskal-Wallis (anova)), Q value = 0.0019

Table S136. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

	nPatients	Mean (Std.Dev)
ALL	130	83.1 (13.9)
subtype1	22	85.9 (10.1)
subtype2	13	86.2 (11.9)
subtype3	37	81.6 (15.4)
subtype4	19	88.9 (7.4)
subtype5	11	66.4 (16.3)
subtype6	28	83.9 (13.4)

Figure S126. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'KARNOFSKY_PERFORMANCE_SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.402 (Kruskal-Wallis (anova)), Q value = 0.48

Table S137. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

	nPatients	Mean (Std.Dev)
ALL	216	39.6 (53.7)
subtype1	40	39.4 (33.4)
subtype2	27	34.5 (20.6)
subtype3	51	34.3 (29.3)
subtype4	33	60.2 (122.0)
subtype5	17	39.9 (19.8)
subtype6	48	33.9 (21.3)

Figure S127. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NUMBER_PACK_YEARS_SMOKED'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER_OF_LYMPH_NODES'

P value = 0.0361 (Kruskal-Wallis (anova)), Q value = 0.073

Table S138. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

	nPatients	Mean (Std.Dev)
ALL	291	2.1 (7.1)
subtype1	57	1.9 (3.4)
subtype2	38	3.4 (5.6)
subtype3	59	2.5 (12.6)
subtype4	56	1.9 (4.8)
subtype5	14	2.4 (8.5)
subtype6	67	1.4 (4.1)

Figure S128. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBER_OF_LYMPH_NODES'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00013 (Fisher's exact test), Q value = 7e-04

Table S139. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	41	22	317
subtype1	5	3	67
subtype2	3	3	39
subtype3	13	6	69
subtype4	3	4	55
subtype5	13	2	14
subtype6	4	4	73

Figure S129. Get High-res Image Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.963 (Fisher's exact test), Q value = 0.97

Table S140. Clustering Approach #10: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	9	357
subtype1	1	72
subtype2	1	41
subtype3	3	80
subtype4	2	59
subtype5	0	28
subtype6	2	77

Figure S130. 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/BLCA-TP/20125359/BLCA-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/BLCA-TP/19775034/BLCA-TP.merged_data.txt
Number of patients = 412
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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