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 8 different clustering approaches and 12 clinical features across 377 patients, 35 significant findings detected with P value < 0.05 and Q value < 0.25.

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

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 12 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 35 significant findings detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.206 (0.388)	0.953 (0.957)	0.00485 (0.0245)	0.00337 (0.0202)	0.384 (0.58)	0.0555 (0.147)	0.101 (0.231)	0.00965 (0.0421)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.618 (0.732)	9.51e-09 (9.13e-07)	4.66e-06 (0.000149)	1.46e-05 (0.000234)	4.53e-06 (0.000149)	6.58e-06 (0.000158)	0.00393 (0.0209)	7.4e-05 (0.00096)
PATHOLOGIC STAGE	Fisher's exact test	0.166 (0.337)	0.741 (0.828)	0.00682 (0.0327)	0.00204 (0.0132)	0.0868 (0.214)	0.948 (0.957)	0.0298 (0.0924)	0.136 (0.294)
PATHOLOGY T STAGE	Fisher's exact test	0.0455 (0.125)	0.957 (0.957)	0.00057 (0.00547)	0.00021 (0.00224)	0.0368 (0.107)	0.879 (0.917)	0.223 (0.393)	0.181 (0.348)
PATHOLOGY N STAGE	Fisher's exact test	0.472 (0.648)	0.572 (0.713)	0.415 (0.612)	0.172 (0.337)	0.946 (0.957)	0.534 (0.693)	0.433 (0.63)	0.444 (0.631)
PATHOLOGY M STAGE	Fisher's exact test	0.83 (0.881)	0.285 (0.471)	0.496 (0.652)	0.491 (0.652)	0.31 (0.504)	0.353 (0.555)	0.225 (0.393)	0.387 (0.58)
GENDER	Fisher's exact test	0.668 (0.773)	0.0153 (0.0611)	1e-05 (0.000192)	8e-05 (0.00096)	0.00206 (0.0132)	0.0277 (0.0886)	0.0403 (0.114)	0.0219 (0.0778)
RADIATION THERAPY	Fisher's exact test	0.835 (0.881)	0.566 (0.713)	0.0739 (0.187)	0.222 (0.393)	0.594 (0.722)	0.138 (0.294)	0.668 (0.773)	0.0996 (0.231)
HISTOLOGICAL TYPE	Fisher's exact test	0.101 (0.231)	0.0183 (0.0677)	0.00364 (0.0206)	0.00076 (0.00663)	0.0567 (0.147)	0.689 (0.788)	0.0322 (0.0965)	0.331 (0.529)
RESIDUAL TUMOR	Fisher's exact test	0.373 (0.577)	0.543 (0.695)	0.0132 (0.0552)	0.0248 (0.0852)	0.487 (0.652)	0.698 (0.789)	0.153 (0.32)	0.215 (0.393)
RACE	Fisher's exact test	0.0162 (0.0621)	0.00124 (0.00916)	0.00093 (0.00744)	0.0263 (0.087)	0.129 (0.289)	0.265 (0.447)	0.462 (0.643)	0.00934 (0.0421)
ETHNICITY	Fisher's exact test	0.262 (0.447)	0.586 (0.721)	0.764 (0.834)	0.609 (0.731)	0.172 (0.337)	0.447 (0.631)	0.755 (0.833)	0.784 (0.846)

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

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

Cluster Labels	1	2	3
Number of samples	93	151	126

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

P value = 0.206 (logrank test), Q value = 0.39

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	364	129	0.0 - 120.8 (19.8)
subtype1	90	26	0.3 - 108.8 (19.9)
subtype2	150	59	0.0 - 120.8 (19.3)
subtype3	124	44	0.4 - 114.3 (20.5)

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 = 0.618 (Kruskal-Wallis (anova)), Q value = 0.73

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

	nPatients	Mean (Std.Dev)
ALL	366	59.4 (13.2)
subtype1	92	57.4 (15.3)
subtype2	149	60.3 (11.8)
subtype3	125	59.9 (13.0)

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 = 0.166 (Fisher's exact test), Q value = 0.34

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	173	87	3	62	9	7	2	1	2
subtype1	47	16	1	18	1	3	0	1	1
subtype2	60	42	1	26	5	4	0	0	1
subtype3	66	29	1	18	3	0	2	0	0

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 = 0.0455 (Fisher's exact test), Q value = 0.12

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

nPatients	T1	T2	T3	T4
ALL	183	95	77	12
subtype1	50	16	20	4
subtype2	63	49	32	7
subtype3	70	30	25	1

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 = 0.472 (Fisher's exact test), Q value = 0.65

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

nPatients	0	1
ALL	254	3
subtype1	63	1
subtype2	99	2
subtype3	92	0

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 = 0.83 (Fisher's exact test), Q value = 0.88

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

nPatients	0	1
ALL	266	4
subtype1	58	1
subtype2	112	1
subtype3	96	2

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 = 0.668 (Fisher's exact test), Q value = 0.77

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

nPatients	FEMALE	MALE
ALL	118	252
subtype1	29	64
subtype2	52	99
subtype3	37	89

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 = 0.835 (Fisher's exact test), Q value = 0.88

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

nPatients	NO	YES
ALL	338	9
subtype1	83	3
subtype2	139	3
subtype3	116	3

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 'HISTOLOGICAL_TYPE'

P value = 0.101 (Fisher's exact test), Q value = 0.23

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	360	7
subtype1	3	89	1
subtype2	0	148	3
subtype3	0	123	3

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

'Copy Number Ratio CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.373 (Fisher's exact test), Q value = 0.58

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

nPatients	R0	R1	R2	RX
ALL	324	17	1	21
subtype1	79	4	0	7
subtype2	131	7	0	11
subtype3	114	6	1	3

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

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.0162 (Fisher's exact test), Q value = 0.062

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

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	158	17	183
subtype1	1	27	6	58
subtype2	0	73	8	68
subtype3	1	58	3	57

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.262 (Fisher's exact test), Q value = 0.45

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	335
subtype1	2	85
subtype2	10	135
subtype3	4	115

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

Clustering Approach #2: 'METHLYATION CNMF'

Table S14. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	93	163	121

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.953 (logrank test), Q value = 0.96

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	371	132	0.0 - 120.8 (19.8)
subtype1	90	33	0.0 - 108.8 (20.8)
subtype2	162	57	0.2 - 120.8 (20.2)
subtype3	119	42	0.2 - 114.3 (18.3)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 9.51e-09 (Kruskal-Wallis (anova)), Q value = 9.1e-07

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	373	59.3 (13.4)
subtype1	91	57.9 (15.2)
subtype2	162	63.8 (9.9)
subtype3	120	54.2 (14.2)

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'METHLYATION CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.741 (Fisher's exact test), Q value = 0.83

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	175	87	3	65	9	9	2	1	2
subtype1	42	19	0	19	4	4	0	1	0
subtype2	79	38	2	26	3	3	0	0	1
subtype3	54	30	1	20	2	2	2	0	1

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.957 (Fisher's exact test), Q value = 0.96

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	185	95	81	13
subtype1	43	22	23	3
subtype2	84	41	31	6
subtype3	58	32	27	4

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.572 (Fisher's exact test), Q value = 0.71

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	257	4
subtype1	65	2
subtype2	107	1
subtype3	85	1

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.285 (Fisher's exact test), Q value = 0.47

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	272	4
subtype1	64	0
subtype2	117	1
subtype3	91	3

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.0153 (Fisher's exact test), Q value = 0.061

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	122	255
subtype1	34	59
subtype2	40	123
subtype3	48	73

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.566 (Fisher's exact test), Q value = 0.71

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	345	9
subtype1	85	1
subtype2	152	4
subtype3	108	4

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'METHLYATION CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0183 (Fisher's exact test), Q value = 0.068

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	367	7
subtype1	3	87	3
subtype2	0	162	1
subtype3	0	118	3

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'METHLYATION CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.543 (Fisher's exact test), Q value = 0.7

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	330	17	1	22
subtype1	76	5	0	8
subtype2	145	8	0	7
subtype3	109	4	1	7

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'METHLYATION CNMF' versus 'RACE'

P value = 0.00124 (Fisher's exact test), Q value = 0.0092

Table S25. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	161	17	187
subtype1	0	29	9	52
subtype2	1	66	7	85
subtype3	1	66	1	50

Figure S23. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.586 (Fisher's exact test), Q value = 0.72

Table S26. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	18	340
subtype1	4	84
subtype2	6	146
subtype3	8	110

Figure S24. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S27. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	68	88	74	71	70

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00485 (logrank test), Q value = 0.025

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	365	130	0.0 - 120.8 (19.6)
subtype1	65	32	0.3 - 120.8 (13.6)
subtype2	88	29	0.3 - 102.7 (22.9)
subtype3	72	26	0.2 - 114.3 (19.4)
subtype4	70	22	0.0 - 107.1 (20.9)
subtype5	70	21	0.4 - 108.8 (19.0)

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 4.66e-06 (Kruskal-Wallis (anova)), Q value = 0.00015

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	367	59.3 (13.4)
subtype1	66	60.7 (13.3)
subtype2	87	62.7 (12.4)
subtype3	73	52.5 (13.8)
subtype4	71	58.8 (12.9)
subtype5	70	61.2 (12.8)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00682 (Fisher's exact test), Q value = 0.033

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	171	86	3	65	8	9	2	1	2
subtype1	17	25	0	14	3	3	0	0	1
subtype2	55	13	1	12	1	0	0	0	0
subtype3	33	17	1	14	1	2	1	0	1
subtype4	35	13	0	9	2	3	1	1	0
subtype5	31	18	1	16	1	1	0	0	0

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

'RNAseq CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00057 (Fisher's exact test), Q value = 0.0055

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	181	94	80	13
subtype1	17	29	17	5
subtype2	57	14	14	2
subtype3	37	18	18	1
subtype4	38	15	13	4
subtype5	32	18	18	1

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.415 (Fisher's exact test), Q value = 0.61

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

nPatients	0	1
ALL	252	4
subtype1	40	1
subtype2	61	0
subtype3	59	2
subtype4	41	1
subtype5	51	0

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.496 (Fisher's exact test), Q value = 0.65

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	266	4
subtype1	46	1
subtype2	64	0
subtype3	61	2
subtype4	43	1
subtype5	52	0

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 1e-05 (Fisher's exact test), Q value = 0.00019

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

nPatients	FEMALE	MALE
ALL	121	250
subtype1	20	48
subtype2	23	65
subtype3	38	36
subtype4	30	41
subtype5	10	60

Figure S31. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.0739 (Fisher's exact test), Q value = 0.19

Table S35. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	339	9
subtype1	64	1
subtype2	81	1
subtype3	69	0
subtype4	63	2
subtype5	62	5

Figure S32. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00364 (Fisher's exact test), Q value = 0.021

Table S36. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	361	7
subtype1	0	66	2
subtype2	0	88	0
subtype3	0	73	1
subtype4	3	64	4
subtype5	0	70	0

Figure S33. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0132 (Fisher's exact test), Q value = 0.055

Table S37. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	324	17	1	22
subtype1	54	6	0	7
subtype2	78	3	0	6
subtype3	69	1	0	4
subtype4	56	6	1	5
subtype5	67	1	0	0

Figure S34. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.00093 (Fisher's exact test), Q value = 0.0074

Table S38. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	158	17	184
subtype1	1	25	8	32
subtype2	0	32	3	48
subtype3	0	48	1	24
subtype4	0	24	2	43
subtype5	1	29	3	37

Figure S35. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.764 (Fisher's exact test), Q value = 0.83

Table S39. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	18	334
subtype1	4	58
subtype2	6	76
subtype3	3	70
subtype4	3	64
subtype5	2	66

Figure S36. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S40. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	55	56	116	87	57

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00337 (logrank test), Q value = 0.02

Table S41. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	365	130	0.0 - 120.8 (19.6)
subtype1	53	29	0.2 - 107.1 (13.1)
subtype2	56	17	0.0 - 90.3 (19.2)
subtype3	116	42	0.3 - 120.8 (21.2)
subtype4	83	27	0.2 - 114.3 (19.6)
subtype5	57	15	0.4 - 108.8 (18.7)

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 1.46e-05 (Kruskal-Wallis (anova)), Q value = 0.00023

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	367	59.3 (13.4)
subtype1	53	61.3 (11.3)
subtype2	55	61.9 (9.6)
subtype3	115	61.9 (11.7)
subtype4	87	51.7 (16.0)
subtype5	57	60.9 (13.6)

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.00204 (Fisher's exact test), Q value = 0.013

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	171	86	3	65	8	9	2	1	2
subtype1	11	17	0	15	4	3	0	0	0
subtype2	33	10	0	3	1	2	0	0	0
subtype3	62	25	1	17	2	1	0	0	1
subtype4	41	19	1	17	0	2	2	1	1
subtype5	24	15	1	13	1	1	0	0	0

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.00021 (Fisher's exact test), Q value = 0.0022

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	181	94	80	13
subtype1	11	20	20	4
subtype2	37	11	5	2
subtype3	63	28	19	5
subtype4	45	20	21	1
subtype5	25	15	15	1

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.172 (Fisher's exact test), Q value = 0.34

Table S45. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	252	4
subtype1	33	1
subtype2	31	0
subtype3	77	0
subtype4	67	3
subtype5	44	0

Figure S41. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.491 (Fisher's exact test), Q value = 0.65

Table S46. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	266	4
subtype1	39	0
subtype2	32	0
subtype3	83	1
subtype4	68	3
subtype5	44	0

Figure S42. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 8e-05 (Fisher's exact test), Q value = 0.00096

Table S47. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	121	250
subtype1	23	32
subtype2	13	43
subtype3	35	81
subtype4	42	45
subtype5	8	49

Figure S43. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.222 (Fisher's exact test), Q value = 0.39

Table S48. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	339	9
subtype1	52	1
subtype2	52	0
subtype3	107	2
subtype4	77	2
subtype5	51	4

Figure S44. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.00076 (Fisher's exact test), Q value = 0.0066

Table S49. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	361	7
subtype1	0	50	5
subtype2	0	56	0
subtype3	0	114	2
subtype4	3	84	0
subtype5	0	57	0

Figure S45. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'RNAseq cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.0248 (Fisher's exact test), Q value = 0.085

Table S50. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	324	17	1	22
subtype1	43	7	0	4
subtype2	46	4	0	4
subtype3	102	4	0	8
subtype4	79	1	1	6
subtype5	54	1	0	0

Figure S46. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.0263 (Fisher's exact test), Q value = 0.087

Table S51. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	158	17	184
subtype1	0	26	1	27
subtype2	0	14	4	35
subtype3	0	45	6	59
subtype4	1	50	2	34
subtype5	1	23	4	29

Figure S47. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.609 (Fisher's exact test), Q value = 0.73

Table S52. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	18	334
subtype1	3	51
subtype2	4	51
subtype3	7	98
subtype4	3	80
subtype5	1	54

Figure S48. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S53. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5	6
Number of samples	40	64	92	85	50	41

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.384 (logrank test), Q value = 0.58

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	366	128	0.0 - 120.8 (19.8)
subtype1	38	17	0.2 - 120.8 (14.6)
subtype2	64	23	0.3 - 113.0 (21.9)
subtype3	92	26	0.0 - 114.3 (21.3)
subtype4	84	27	0.3 - 108.8 (18.1)
subtype5	49	19	0.4 - 83.2 (24.5)
subtype6	39	16	0.2 - 89.7 (18.2)

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.53e-06 (Kruskal-Wallis (anova)), Q value = 0.00015

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	368	59.2 (13.4)
subtype1	38	61.5 (11.3)
subtype2	63	64.8 (10.6)
subtype3	92	57.0 (13.1)
subtype4	85	61.9 (11.9)
subtype5	50	54.0 (16.1)
subtype6	40	54.1 (14.5)

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'PATHOLOGIC_STAGE'

P value = 0.0868 (Fisher's exact test), Q value = 0.21

Table S56. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	172	86	3	64	9	9	2	1	2
subtype1	19	6	0	10	2	1	0	0	1
subtype2	26	20	1	8	3	1	0	0	0
subtype3	51	17	0	9	0	2	1	1	1
subtype4	44	19	0	14	3	2	0	0	0
subtype5	18	14	2	11	0	2	1	0	0
subtype6	14	10	0	12	1	1	0	0	0

Figure S51. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_T_STAGE'

P value = 0.0368 (Fisher's exact test), Q value = 0.11

Table S57. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	182	94	80	13
subtype1	19	7	12	2
subtype2	27	23	11	3
subtype3	57	17	12	4
subtype4	45	19	17	3
subtype5	20	15	14	1
subtype6	14	13	14	0

Figure S52. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_N_STAGE'

P value = 0.946 (Fisher's exact test), Q value = 0.96

Table S58. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	254	4
subtype1	28	0
subtype2	40	0
subtype3	55	1
subtype4	62	1
subtype5	38	1
subtype6	31	1

Figure S53. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY_M_STAGE'

P value = 0.31 (Fisher's exact test), Q value = 0.5

Table S59. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	269	4
subtype1	24	1
subtype2	45	0
subtype3	60	2
subtype4	66	0
subtype5	40	1
subtype6	34	0

Figure S54. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.00206 (Fisher's exact test), Q value = 0.013

Table S60. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	119	253
subtype1	14	26
subtype2	18	46
subtype3	29	63
subtype4	16	69
subtype5	19	31
subtype6	23	18

Figure S55. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.594 (Fisher's exact test), Q value = 0.72

Table S61. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	340	9
subtype1	39	0
subtype2	61	1
subtype3	83	2
subtype4	75	4
subtype5	46	2
subtype6	36	0

Figure S56. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.0567 (Fisher's exact test), Q value = 0.15

Table S62. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	362	7
subtype1	0	40	0
subtype2	0	63	1
subtype3	3	88	1
subtype4	0	85	0
subtype5	0	47	3
subtype6	0	39	2

Figure S57. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.487 (Fisher's exact test), Q value = 0.65

Table S63. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	325	17	1	22
subtype1	32	2	0	3
subtype2	53	3	0	6
subtype3	78	5	1	7
subtype4	79	3	0	2
subtype5	45	1	0	4
subtype6	38	3	0	0

Figure S58. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.129 (Fisher's exact test), Q value = 0.29

Table S64. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	161	17	182
subtype1	0	16	5	19
subtype2	0	20	4	36
subtype3	0	35	5	47
subtype4	1	42	2	40
subtype5	0	27	1	22
subtype6	1	21	0	18

Figure S59. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.172 (Fisher's exact test), Q value = 0.34

Table S65. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	18	335
subtype1	2	36
subtype2	4	55
subtype3	3	82
subtype4	1	82
subtype5	5	44
subtype6	3	36

Figure S60. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S66. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4	5
Number of samples	44	97	57	126	48

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0555 (logrank test), Q value = 0.15

Table S67. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	366	128	0.0 - 120.8 (19.8)
subtype1	42	18	0.0 - 120.8 (16.9)
subtype2	97	34	0.3 - 102.7 (21.8)
subtype3	55	26	0.3 - 113.0 (16.0)
subtype4	124	40	0.2 - 114.3 (21.5)
subtype5	48	10	0.3 - 108.8 (17.9)

Figure S61. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 6.58e-06 (Kruskal-Wallis (anova)), Q value = 0.00016

Table S68. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	368	59.2 (13.4)
subtype1	43	61.6 (11.0)
subtype2	95	62.8 (11.7)
subtype3	57	59.0 (13.6)
subtype4	125	54.1 (14.8)
subtype5	48	63.6 (9.9)

Figure S62. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.948 (Fisher's exact test), Q value = 0.96

Table S69. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	172	86	3	64	9	9	2	1	2
subtype1	21	7	0	11	2	1	0	0	1
subtype2	45	23	1	11	4	1	0	0	0
subtype3	26	14	0	12	1	1	0	0	0
subtype4	57	30	2	21	1	5	2	1	1
subtype5	23	12	0	9	1	1	0	0	0

Figure S63. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.879 (Fisher's exact test), Q value = 0.92

Table S70. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	182	94	80	13
subtype1	21	8	13	2
subtype2	49	26	17	5
subtype3	28	15	14	0
subtype4	60	33	26	5
subtype5	24	12	10	1

Figure S64. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.534 (Fisher's exact test), Q value = 0.69

Table S71. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	254	4
subtype1	31	0
subtype2	59	0
subtype3	39	1
subtype4	88	3
subtype5	37	0

Figure S65. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY_M_STAGE'

P value = 0.353 (Fisher's exact test), Q value = 0.55

Table S72. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	269	4
subtype1	28	1
subtype2	65	0
subtype3	43	0
subtype4	94	3
subtype5	39	0

Figure S66. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.0277 (Fisher's exact test), Q value = 0.089

Table S73. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	119	253
subtype1	14	30
subtype2	29	68
subtype3	19	38
subtype4	50	76
subtype5	7	41

Figure S67. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.138 (Fisher's exact test), Q value = 0.29

Table S74. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	340	9
subtype1	41	1
subtype2	93	0
subtype3	50	1
subtype4	113	4
subtype5	43	3

Figure S68. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.689 (Fisher's exact test), Q value = 0.79

Table S75. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	3	362	7
subtype1	0	44	0
subtype2	0	95	2
subtype3	0	56	1
subtype4	3	119	4
subtype5	0	48	0

Figure S69. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RESIDUAL_TUMOR'

P value = 0.698 (Fisher's exact test), Q value = 0.79

Table S76. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	325	17	1	22
subtype1	35	3	0	3
subtype2	83	5	0	6
subtype3	50	2	0	5
subtype4	111	6	1	8
subtype5	46	1	0	0

Figure S70. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.265 (Fisher's exact test), Q value = 0.45

Table S77. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	161	17	182
subtype1	0	18	5	21
subtype2	0	34	5	52
subtype3	0	32	2	22
subtype4	1	56	4	62
subtype5	1	21	1	25

Figure S71. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.447 (Fisher's exact test), Q value = 0.63

Table S78. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	18	335
subtype1	3	40
subtype2	4	86
subtype3	3	52
subtype4	8	112
subtype5	0	45

Figure S72. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S79. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	110	81	104	51

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

P value = 0.101 (logrank test), Q value = 0.23

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	340	120	0.0 - 120.8 (19.8)
subtype1	107	46	0.3 - 120.8 (17.6)
subtype2	80	28	0.2 - 107.1 (21.4)
subtype3	104	31	0.3 - 108.8 (21.2)
subtype4	49	15	0.0 - 90.3 (17.1)

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

'MIRseq Mature CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.00393 (Kruskal-Wallis (anova)), Q value = 0.021

Table S81. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	342	59.2 (13.1)
subtype1	108	58.6 (14.0)
subtype2	80	58.1 (12.4)
subtype3	104	63.1 (9.8)
subtype4	50	54.3 (16.2)

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.0298 (Fisher's exact test), Q value = 0.092

Table S82. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	157	83	3	59	7	8	2	1	2
subtype1	47	26	0	26	5	5	0	0	0
subtype2	38	17	0	10	0	3	1	1	1
subtype3	51	26	1	14	0	0	0	0	1
subtype4	21	14	2	9	2	0	1	0	0

Figure S75. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.223 (Fisher's exact test), Q value = 0.39

Table S83. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	167	91	73	12
subtype1	48	27	30	5
subtype2	42	20	13	5
subtype3	55	29	16	2
subtype4	22	15	14	0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.433 (Fisher's exact test), Q value = 0.63

Table S84. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	238	4
subtype1	81	3
subtype2	48	1
subtype3	70	0
subtype4	39	0

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

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.225 (Fisher's exact test), Q value = 0.39

Table S85. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	255	4
subtype1	88	0
subtype2	49	2
subtype3	75	1
subtype4	43	1

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0403 (Fisher's exact test), Q value = 0.11

Table S86. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	115	231
subtype1	45	65
subtype2	27	54
subtype3	24	80
subtype4	19	32

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

'MIRseq Mature CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.668 (Fisher's exact test), Q value = 0.77

Table S87. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	315	9
subtype1	101	3
subtype2	73	2
subtype3	94	4
subtype4	47	0

Figure S80. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0322 (Fisher's exact test), Q value = 0.097

Table S88. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	337	7
subtype1	0	108	2
subtype2	2	77	2
subtype3	0	104	0
subtype4	0	48	3

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

'MIRseq Mature CNMF subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.153 (Fisher's exact test), Q value = 0.32

Table S89. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	303	17	1	19
subtype1	96	5	0	6
subtype2	65	8	1	5
subtype3	97	3	0	3
subtype4	45	1	0	5

Figure S82. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.462 (Fisher's exact test), Q value = 0.64

Table S90. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	155	16	163
subtype1	1	56	3	48
subtype2	0	31	4	42
subtype3	1	40	7	52
subtype4	0	28	2	21

Figure S83. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature CNMF subtypes' versus 'ETHNICITY'

P value = 0.755 (Fisher's exact test), Q value = 0.83

Table S91. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	312
subtype1	5	98
subtype2	5	72
subtype3	3	94
subtype4	3	48

Figure S84. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'ETHNICITY'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S92. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	73	84	101	34	54

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

P value = 0.00965 (logrank test), Q value = 0.042

Table S93. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	340	120	0.0 - 120.8 (19.8)
subtype1	69	34	0.3 - 107.1 (16.0)
subtype2	84	30	0.3 - 113.0 (21.8)
subtype3	100	32	0.2 - 114.3 (21.4)
subtype4	33	14	0.0 - 120.8 (15.5)
subtype5	54	10	0.3 - 108.8 (19.4)

Figure S85. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 7.4e-05 (Kruskal-Wallis (anova)), Q value = 0.00096

Table S94. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	342	59.2 (13.1)
subtype1	72	54.8 (13.1)
subtype2	82	63.2 (11.1)
subtype3	100	56.4 (15.0)
subtype4	34	62.6 (11.1)
subtype5	54	62.4 (10.4)

Figure S86. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.136 (Fisher's exact test), Q value = 0.29

Table S95. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	157	83	3	59	7	8	2	1	2
subtype1	27	17	0	20	2	2	0	0	1
subtype2	39	20	1	11	4	1	0	0	0
subtype3	44	25	2	13	0	4	2	1	0
subtype4	19	4	0	8	1	1	0	0	1
subtype5	28	17	0	7	0	0	0	0	0

Figure S87. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'PATHOLOGIC_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.181 (Fisher's exact test), Q value = 0.35

Table S96. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

nPatients	T1	T2	T3	T4
ALL	167	91	73	12
subtype1	28	19	23	3
subtype2	42	23	15	4
subtype3	49	28	19	3
subtype4	19	4	9	2
subtype5	29	17	7	0

Figure S88. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY_T_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.444 (Fisher's exact test), Q value = 0.63

Table S97. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

nPatients	0	1
ALL	238	4
subtype1	50	1
subtype2	51	0
subtype3	68	3
subtype4	27	0
subtype5	42	0

Figure S89. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY_N_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY_M_STAGE'

P value = 0.387 (Fisher's exact test), Q value = 0.58

Table S98. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

nPatients	0	1
ALL	255	4
subtype1	54	1
subtype2	59	0
subtype3	73	2
subtype4	23	1
subtype5	46	0

Figure S90. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_M_STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0219 (Fisher's exact test), Q value = 0.078

Table S99. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	115	231
subtype1	24	49
subtype2	24	60
subtype3	44	57
subtype4	13	21
subtype5	10	44

Figure S91. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.0996 (Fisher's exact test), Q value = 0.23

Table S100. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

nPatients	NO	YES
ALL	315	9
subtype1	63	3
subtype2	79	1
subtype3	94	1
subtype4	32	0
subtype5	47	4

Figure S92. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'RADIATION_THERAPY'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.331 (Fisher's exact test), Q value = 0.53

Table S101. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	337	7
subtype1	0	72	1
subtype2	0	83	1
subtype3	2	94	5
subtype4	0	34	0
subtype5	0	54	0

Figure S93. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL_TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RESIDUAL_TUMOR'

P value = 0.215 (Fisher's exact test), Q value = 0.39

Table S102. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

nPatients	R0	R1	R2	RX
ALL	303	17	1	19
subtype1	66	4	0	3
subtype2	73	2	0	7
subtype3	85	7	1	8
subtype4	27	3	0	1
subtype5	52	1	0	0

Figure S94. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RESIDUAL_TUMOR'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.00934 (Fisher's exact test), Q value = 0.042

Table S103. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients	AMERICAN INDIAN OR ALASKA NATIVE	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	155	16	163
subtype1	1	45	0	27
subtype2	0	27	7	44
subtype3	0	43	5	50
subtype4	0	13	3	18
subtype5	1	27	1	24

Figure S95. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'MIRseq Mature cHierClus subtypes' versus 'ETHNICITY'

P value = 0.784 (Fisher's exact test), Q value = 0.85

Table S104. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	16	312
subtype1	5	66
subtype2	4	73
subtype3	3	93
subtype4	2	31
subtype5	2	49

Figure S96. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LIHC-TP/22542932/LIHC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LIHC-TP/22506983/LIHC-TP.merged_data.txt
Number of patients = 377
Number of clustering approaches = 8
Number of selected clinical features = 12
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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