Correlation between aggregated molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

16 April 2014 | analyses__2014_04_16

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 (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C19P308G

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 8 clinical features across 155 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that correlate to 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE'.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
6 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes correlate to 'AGE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 8 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.


Clinical Features	Time to Death	AGE	NEOPLASM DISEASESTAGE	PATHOLOGY T STAGE	PATHOLOGY N STAGE	PATHOLOGY M STAGE	GENDER	COMPLETENESS OF RESECTION
Statistical Tests	logrank test	ANOVA	Chi-square test	Chi-square test	Fisher's exact test	Chi-square test	Fisher's exact test	Chi-square test
Copy Number Ratio CNMF subtypes	0.0981 (1.00)	0.61 (1.00)	0.2 (1.00)	0.153 (1.00)	1 (1.00)	0.89 (1.00)	0.0129 (0.707)	0.301 (1.00)
METHLYATION CNMF	0.286 (1.00)	0.000212 (0.0129)	0.738 (1.00)	0.933 (1.00)	0.775 (1.00)	0.324 (1.00)	0.202 (1.00)	0.803 (1.00)
RNAseq CNMF subtypes	0.0124 (0.696)	0.0154 (0.83)	0.24 (1.00)	0.0509 (1.00)	0.456 (1.00)	0.699 (1.00)	5.98e-06 (0.000382)	0.376 (1.00)
RNAseq cHierClus subtypes	0.589 (1.00)	0.00605 (0.345)	0.51 (1.00)	0.115 (1.00)	0.412 (1.00)	0.457 (1.00)	0.00029 (0.0174)	0.611 (1.00)
MIRSEQ CNMF	0.141 (1.00)	0.00106 (0.0627)	0.605 (1.00)	0.317 (1.00)	0.181 (1.00)	0.558 (1.00)	0.894 (1.00)	0.817 (1.00)
MIRSEQ CHIERARCHICAL	0.33 (1.00)	6.38e-05 (0.00395)	0.83 (1.00)	0.926 (1.00)	1 (1.00)	0.527 (1.00)	0.038 (1.00)	0.421 (1.00)
MIRseq Mature CNMF subtypes	0.541 (1.00)	0.00198 (0.115)	0.644 (1.00)	0.619 (1.00)	0.679 (1.00)	0.526 (1.00)	0.0309 (1.00)	0.445 (1.00)
MIRseq Mature cHierClus subtypes	0.144 (1.00)	6.23e-06 (0.000393)	0.461 (1.00)	0.677 (1.00)	1 (1.00)	0.232 (1.00)	0.289 (1.00)	0.629 (1.00)

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	43	58	48

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

P value = 0.0981 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	63	0.0 - 113.0 (14.1)
subtype1	42	21	0.1 - 101.0 (13.7)
subtype2	58	20	0.1 - 107.1 (18.8)
subtype3	47	22	0.0 - 113.0 (11.7)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.61 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	147	61.6 (13.8)
subtype1	42	63.4 (13.5)
subtype2	57	61.0 (13.9)
subtype3	48	60.9 (14.0)

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

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

P value = 0.2 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	58	36	2	30	4	5	1	1	2
subtype1	21	9	0	8	1	0	1	1	0
subtype2	25	11	0	13	2	4	0	0	1
subtype3	12	16	2	9	1	1	0	0	1

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

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

P value = 0.153 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	61	40	40	8
subtype1	22	11	10	0
subtype2	26	12	16	4
subtype3	13	17	14	4

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

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

P value = 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	97	3
subtype1	26	1
subtype2	44	1
subtype3	27	1

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

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

P value = 0.89 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	115	3	31
subtype1	31	1	11
subtype2	47	1	10
subtype3	37	1	10

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 = 0.0129 (Fisher's exact test), Q value = 0.71

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

nPatients	FEMALE	MALE
ALL	58	91
subtype1	9	34
subtype2	28	30
subtype3	21	27

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 'COMPLETENESS.OF.RESECTION'

P value = 0.301 (Chi-square test), Q value = 1

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

nPatients	R0	R1	R2	RX
ALL	122	11	1	10
subtype1	33	5	1	3
subtype2	50	1	0	3
subtype3	39	5	0	4

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	42	43	69

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.286 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	63	0.0 - 113.0 (13.9)
subtype1	42	14	0.1 - 107.1 (13.8)
subtype2	42	20	0.1 - 113.0 (13.2)
subtype3	67	29	0.0 - 102.7 (15.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.000212 (ANOVA), Q value = 0.013

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

	nPatients	Mean (Std.Dev)
ALL	152	61.2 (14.2)
subtype1	42	56.0 (15.9)
subtype2	43	58.4 (15.7)
subtype3	67	66.3 (9.8)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.738 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	58	36	2	34	4	6	1	1	2
subtype1	15	9	0	11	2	4	0	0	0
subtype2	15	10	1	9	1	1	1	0	1
subtype3	28	17	1	14	1	1	0	1	1

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

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

P value = 0.933 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	61	40	44	9
subtype1	16	10	13	3
subtype2	15	11	14	3
subtype3	30	19	17	3

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

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

P value = 0.775 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	101	3
subtype1	34	1
subtype2	30	0
subtype3	37	2

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

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

P value = 0.324 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	121	3	30
subtype1	35	0	7
subtype2	35	2	6
subtype3	51	1	17

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.202 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	61	93
subtype1	19	23
subtype2	20	23
subtype3	22	47

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

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.803 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	126	11	1	11
subtype1	34	2	0	3
subtype2	35	4	1	3
subtype3	57	5	0	5

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3	4	5
Number of samples	35	23	43	21	25

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0124 (logrank test), Q value = 0.7

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	145	62	0.0 - 113.0 (14.1)
subtype1	35	17	0.1 - 113.0 (15.8)
subtype2	22	13	0.1 - 45.1 (8.4)
subtype3	42	18	0.1 - 102.7 (20.3)
subtype4	21	7	0.0 - 80.8 (12.7)
subtype5	25	7	0.2 - 93.7 (13.6)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0154 (ANOVA), Q value = 0.83

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

	nPatients	Mean (Std.Dev)
ALL	145	61.7 (13.8)
subtype1	35	57.7 (15.4)
subtype2	22	63.8 (12.3)
subtype3	42	66.4 (10.9)
subtype4	21	56.0 (15.0)
subtype5	25	62.6 (13.8)

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

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.24 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	57	34	2	31	3	6	1	1	2
subtype1	8	12	1	6	1	3	1	0	1
subtype2	5	7	0	7	1	0	0	0	0
subtype3	23	5	1	9	0	0	0	1	1
subtype4	10	3	0	3	0	3	0	0	0
subtype5	11	7	0	6	1	0	0	0	0

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

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

P value = 0.0509 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	60	38	40	9
subtype1	9	14	10	2
subtype2	5	9	8	1
subtype3	24	5	11	3
subtype4	11	3	4	3
subtype5	11	7	7	0

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

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

P value = 0.456 (Chi-square test), Q value = 1

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

nPatients	0	1
ALL	95	3
subtype1	22	2
subtype2	14	0
subtype3	29	1
subtype4	14	0
subtype5	16	0

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

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

P value = 0.699 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	113	3	31
subtype1	28	2	5
subtype2	17	0	6
subtype3	33	1	9
subtype4	15	0	6
subtype5	20	0	5

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 5.98e-06 (Chi-square test), Q value = 0.00038

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

nPatients	FEMALE	MALE
ALL	56	91
subtype1	25	10
subtype2	7	16
subtype3	15	28
subtype4	8	13
subtype5	1	24

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

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.376 (Chi-square test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	120	11	1	10
subtype1	29	3	1	2
subtype2	15	4	0	3
subtype3	37	3	0	3
subtype4	16	1	0	2
subtype5	23	0	0	0

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3	4
Number of samples	25	25	55	42

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.589 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	145	62	0.0 - 113.0 (14.1)
subtype1	24	9	0.0 - 80.8 (17.0)
subtype2	25	7	0.2 - 93.7 (14.1)
subtype3	54	27	0.1 - 113.0 (13.6)
subtype4	42	19	0.1 - 102.7 (14.5)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00605 (ANOVA), Q value = 0.34

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

	nPatients	Mean (Std.Dev)
ALL	145	61.7 (13.8)
subtype1	24	61.7 (14.3)
subtype2	25	63.2 (13.3)
subtype3	55	57.2 (15.2)
subtype4	41	66.9 (9.7)

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

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.51 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	57	34	2	31	3	6	1	1	2
subtype1	14	4	0	2	0	2	0	1	0
subtype2	11	7	0	5	1	1	0	0	0
subtype3	14	15	1	14	2	3	1	0	1
subtype4	18	8	1	10	0	0	0	0	1

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

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

P value = 0.115 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	60	38	40	9
subtype1	16	4	3	2
subtype2	12	7	6	0
subtype3	14	17	20	4
subtype4	18	10	11	3

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

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

P value = 0.412 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	95	3
subtype1	16	1
subtype2	15	1
subtype3	37	1
subtype4	27	0

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

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

P value = 0.457 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	113	3	31
subtype1	16	0	9
subtype2	20	0	5
subtype3	44	2	9
subtype4	33	1	8

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00029 (Fisher's exact test), Q value = 0.017

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

nPatients	FEMALE	MALE
ALL	56	91
subtype1	6	19
subtype2	2	23
subtype3	29	26
subtype4	19	23

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

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.611 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	120	11	1	10
subtype1	20	2	0	1
subtype2	23	0	0	0
subtype3	43	5	1	5
subtype4	34	4	0	4

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3
Number of samples	56	33	61

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.141 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	61	0.0 - 113.0 (13.9)
subtype1	55	19	0.1 - 101.0 (14.4)
subtype2	32	18	0.0 - 102.7 (10.0)
subtype3	60	24	0.2 - 113.0 (14.0)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00106 (ANOVA), Q value = 0.063

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

	nPatients	Mean (Std.Dev)
ALL	148	61.3 (14.2)
subtype1	56	55.9 (14.2)
subtype2	31	65.8 (12.8)
subtype3	61	63.9 (13.4)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.605 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	56	35	2	33	4	6	1	1	2
subtype1	21	10	1	14	1	3	1	0	1
subtype2	13	6	1	5	1	2	0	1	1
subtype3	22	19	0	14	2	1	0	0	0

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

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

P value = 0.317 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	59	39	43	9
subtype1	22	11	18	5
subtype2	15	7	8	3
subtype3	22	21	17	1

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

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

P value = 0.181 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	98	3
subtype1	39	0
subtype2	22	2
subtype3	37	1

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

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

P value = 0.558 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	3	29
subtype1	45	2	9
subtype2	24	1	8
subtype3	49	0	12

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.894 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	58	92
subtype1	21	35
subtype2	12	21
subtype3	25	36

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

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.817 (Chi-square test), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	11	1	11
subtype1	46	4	1	5
subtype2	24	3	0	3
subtype3	52	4	0	3

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3	4
Number of samples	15	67	26	42

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.33 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	61	0.0 - 113.0 (13.9)
subtype1	15	9	0.0 - 83.6 (10.0)
subtype2	66	27	0.1 - 101.0 (14.1)
subtype3	25	7	0.3 - 93.7 (11.0)
subtype4	41	18	0.1 - 113.0 (15.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 6.38e-05 (ANOVA), Q value = 0.004

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

	nPatients	Mean (Std.Dev)
ALL	148	61.3 (14.2)
subtype1	15	62.1 (14.1)
subtype2	67	55.6 (14.9)
subtype3	26	66.6 (12.0)
subtype4	40	66.9 (10.5)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.83 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	56	35	2	33	4	6	1	1	2
subtype1	5	3	0	4	0	1	0	0	1
subtype2	25	14	1	15	1	5	1	0	1
subtype3	11	6	0	7	1	0	0	0	0
subtype4	15	12	1	7	2	0	0	1	0

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

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

P value = 0.926 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	59	39	43	9
subtype1	5	4	4	2
subtype2	27	16	19	5
subtype3	11	7	8	0
subtype4	16	12	12	2

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

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

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

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

nPatients	0	1
ALL	98	3
subtype1	11	0
subtype2	46	2
subtype3	17	0
subtype4	24	1

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

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

P value = 0.527 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	3	29
subtype1	10	1	4
subtype2	54	2	11
subtype3	22	0	4
subtype4	32	0	10

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.038 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	58	92
subtype1	5	10
subtype2	31	36
subtype3	4	22
subtype4	18	24

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

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.421 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	11	1	11
subtype1	9	3	0	0
subtype2	57	3	1	6
subtype3	20	3	0	2
subtype4	36	2	0	3

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

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

Cluster Labels	1	2	3	4	5	6
Number of samples	15	12	30	25	32	36

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

P value = 0.541 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	61	0.0 - 113.0 (13.9)
subtype1	14	4	0.1 - 75.7 (8.7)
subtype2	12	8	0.0 - 83.6 (11.8)
subtype3	30	14	0.1 - 101.0 (15.4)
subtype4	25	8	0.1 - 80.8 (12.8)
subtype5	30	13	0.2 - 113.0 (14.4)
subtype6	36	14	0.3 - 107.1 (12.5)

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

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00198 (ANOVA), Q value = 0.11

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

	nPatients	Mean (Std.Dev)
ALL	148	61.3 (14.2)
subtype1	15	55.5 (15.4)
subtype2	12	61.3 (15.5)
subtype3	30	57.0 (17.6)
subtype4	25	56.2 (13.0)
subtype5	30	68.3 (8.3)
subtype6	36	64.8 (11.6)

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

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

P value = 0.644 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	56	35	2	33	4	6	1	1	2
subtype1	3	4	0	6	0	2	0	0	0
subtype2	5	2	0	3	0	1	0	0	1
subtype3	8	7	1	7	1	2	0	0	1
subtype4	11	5	0	5	0	1	1	0	0
subtype5	10	8	1	5	2	0	0	1	0
subtype6	19	9	0	7	1	0	0	0	0

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

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

P value = 0.619 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	59	39	43	9
subtype1	4	4	6	1
subtype2	5	2	3	2
subtype3	8	9	11	2
subtype4	12	5	7	1
subtype5	11	10	8	3
subtype6	19	9	8	0

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

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

P value = 0.679 (Chi-square test), Q value = 1

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

nPatients	0	1
ALL	98	3
subtype1	11	1
subtype2	10	0
subtype3	22	1
subtype4	15	0
subtype5	18	1
subtype6	22	0

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

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

P value = 0.526 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	3	29
subtype1	14	0	1
subtype2	8	1	3
subtype3	25	1	4
subtype4	19	1	5
subtype5	23	0	9
subtype6	29	0	7

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

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.0309 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	58	92
subtype1	7	8
subtype2	4	8
subtype3	19	11
subtype4	7	18
subtype5	12	20
subtype6	9	27

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

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.445 (Chi-square test), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	11	1	11
subtype1	12	1	0	2
subtype2	7	2	0	0
subtype3	25	2	0	3
subtype4	22	1	1	1
subtype5	23	4	0	4
subtype6	33	1	0	1

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	13	69	68

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

P value = 0.144 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	147	61	0.0 - 113.0 (13.9)
subtype1	13	8	0.0 - 83.6 (10.0)
subtype2	67	25	0.2 - 113.0 (14.2)
subtype3	67	28	0.1 - 101.0 (13.9)

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

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 6.23e-06 (ANOVA), Q value = 0.00039

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

	nPatients	Mean (Std.Dev)
ALL	148	61.3 (14.2)
subtype1	13	61.3 (14.8)
subtype2	67	67.1 (10.4)
subtype3	68	55.5 (15.1)

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

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

P value = 0.461 (Chi-square test), Q value = 1

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

nPatients	STAGE I	STAGE II	STAGE III	STAGE IIIA	STAGE IIIB	STAGE IIIC	STAGE IV	STAGE IVA	STAGE IVB
ALL	56	35	2	33	4	6	1	1	2
subtype1	5	2	0	3	0	1	0	0	1
subtype2	27	19	1	13	3	0	0	1	0
subtype3	24	14	1	17	1	5	1	0	1

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

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

P value = 0.677 (Chi-square test), Q value = 1

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

nPatients	T1	T2	T3	T4
ALL	59	39	43	9
subtype1	5	3	3	2
subtype2	28	20	19	2
subtype3	26	16	21	5

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

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

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

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

nPatients	0	1
ALL	98	3
subtype1	10	0
subtype2	42	1
subtype3	46	2

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

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

P value = 0.232 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	118	3	29
subtype1	8	1	4
subtype2	55	0	14
subtype3	55	2	11

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

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.289 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	58	92
subtype1	4	9
subtype2	23	46
subtype3	31	37

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

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.629 (Chi-square test), Q value = 1

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	122	11	1	11
subtype1	8	2	0	0
subtype2	57	5	0	5
subtype3	57	4	1	6

Figure S64. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LIHC-TP.mergedcluster.txt
Clinical data file = LIHC-TP.merged_data.txt
Number of patients = 155
Number of clustering approaches = 8
Number of selected clinical features = 8
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

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

References

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

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

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

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[6] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

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