Correlation between aggregated molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

21 August 2015 | analyses__2015_08_21

Maintainer Information

Citation Information

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

Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C16W99BH

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 368 patients, 34 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 'Time to Death', 'GENDER', and 'RACE'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH', 'GENDER', 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', 'RADIATION_THERAPY', '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'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'YEARS_TO_BIRTH'.
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', 'PATHOLOGY_T_STAGE', and 'GENDER'.
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, 34 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	0.0307 (0.0951)	0.523 (0.707)	0.00809 (0.0393)	0.00423 (0.029)	0.473 (0.668)	0.056 (0.145)	0.119 (0.278)	0.035 (0.103)
YEARS TO BIRTH	Kruskal-Wallis (anova)	0.227 (0.42)	1.01e-07 (9.69e-06)	2.16e-06 (0.000104)	5.63e-05 (0.000823)	0.00217 (0.017)	3.28e-05 (0.000629)	0.00566 (0.0331)	1.91e-05 (0.00048)
PATHOLOGIC STAGE	Fisher's exact test	0.257 (0.448)	0.878 (0.937)	0.0356 (0.103)	0.0023 (0.017)	0.148 (0.321)	0.946 (0.987)	0.00032 (0.00341)	0.0744 (0.188)
PATHOLOGY T STAGE	Fisher's exact test	0.179 (0.357)	0.837 (0.93)	0.00468 (0.03)	0.00029 (0.00341)	0.852 (0.93)	0.863 (0.931)	0.00901 (0.0412)	0.0953 (0.235)
PATHOLOGY N STAGE	Fisher's exact test	1 (1.00)	1 (1.00)	0.601 (0.779)	0.305 (0.476)	0.677 (0.812)	1 (1.00)	0.159 (0.325)	0.944 (0.987)
PATHOLOGY M STAGE	Fisher's exact test	1 (1.00)	0.273 (0.453)	0.635 (0.784)	0.473 (0.668)	0.236 (0.427)	0.308 (0.476)	0.289 (0.462)	0.15 (0.321)
GENDER	Fisher's exact test	0.0269 (0.0891)	0.0259 (0.0888)	2e-05 (0.00048)	6e-05 (0.000823)	0.228 (0.42)	0.0162 (0.0623)	0.0144 (0.0601)	0.0395 (0.112)
RADIATION THERAPY	Fisher's exact test	0.716 (0.839)	0.483 (0.672)	0.0299 (0.0951)	0.281 (0.457)	0.637 (0.784)	0.0521 (0.139)	0.757 (0.876)	0.0521 (0.139)
HISTOLOGICAL TYPE	Fisher's exact test	0.669 (0.812)	0.156 (0.325)	0.00819 (0.0393)	0.00187 (0.0163)	0.102 (0.245)	0.851 (0.93)	0.123 (0.28)	0.628 (0.784)
RESIDUAL TUMOR	Fisher's exact test	0.261 (0.448)	0.517 (0.707)	0.00714 (0.0381)	0.0222 (0.082)	0.835 (0.93)	0.356 (0.543)	0.182 (0.357)	0.194 (0.372)
RACE	Fisher's exact test	0.0249 (0.0886)	0.00136 (0.0131)	0.00586 (0.0331)	0.0154 (0.0615)	0.42 (0.621)	0.138 (0.309)	0.817 (0.93)	0.0112 (0.0487)
ETHNICITY	Fisher's exact test	0.266 (0.448)	0.442 (0.642)	0.699 (0.829)	0.256 (0.448)	0.4 (0.6)	0.63 (0.784)	0.54 (0.717)	0.545 (0.717)

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	106	122	134

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

P value = 0.0307 (logrank test), Q value = 0.095

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	358	129	0.0 - 120.8 (19.8)
subtype1	106	35	0.3 - 114.3 (18.9)
subtype2	120	53	0.0 - 120.8 (18.4)
subtype3	132	41	0.2 - 108.8 (21.1)

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

'Copy Number Ratio CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.227 (Kruskal-Wallis (anova)), Q value = 0.42

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

	nPatients	Mean (Std.Dev)
ALL	359	59.7 (12.7)
subtype1	105	61.4 (11.2)
subtype2	120	58.3 (13.0)
subtype3	134	59.6 (13.6)

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 = 0.257 (Fisher's exact test), Q value = 0.45

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	171	85	3	60	8	7	3	1	2
subtype1	58	25	0	14	1	0	1	1	0
subtype2	49	34	1	22	3	2	2	0	1
subtype3	64	26	2	24	4	5	0	0	1

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 = 0.179 (Fisher's exact test), Q value = 0.36

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

nPatients	T0+T1	T2	T3	T4
ALL	182	92	74	12
subtype1	61	26	17	1
subtype2	52	37	27	6
subtype3	69	29	30	5

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

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

nPatients	0	1
ALL	250	3
subtype1	73	1
subtype2	87	1
subtype3	90	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 = 1 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	261	4
subtype1	77	1
subtype2	92	2
subtype3	92	1

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 = 0.0269 (Fisher's exact test), Q value = 0.089

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

nPatients	FEMALE	MALE
ALL	115	247
subtype1	23	83
subtype2	44	78
subtype3	48	86

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 = 0.716 (Fisher's exact test), Q value = 0.84

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

nPatients	NO	YES
ALL	336	7
subtype1	95	3
subtype2	114	2
subtype3	127	2

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 = 0.669 (Fisher's exact test), Q value = 0.81

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	353	7
subtype1	0	104	2
subtype2	0	119	3
subtype3	2	130	2

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 = 0.261 (Fisher's exact test), Q value = 0.45

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

nPatients	R0	R1	R2	RX
ALL	318	15	1	21
subtype1	95	6	1	3
subtype2	105	6	0	10
subtype3	118	3	0	8

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 = 0.0249 (Fisher's exact test), Q value = 0.089

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	1	157	17	177
subtype1	1	49	5	46
subtype2	0	64	4	53
subtype3	0	44	8	78

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 = 0.266 (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	13	332
subtype1	5	96
subtype2	6	113
subtype3	2	123

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	83	167	118

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.523 (logrank test), Q value = 0.71

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	364	132	0.0 - 120.8 (19.8)
subtype1	81	27	0.2 - 108.8 (20.8)
subtype2	166	61	0.0 - 120.8 (19.6)
subtype3	117	44	0.3 - 114.3 (18.5)

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 = 1.01e-07 (Kruskal-Wallis (anova)), Q value = 9.7e-06

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

	nPatients	Mean (Std.Dev)
ALL	365	59.6 (13.0)
subtype1	83	58.3 (13.9)
subtype2	165	63.7 (10.0)
subtype3	117	54.7 (14.3)

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 = 0.878 (Fisher's exact test), Q value = 0.94

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	172	85	3	63	8	9	3	1	2
subtype1	39	16	0	17	3	4	0	0	0
subtype2	80	40	2	25	4	3	1	1	1
subtype3	53	29	1	21	1	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 = 0.837 (Fisher's exact test), Q value = 0.93

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

nPatients	T0+T1	T2	T3	T4
ALL	183	92	78	13
subtype1	41	18	20	3
subtype2	86	43	30	7
subtype3	56	31	28	3

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

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

nPatients	0	1
ALL	253	4
subtype1	56	1
subtype2	112	2
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 = 0.273 (Fisher's exact test), Q value = 0.45

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

nPatients	0	1
ALL	267	4
subtype1	58	0
subtype2	120	1
subtype3	89	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 = 0.0259 (Fisher's exact test), Q value = 0.089

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

nPatients	FEMALE	MALE
ALL	118	250
subtype1	29	54
subtype2	42	125
subtype3	47	71

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

'METHLYATION CNMF' versus 'RADIATION_THERAPY'

P value = 0.483 (Fisher's exact test), Q value = 0.67

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

nPatients	NO	YES
ALL	342	7
subtype1	77	1
subtype2	158	2
subtype3	107	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 = 0.156 (Fisher's exact test), Q value = 0.33

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	359	7
subtype1	2	79	2
subtype2	0	165	2
subtype3	0	115	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 = 0.517 (Fisher's exact test), Q value = 0.71

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

nPatients	R0	R1	R2	RX
ALL	323	15	1	22
subtype1	68	3	0	8
subtype2	150	6	0	8
subtype3	105	6	1	6

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.00136 (Fisher's exact test), Q value = 0.013

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	1	160	17	180
subtype1	0	26	8	46
subtype2	1	68	8	86
subtype3	0	66	1	48

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

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.442 (Fisher's exact test), Q value = 0.64

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	337
subtype1	2	77
subtype2	5	151
subtype3	7	109

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	59	97	70	68	68

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00809 (logrank test), Q value = 0.039

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	358	130	0.0 - 120.8 (19.4)
subtype1	58	30	0.2 - 120.8 (13.7)
subtype2	97	32	0.3 - 102.7 (22.1)
subtype3	68	26	1.0 - 114.3 (18.2)
subtype4	67	21	0.0 - 107.1 (19.8)
subtype5	68	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 = 2.16e-06 (Kruskal-Wallis (anova)), Q value = 1e-04

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

	nPatients	Mean (Std.Dev)
ALL	359	59.6 (13.0)
subtype1	58	61.6 (11.5)
subtype2	96	62.8 (12.5)
subtype3	69	52.4 (13.8)
subtype4	68	58.6 (12.8)
subtype5	68	61.6 (11.7)

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 = 0.0356 (Fisher's exact test), Q value = 0.1

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	168	84	3	63	7	9	3	1	2
subtype1	15	21	0	12	3	3	1	0	0
subtype2	56	16	1	12	2	0	0	1	1
subtype3	31	17	1	14	1	2	1	0	1
subtype4	35	13	0	10	0	3	1	0	0
subtype5	31	17	1	15	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 = 0.00468 (Fisher's exact test), Q value = 0.03

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

nPatients	T0+T1	T2	T3	T4
ALL	179	91	77	13
subtype1	15	25	15	4
subtype2	60	17	15	4
subtype3	34	17	18	1
subtype4	38	15	12	3
subtype5	32	17	17	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 = 0.601 (Fisher's exact test), Q value = 0.78

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

nPatients	0	1
ALL	248	4
subtype1	35	1
subtype2	64	1
subtype3	58	2
subtype4	40	0
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 = 0.635 (Fisher's exact test), Q value = 0.78

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

nPatients	0	1
ALL	261	4
subtype1	42	0
subtype2	67	1
subtype3	59	2
subtype4	40	1
subtype5	53	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 = 2e-05 (Fisher's exact test), Q value = 0.00048

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

nPatients	FEMALE	MALE
ALL	117	245
subtype1	17	42
subtype2	26	71
subtype3	37	33
subtype4	28	40
subtype5	9	59

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 = 0.0299 (Fisher's exact test), Q value = 0.095

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

nPatients	NO	YES
ALL	336	7
subtype1	57	1
subtype2	91	0
subtype3	65	0
subtype4	61	2
subtype5	62	4

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 = 0.00819 (Fisher's exact test), Q value = 0.039

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	353	7
subtype1	0	57	2
subtype2	0	97	0
subtype3	0	69	1
subtype4	2	62	4
subtype5	0	68	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 = 0.00714 (Fisher's exact test), Q value = 0.038

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

nPatients	R0	R1	R2	RX
ALL	317	15	1	22
subtype1	48	5	0	5
subtype2	84	3	0	8
subtype3	65	1	0	4
subtype4	54	6	1	5
subtype5	66	0	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 = 0.00586 (Fisher's exact test), Q value = 0.033

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	1	157	17	177
subtype1	0	24	6	27
subtype2	0	35	5	52
subtype3	0	45	1	23
subtype4	0	23	2	41
subtype5	1	30	3	34

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

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.699 (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	14	331
subtype1	3	51
subtype2	5	86
subtype3	2	67
subtype4	3	62
subtype5	1	65

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	53	56	114	84	55

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.00423 (logrank test), Q value = 0.029

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	358	130	0.0 - 120.8 (19.4)
subtype1	52	29	0.2 - 107.1 (13.3)
subtype2	56	17	0.0 - 90.3 (19.2)
subtype3	114	42	0.3 - 120.8 (21.0)
subtype4	81	27	0.2 - 114.3 (19.6)
subtype5	55	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 = 5.63e-05 (Kruskal-Wallis (anova)), Q value = 0.00082

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

	nPatients	Mean (Std.Dev)
ALL	359	59.6 (13.0)
subtype1	52	61.1 (11.2)
subtype2	55	61.9 (9.6)
subtype3	113	62.1 (11.7)
subtype4	84	52.5 (15.5)
subtype5	55	61.5 (12.3)

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 = 0.0023 (Fisher's exact test), Q value = 0.017

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	168	84	3	63	7	9	3	1	2
subtype1	11	15	0	15	4	3	0	0	0
subtype2	32	10	0	3	1	2	1	1	0
subtype3	61	25	1	17	1	1	0	0	1
subtype4	40	19	1	17	0	2	2	0	1
subtype5	24	15	1	11	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 = 0.00029 (Fisher's exact test), Q value = 0.0034

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

nPatients	T0+T1	T2	T3	T4
ALL	179	91	77	13
subtype1	11	18	20	4
subtype2	38	11	5	2
subtype3	62	28	18	5
subtype4	43	19	21	1
subtype5	25	15	13	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 = 0.305 (Fisher's exact test), Q value = 0.48

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

nPatients	0	1
ALL	248	4
subtype1	33	1
subtype2	30	1
subtype3	76	0
subtype4	66	2
subtype5	43	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 = 0.473 (Fisher's exact test), Q value = 0.67

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

nPatients	0	1
ALL	261	4
subtype1	38	0
subtype2	32	0
subtype3	81	1
subtype4	66	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 = 6e-05 (Fisher's exact test), Q value = 0.00082

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

nPatients	FEMALE	MALE
ALL	117	245
subtype1	23	30
subtype2	13	43
subtype3	34	80
subtype4	40	44
subtype5	7	48

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 = 0.281 (Fisher's exact test), Q value = 0.46

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

nPatients	NO	YES
ALL	336	7
subtype1	51	1
subtype2	52	0
subtype3	106	1
subtype4	76	2
subtype5	51	3

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 = 0.00187 (Fisher's exact test), Q value = 0.016

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	353	7
subtype1	0	48	5
subtype2	0	56	0
subtype3	0	112	2
subtype4	2	82	0
subtype5	0	55	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 = 0.0222 (Fisher's exact test), Q value = 0.082

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

nPatients	R0	R1	R2	RX
ALL	317	15	1	22
subtype1	42	6	0	4
subtype2	46	4	0	4
subtype3	100	4	0	8
subtype4	76	1	1	6
subtype5	53	0	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 = 0.0154 (Fisher's exact test), Q value = 0.062

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	1	157	17	177
subtype1	0	25	1	26
subtype2	0	14	4	35
subtype3	0	45	6	57
subtype4	0	50	2	32
subtype5	1	23	4	27

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

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.256 (Fisher's exact test), Q value = 0.45

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	331
subtype1	3	49
subtype2	4	51
subtype3	5	98
subtype4	2	80
subtype5	0	53

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
Number of samples	62	128	50	123

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.473 (logrank test), Q value = 0.67

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	359	128	0.0 - 120.8 (19.8)
subtype1	61	23	0.0 - 120.8 (20.0)
subtype2	127	48	0.3 - 108.8 (19.3)
subtype3	49	20	0.2 - 83.2 (21.0)
subtype4	122	37	0.2 - 114.3 (20.4)

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 = 0.00217 (Kruskal-Wallis (anova)), Q value = 0.017

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

	nPatients	Mean (Std.Dev)
ALL	360	59.5 (13.0)
subtype1	60	62.8 (10.9)
subtype2	128	61.3 (13.2)
subtype3	50	55.4 (14.7)
subtype4	122	57.7 (12.3)

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 = 0.148 (Fisher's exact test), Q value = 0.32

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	169	84	3	62	8	9	3	1	2
subtype1	32	8	1	9	3	1	1	1	1
subtype2	60	32	0	26	3	3	0	0	0
subtype3	19	13	2	11	0	2	1	0	0
subtype4	58	31	0	16	2	3	1	0	1

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 = 0.852 (Fisher's exact test), Q value = 0.93

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

nPatients	T0+T1	T2	T3	T4
ALL	180	91	77	13
subtype1	35	11	13	3
subtype2	61	33	29	4
subtype3	22	13	13	2
subtype4	62	34	22	4

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 = 0.677 (Fisher's exact test), Q value = 0.81

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

nPatients	0	1
ALL	250	4
subtype1	40	1
subtype2	91	1
subtype3	37	1
subtype4	82	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 = 0.236 (Fisher's exact test), Q value = 0.43

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

nPatients	0	1
ALL	264	4
subtype1	36	1
subtype2	101	0
subtype3	39	1
subtype4	88	2

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.228 (Fisher's exact test), Q value = 0.42

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

nPatients	FEMALE	MALE
ALL	115	248
subtype1	19	43
subtype2	33	95
subtype3	20	30
subtype4	43	80

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

'MIRSEQ CNMF' versus 'RADIATION_THERAPY'

P value = 0.637 (Fisher's exact test), Q value = 0.78

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

nPatients	NO	YES
ALL	337	7
subtype1	58	0
subtype2	117	4
subtype3	47	1
subtype4	115	2

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

'MIRSEQ CNMF' versus 'HISTOLOGICAL_TYPE'

P value = 0.102 (Fisher's exact test), Q value = 0.25

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	354	7
subtype1	0	61	1
subtype2	0	128	0
subtype3	0	48	2
subtype4	2	117	4

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

'MIRSEQ CNMF' versus 'RESIDUAL_TUMOR'

P value = 0.835 (Fisher's exact test), Q value = 0.93

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

nPatients	R0	R1	R2	RX
ALL	318	15	1	22
subtype1	49	4	0	5
subtype2	116	4	0	7
subtype3	44	2	0	4
subtype4	109	5	1	6

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.42 (Fisher's exact test), Q value = 0.62

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	1	160	17	175
subtype1	0	20	4	36
subtype2	1	58	6	60
subtype3	0	27	3	20
subtype4	0	55	4	59

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

'MIRSEQ CNMF' versus 'ETHNICITY'

P value = 0.4 (Fisher's exact test), Q value = 0.6

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	332
subtype1	2	56
subtype2	3	119
subtype3	1	47
subtype4	8	110

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	41	97	56	122	47

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.056 (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	359	128	0.0 - 120.8 (19.8)
subtype1	40	18	0.0 - 120.8 (14.6)
subtype2	97	34	0.3 - 102.7 (21.5)
subtype3	54	26	0.3 - 113.0 (16.4)
subtype4	121	40	0.2 - 114.3 (21.3)
subtype5	47	10	0.3 - 108.8 (18.7)

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 = 3.28e-05 (Kruskal-Wallis (anova)), Q value = 0.00063

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

	nPatients	Mean (Std.Dev)
ALL	360	59.5 (13.0)
subtype1	41	62.0 (11.1)
subtype2	95	62.8 (11.7)
subtype3	56	58.7 (13.6)
subtype4	121	55.0 (13.9)
subtype5	47	63.4 (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 = 0.946 (Fisher's exact test), Q value = 0.99

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	169	84	3	62	8	9	3	1	2
subtype1	20	6	0	11	1	1	0	0	1
subtype2	44	23	1	11	4	1	1	1	0
subtype3	26	14	0	12	1	1	0	0	0
subtype4	56	29	2	20	1	5	2	0	1
subtype5	23	12	0	8	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 = 0.863 (Fisher's exact test), Q value = 0.93

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

nPatients	T0+T1	T2	T3	T4
ALL	180	91	77	13
subtype1	20	7	12	2
subtype2	49	26	17	5
subtype3	28	14	14	0
subtype4	59	32	25	5
subtype5	24	12	9	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 = 1 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	250	4
subtype1	30	0
subtype2	58	1
subtype3	39	1
subtype4	86	2
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 = 0.308 (Fisher's exact test), Q value = 0.48

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

nPatients	0	1
ALL	264	4
subtype1	26	1
subtype2	65	0
subtype3	42	0
subtype4	92	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 = 0.0162 (Fisher's exact test), Q value = 0.062

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

nPatients	FEMALE	MALE
ALL	115	248
subtype1	13	28
subtype2	29	68
subtype3	19	37
subtype4	48	74
subtype5	6	41

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

'MIRSEQ CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0521 (Fisher's exact test), Q value = 0.14

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

nPatients	NO	YES
ALL	337	7
subtype1	39	0
subtype2	93	0
subtype3	51	0
subtype4	111	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 = 0.851 (Fisher's exact test), Q value = 0.93

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	2	354	7
subtype1	0	41	0
subtype2	0	95	2
subtype3	0	55	1
subtype4	2	116	4
subtype5	0	47	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 = 0.356 (Fisher's exact test), Q value = 0.54

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

nPatients	R0	R1	R2	RX
ALL	318	15	1	22
subtype1	32	3	0	3
subtype2	83	5	0	6
subtype3	50	1	0	5
subtype4	107	6	1	8
subtype5	46	0	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 = 0.138 (Fisher's exact test), Q value = 0.31

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	1	160	17	175
subtype1	0	17	5	19
subtype2	0	34	5	52
subtype3	0	32	2	21
subtype4	0	56	4	59
subtype5	1	21	1	24

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

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.63 (Fisher's exact test), Q value = 0.78

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	14	332
subtype1	1	39
subtype2	4	86
subtype3	3	51
subtype4	6	112
subtype5	0	44

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	93	86	118	41

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

P value = 0.119 (logrank test), Q value = 0.28

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	334	120	0.0 - 120.8 (19.7)
subtype1	92	41	0.5 - 120.8 (17.9)
subtype2	85	30	0.2 - 107.1 (20.8)
subtype3	118	35	0.3 - 108.8 (21.1)
subtype4	39	14	0.0 - 90.3 (15.4)

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 = 0.00566 (Kruskal-Wallis (anova)), Q value = 0.033

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

	nPatients	Mean (Std.Dev)
ALL	335	59.5 (12.7)
subtype1	93	57.8 (13.5)
subtype2	85	58.8 (12.3)
subtype3	117	62.9 (10.2)
subtype4	40	55.1 (15.7)

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 = 0.00032 (Fisher's exact test), Q value = 0.0034

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	155	81	3	57	6	8	3	1	2
subtype1	34	22	0	26	5	5	0	0	0
subtype2	37	21	0	12	0	3	2	1	1
subtype3	66	27	1	12	0	0	0	0	1
subtype4	18	11	2	7	1	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 = 0.00901 (Fisher's exact test), Q value = 0.041

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

nPatients	T0+T1	T2	T3	T4
ALL	166	88	70	12
subtype1	35	23	30	5
subtype2	42	24	15	5
subtype3	70	30	14	2
subtype4	19	11	11	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 = 0.159 (Fisher's exact test), Q value = 0.33

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

nPatients	0	1
ALL	234	4
subtype1	65	3
subtype2	54	1
subtype3	83	0
subtype4	32	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 = 0.289 (Fisher's exact test), Q value = 0.46

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

nPatients	0	1
ALL	250	4
subtype1	73	0
subtype2	52	2
subtype3	89	1
subtype4	36	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 = 0.0144 (Fisher's exact test), Q value = 0.06

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

nPatients	FEMALE	MALE
ALL	112	226
subtype1	40	53
subtype2	29	57
subtype3	27	91
subtype4	16	25

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 = 0.757 (Fisher's exact test), Q value = 0.88

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

nPatients	NO	YES
ALL	313	7
subtype1	87	3
subtype2	80	1
subtype3	109	3
subtype4	37	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 = 0.123 (Fisher's exact test), Q value = 0.28

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	1	330	7
subtype1	0	90	3
subtype2	1	82	3
subtype3	0	118	0
subtype4	0	40	1

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 = 0.182 (Fisher's exact test), Q value = 0.36

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

nPatients	R0	R1	R2	RX
ALL	297	15	1	19
subtype1	79	5	0	6
subtype2	71	7	1	5
subtype3	111	2	0	4
subtype4	36	1	0	4

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 = 0.817 (Fisher's exact test), Q value = 0.93

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	1	154	16	157
subtype1	0	45	4	43
subtype2	0	35	4	43
subtype3	1	50	7	55
subtype4	0	24	1	16

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 = 0.54 (Fisher's exact test), Q value = 0.72

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	309
subtype1	6	82
subtype2	3	80
subtype3	3	107
subtype4	1	40

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	66	87	99	34	52

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

P value = 0.035 (logrank test), Q value = 0.1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	334	120	0.0 - 120.8 (19.7)
subtype1	64	31	0.7 - 107.1 (17.9)
subtype2	87	32	0.3 - 113.0 (21.8)
subtype3	98	32	0.2 - 114.3 (20.8)
subtype4	33	15	0.0 - 120.8 (13.7)
subtype5	52	10	0.3 - 79.4 (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 = 1.91e-05 (Kruskal-Wallis (anova)), Q value = 0.00048

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

	nPatients	Mean (Std.Dev)
ALL	335	59.5 (12.7)
subtype1	66	54.9 (12.7)
subtype2	85	63.1 (11.5)
subtype3	98	56.6 (14.0)
subtype4	34	63.2 (10.9)
subtype5	52	62.7 (9.6)

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 = 0.0744 (Fisher's exact test), Q value = 0.19

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	155	81	3	57	6	8	3	1	2
subtype1	25	15	0	20	1	1	0	0	0
subtype2	38	20	1	11	4	1	1	1	0
subtype3	46	25	2	12	0	5	2	0	1
subtype4	19	4	0	8	1	1	0	0	1
subtype5	27	17	0	6	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 = 0.0953 (Fisher's exact test), Q value = 0.23

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

nPatients	T0+T1	T2	T3	T4
ALL	166	88	70	12
subtype1	27	16	22	1
subtype2	43	23	16	5
subtype3	49	28	17	4
subtype4	19	4	9	2
subtype5	28	17	6	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 = 0.944 (Fisher's exact test), Q value = 0.99

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

nPatients	0	1
ALL	234	4
subtype1	47	1
subtype2	51	1
subtype3	69	2
subtype4	26	0
subtype5	41	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 = 0.15 (Fisher's exact test), Q value = 0.32

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

nPatients	0	1
ALL	250	4
subtype1	51	0
subtype2	59	0
subtype3	73	3
subtype4	22	1
subtype5	45	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 = 0.0395 (Fisher's exact test), Q value = 0.11

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

nPatients	FEMALE	MALE
ALL	112	226
subtype1	24	42
subtype2	26	61
subtype3	41	58
subtype4	12	22
subtype5	9	43

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 = 0.0521 (Fisher's exact test), Q value = 0.14

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

nPatients	NO	YES
ALL	313	7
subtype1	59	3
subtype2	83	0
subtype3	92	1
subtype4	32	0
subtype5	47	3

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 = 0.628 (Fisher's exact test), Q value = 0.78

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

nPatients	FIBROLAMELLAR CARCINOMA	HEPATOCELLULAR CARCINOMA	HEPATOCHOLANGIOCARCINOMA (MIXED)
ALL	1	330	7
subtype1	0	64	2
subtype2	0	86	1
subtype3	1	94	4
subtype4	0	34	0
subtype5	0	52	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 = 0.194 (Fisher's exact test), Q value = 0.37

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

nPatients	R0	R1	R2	RX
ALL	297	15	1	19
subtype1	59	4	0	3
subtype2	75	3	0	7
subtype3	85	5	1	8
subtype4	27	3	0	1
subtype5	51	0	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 = 0.0112 (Fisher's exact test), Q value = 0.049

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	1	154	16	157
subtype1	0	42	1	23
subtype2	0	27	7	47
subtype3	0	45	4	47
subtype4	0	13	3	18
subtype5	1	27	1	22

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 = 0.545 (Fisher's exact test), Q value = 0.72

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

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	13	309
subtype1	5	60
subtype2	3	77
subtype3	2	93
subtype4	1	32
subtype5	2	47

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/20139336/LIHC-TP.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LIHC-TP/19775332/LIHC-TP.merged_data.txt
Number of patients = 368
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.

Download Results

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

References

[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] 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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