Correlation between aggregated molecular cancer subtypes and selected clinical features

Liver Hepatocellular Carcinoma (Primary solid tumor)

23 September 2013 | analyses__2013_09_23

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 (2013): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1GT5KHB

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 119 patients, 4 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 do not correlate to any clinical features.
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 3 subtypes that correlate to 'GENDER'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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, 4 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	0.0959 (1.00)	0.0236 (1.00)	0.525 (1.00)	0.323 (1.00)	0.488 (1.00)	0.466 (1.00)	0.128 (1.00)	0.339 (1.00)
METHLYATION CNMF	0.387 (1.00)	0.14 (1.00)	0.49 (1.00)	0.738 (1.00)	0.62 (1.00)	0.218 (1.00)	0.111 (1.00)	0.812 (1.00)
RNAseq CNMF subtypes	0.455 (1.00)	0.115 (1.00)	0.361 (1.00)	0.0943 (1.00)	0.771 (1.00)	0.388 (1.00)	4.57e-07 (2.92e-05)	0.345 (1.00)
RNAseq cHierClus subtypes	0.535 (1.00)	0.0083 (0.498)	0.324 (1.00)	0.0565 (1.00)	1 (1.00)	0.511 (1.00)	3.17e-05 (0.002)	0.731 (1.00)
MIRSEQ CNMF	0.0907 (1.00)	0.021 (1.00)	0.224 (1.00)	0.71 (1.00)	0.628 (1.00)	0.13 (1.00)	0.168 (1.00)	0.666 (1.00)
MIRSEQ CHIERARCHICAL	0.0492 (1.00)	0.00404 (0.247)	0.954 (1.00)	0.992 (1.00)	0.704 (1.00)	0.396 (1.00)	0.305 (1.00)	0.396 (1.00)
MIRseq Mature CNMF subtypes	0.427 (1.00)	0.0429 (1.00)	0.0994 (1.00)	0.474 (1.00)	0.163 (1.00)	0.544 (1.00)	0.137 (1.00)	0.517 (1.00)
MIRseq Mature cHierClus subtypes	0.131 (1.00)	0.000702 (0.0435)	0.825 (1.00)	0.943 (1.00)	0.694 (1.00)	0.295 (1.00)	0.393 (1.00)	0.0511 (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	37	34	46

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

P value = 0.0959 (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	114	53	0.0 - 113.0 (15.7)
subtype1	35	17	0.1 - 113.0 (14.4)
subtype2	34	12	0.1 - 107.1 (19.9)
subtype3	45	24	0.0 - 102.7 (15.1)

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 'AGE'

P value = 0.0236 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	115	61.8 (13.7)
subtype1	36	65.6 (12.8)
subtype2	34	56.9 (13.1)
subtype3	45	62.5 (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 = 0.525 (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	45	25	2	25	3	4	1	1	1
subtype1	16	8	0	10	0	0	1	1	0
subtype2	16	7	0	6	1	2	0	0	0
subtype3	13	10	2	9	2	2	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 = 0.323 (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	48	28	34	6
subtype1	17	8	12	0
subtype2	17	7	7	2
subtype3	14	13	15	4

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

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

P value = 0.488 (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	74	3
subtype1	20	1
subtype2	26	0
subtype3	28	2

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 = 0.466 (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	90	2	25
subtype1	25	1	11
subtype2	29	0	5
subtype3	36	1	9

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 = 0.128 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	44	73
subtype1	9	28
subtype2	14	20
subtype3	21	25

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 = 0.339 (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	93	10	1	8
subtype1	28	3	1	1
subtype2	30	1	0	2
subtype3	35	6	0	5

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	26	34	50

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.387 (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	106	49	0.0 - 113.0 (14.9)
subtype1	26	11	0.1 - 107.1 (26.6)
subtype2	33	15	0.1 - 113.0 (12.9)
subtype3	47	23	0.0 - 102.7 (14.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.14 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	108	61.6 (14.0)
subtype1	26	59.3 (14.1)
subtype2	34	59.1 (15.7)
subtype3	48	64.5 (12.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.49 (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	40	23	2	24	3	5	1	1	1
subtype1	8	4	0	6	2	3	0	0	0
subtype2	11	7	1	8	1	1	1	0	1
subtype3	21	12	1	10	0	1	0	1	0

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 = 0.738 (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	43	26	33	7
subtype1	9	6	8	2
subtype2	11	7	13	3
subtype3	23	13	12	2

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 = 0.62 (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	70	3
subtype1	19	1
subtype2	24	0
subtype3	27	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 = 0.218 (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	84	2	24
subtype1	19	0	7
subtype2	27	2	5
subtype3	38	0	12

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.111 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	44	66
subtype1	14	12
subtype2	15	19
subtype3	15	35

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

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

P value = 0.812 (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	86	9	1	9
subtype1	18	3	0	2
subtype2	28	2	1	3
subtype3	40	4	0	4

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	30	15	21	22	27

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.455 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	112	51	0.0 - 113.0 (14.9)
subtype1	30	14	0.1 - 113.0 (17.3)
subtype2	13	6	0.1 - 60.4 (7.7)
subtype3	20	8	0.0 - 69.6 (21.8)
subtype4	22	10	0.4 - 93.7 (16.3)
subtype5	27	13	0.1 - 102.7 (19.1)

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 = 0.115 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	113	61.5 (13.7)
subtype1	30	56.9 (15.9)
subtype2	14	60.9 (9.6)
subtype3	21	63.4 (12.5)
subtype4	22	60.4 (15.6)
subtype5	26	66.5 (10.7)

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 = 0.361 (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	43	25	2	25	2	5	1	1	1
subtype1	8	9	1	4	1	2	0	0	1
subtype2	2	3	0	7	1	1	0	0	0
subtype3	10	3	0	2	0	1	1	1	0
subtype4	9	6	0	6	0	0	0	0	0
subtype5	14	4	1	6	0	1	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 = 0.0943 (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	46	28	33	7
subtype1	8	11	8	2
subtype2	2	3	8	2
subtype3	12	3	4	2
subtype4	9	7	6	0
subtype5	15	4	7	1

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 = 0.771 (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	73	3
subtype1	22	1
subtype2	10	0
subtype3	10	1
subtype4	13	0
subtype5	18	1

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 = 0.388 (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	88	2	25
subtype1	25	1	4
subtype2	11	0	4
subtype3	12	1	8
subtype4	17	0	5
subtype5	23	0	4

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 = 4.57e-07 (Chi-square test), Q value = 2.9e-05

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

nPatients	FEMALE	MALE
ALL	44	71
subtype1	20	10
subtype2	3	12
subtype3	3	18
subtype4	1	21
subtype5	17	10

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 = 0.345 (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	91	10	1	8
subtype1	24	2	0	4
subtype2	10	3	0	1
subtype3	16	2	1	0
subtype4	19	1	0	0
subtype5	22	2	0	3

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
Number of samples	43	51	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.535 (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	112	51	0.0 - 113.0 (14.9)
subtype1	41	17	0.0 - 102.7 (21.5)
subtype2	50	26	0.1 - 113.0 (14.0)
subtype3	21	8	0.4 - 93.7 (14.1)

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 = 0.0083 (ANOVA), Q value = 0.5

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

	nPatients	Mean (Std.Dev)
ALL	113	61.5 (13.7)
subtype1	41	66.1 (11.1)
subtype2	51	57.4 (14.3)
subtype3	21	62.6 (14.4)

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 = 0.324 (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	43	25	2	25	2	5	1	1	1
subtype1	22	7	1	7	0	1	0	1	0
subtype2	11	13	1	13	2	4	1	0	1
subtype3	10	5	0	5	0	0	0	0	0

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 = 0.0565 (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	46	28	33	7
subtype1	24	7	9	3
subtype2	12	15	19	4
subtype3	10	6	5	0

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 = 1 (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	73	3
subtype1	27	1
subtype2	33	2
subtype3	13	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 = 0.511 (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	88	2	25
subtype1	32	0	11
subtype2	40	2	9
subtype3	16	0	5

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 = 3.17e-05 (Fisher's exact test), Q value = 0.002

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

nPatients	FEMALE	MALE
ALL	44	71
subtype1	18	25
subtype2	26	25
subtype3	0	21

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 = 0.731 (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	91	10	1	8
subtype1	35	3	0	3
subtype2	39	5	1	5
subtype3	17	2	0	0

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	4	5
Number of samples	12	10	28	25	39

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0907 (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	110	50	0.0 - 113.0 (15.7)
subtype1	11	4	0.1 - 75.7 (29.2)
subtype2	10	7	0.0 - 83.6 (3.4)
subtype3	28	14	0.1 - 93.7 (18.3)
subtype4	23	11	0.1 - 102.7 (21.5)
subtype5	38	14	0.2 - 113.0 (15.7)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.021 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	112	61.4 (14.0)
subtype1	12	53.2 (17.0)
subtype2	10	55.4 (16.7)
subtype3	28	59.2 (12.7)
subtype4	23	66.7 (11.1)
subtype5	39	63.9 (13.3)

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

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.224 (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	42	24	2	25	3	5	1	1	1
subtype1	2	2	1	4	0	2	0	0	0
subtype2	3	2	0	4	0	1	0	0	0
subtype3	11	6	0	3	1	2	1	0	1
subtype4	11	2	1	4	2	0	0	1	0
subtype5	15	12	0	10	0	0	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 = 0.71 (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	45	27	34	7
subtype1	4	2	5	1
subtype2	3	2	4	1
subtype3	11	8	6	2
subtype4	12	2	9	2
subtype5	15	13	10	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 = 0.628 (Chi-square test), Q value = 1

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

nPatients	0	1
ALL	73	3
subtype1	9	1
subtype2	8	0
subtype3	19	1
subtype4	14	1
subtype5	23	0

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 = 0.13 (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	89	2	23
subtype1	10	0	2
subtype2	7	0	3
subtype3	23	2	3
subtype4	16	0	9
subtype5	33	0	6

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.168 (Chi-square test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	43	71
subtype1	7	5
subtype2	2	8
subtype3	14	14
subtype4	8	17
subtype5	12	27

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

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

P value = 0.666 (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	89	10	1	9
subtype1	9	1	0	2
subtype2	4	2	0	1
subtype3	24	1	1	2
subtype4	21	2	0	1
subtype5	31	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
Number of samples	9	59	46

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0492 (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	110	50	0.0 - 113.0 (15.7)
subtype1	9	6	0.0 - 83.6 (3.3)
subtype2	56	21	0.1 - 113.0 (14.5)
subtype3	45	23	0.1 - 75.7 (21.7)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00404 (ANOVA), Q value = 0.25

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

	nPatients	Mean (Std.Dev)
ALL	112	61.4 (14.0)
subtype1	9	57.7 (16.0)
subtype2	57	65.6 (11.3)
subtype3	46	56.9 (15.2)

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

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.954 (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	42	24	2	25	3	5	1	1	1
subtype1	3	2	0	3	0	1	0	0	0
subtype2	24	13	1	13	2	1	0	1	0
subtype3	15	9	1	9	1	3	1	0	1

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 = 0.992 (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	45	27	34	7
subtype1	3	2	3	1
subtype2	25	14	17	3
subtype3	17	11	14	3

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 = 0.704 (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	73	3
subtype1	7	0
subtype2	35	1
subtype3	31	2

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 = 0.396 (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	89	2	23
subtype1	6	0	3
subtype2	47	0	12
subtype3	36	2	8

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.305 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	43	71
subtype1	2	7
subtype2	20	39
subtype3	21	25

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

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

P value = 0.396 (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	89	10	1	9
subtype1	4	2	0	0
subtype2	47	5	0	5
subtype3	38	3	1	4

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
Number of samples	26	53	35

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

P value = 0.427 (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	110	50	0.0 - 113.0 (15.7)
subtype1	25	10	0.0 - 102.7 (25.3)
subtype2	51	26	0.3 - 113.0 (14.9)
subtype3	34	14	0.1 - 93.7 (17.3)

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 = 0.0429 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	112	61.4 (14.0)
subtype1	26	56.9 (15.5)
subtype2	51	64.8 (12.7)
subtype3	35	59.7 (13.7)

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 = 0.0994 (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	42	24	2	25	3	5	1	1	1
subtype1	8	3	2	6	1	3	1	0	0
subtype2	23	12	0	13	1	0	0	0	0
subtype3	11	9	0	6	1	2	0	1	1

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 = 0.474 (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	45	27	34	7
subtype1	10	3	10	3
subtype2	23	13	15	2
subtype3	12	11	9	2

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 = 0.163 (Fisher's exact test), Q value = 1

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

nPatients	0	1
ALL	73	3
subtype1	18	1
subtype2	34	0
subtype3	21	2

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 = 0.544 (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	89	2	23
subtype1	20	1	5
subtype2	40	0	13
subtype3	29	1	5

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 = 0.137 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	43	71
subtype1	11	15
subtype2	15	38
subtype3	17	18

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 = 0.517 (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	89	10	1	9
subtype1	19	2	1	2
subtype2	39	6	0	5
subtype3	31	2	0	2

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	39	10	65

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

P value = 0.131 (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	110	50	0.0 - 113.0 (15.7)
subtype1	38	16	0.1 - 75.7 (20.7)
subtype2	10	7	0.0 - 83.6 (3.4)
subtype3	62	27	0.2 - 113.0 (15.7)

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 = 0.000702 (ANOVA), Q value = 0.044

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

	nPatients	Mean (Std.Dev)
ALL	112	61.4 (14.0)
subtype1	39	55.1 (15.0)
subtype2	10	59.3 (15.9)
subtype3	63	65.6 (11.5)

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 = 0.825 (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	42	24	2	25	3	5	1	1	1
subtype1	12	7	1	8	1	3	1	0	1
subtype2	3	2	0	4	0	1	0	0	0
subtype3	27	15	1	13	2	1	0	1	0

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 = 0.943 (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	45	27	34	7
subtype1	14	9	12	3
subtype2	3	2	4	1
subtype3	28	16	18	3

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 = 0.694 (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	73	3
subtype1	27	2
subtype2	8	0
subtype3	38	1

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 = 0.295 (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	89	2	23
subtype1	31	2	6
subtype2	7	0	3
subtype3	51	0	14

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 = 0.393 (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	43	71
subtype1	18	21
subtype2	3	7
subtype3	22	43

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 = 0.0511 (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	89	10	1	9
subtype1	33	2	1	3
subtype2	4	3	0	0
subtype3	52	5	0	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.clin.merged.picked.txt
Number of patients = 119
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)

Made with Nozzle