Correlation between aggregated molecular cancer subtypes and selected clinical features

Kidney Chromophobe (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/C1SX6BHN

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 10 clinical features across 64 patients, no significant finding 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 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 8 different clustering approaches and 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding 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	0.329 (1.00)	0.534 (1.00)	0.179 (1.00)	0.0507 (1.00)	0.168 (1.00)	0.0756 (1.00)	0.249 (1.00)	0.693 (1.00)
AGE	ANOVA	0.894 (1.00)	0.442 (1.00)	0.345 (1.00)	0.271 (1.00)	0.00506 (0.349)	0.159 (1.00)	0.403 (1.00)	0.052 (1.00)
NEOPLASM DISEASESTAGE	Chi-square test	0.493 (1.00)	0.0681 (1.00)	0.217 (1.00)	0.278 (1.00)	0.18 (1.00)	0.254 (1.00)	0.163 (1.00)	0.61 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.489 (1.00)	0.0308 (1.00)	0.108 (1.00)	0.197 (1.00)	0.142 (1.00)	0.245 (1.00)	0.0974 (1.00)	0.455 (1.00)
PATHOLOGY N STAGE	Fisher's exact test	0.565 (1.00)	0.383 (1.00)	0.113 (1.00)	0.105 (1.00)	0.722 (1.00)	0.387 (1.00)	0.56 (1.00)	1 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.786 (1.00)	0.22 (1.00)	0.188 (1.00)	0.0973 (1.00)	0.292 (1.00)	0.071 (1.00)	0.363 (1.00)	0.128 (1.00)
GENDER	Fisher's exact test	0.535 (1.00)	0.838 (1.00)	0.487 (1.00)	0.654 (1.00)	0.654 (1.00)	0.463 (1.00)	0.194 (1.00)	0.242 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA	0.0658 (1.00)	0.608 (1.00)	0.264 (1.00)	0.264 (1.00)	0.0288 (1.00)	0.148 (1.00)	0.0859 (1.00)	0.142 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA		0.088 (1.00)	0.105 (1.00)	0.105 (1.00)	0.497 (1.00)			0.497 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA

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	5	45	14

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

P value = 0.329 (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	63	7	0.6 - 151.9 (63.9)
subtype1	5	1	32.7 - 92.7 (38.1)
subtype2	44	6	0.6 - 151.9 (63.8)
subtype3	14	0	2.5 - 137.1 (68.3)

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 = 0.894 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	5	51.6 (11.7)
subtype2	45	51.7 (14.8)
subtype3	14	49.6 (14.5)

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 = 0.493 (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 IV
ALL	21	23	14	6
subtype1	1	2	2	0
subtype2	13	16	10	6
subtype3	7	5	2	0

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 = 0.489 (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	21	23	20
subtype1	1	2	2
subtype2	13	16	16
subtype3	7	5	2

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 = 0.565 (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	N0	N1+N2
ALL	40	5
subtype1	2	0
subtype2	30	5
subtype3	8	0

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

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

P value = 0.786 (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	32	2	9
subtype1	3	0	1
subtype2	21	2	7
subtype3	8	0	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 = 0.535 (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	25	39
subtype1	3	2
subtype2	16	29
subtype3	6	8

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 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0658 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	1	90.0 (NA)
subtype2	7	85.7 (9.8)
subtype3	3	96.7 (5.8)

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	17	34	13

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.534 (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	63	7	0.6 - 151.9 (63.9)
subtype1	17	1	23.5 - 137.1 (72.7)
subtype2	33	5	2.5 - 151.9 (65.2)
subtype3	13	1	0.6 - 98.3 (51.0)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.442 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	17	49.3 (14.0)
subtype2	34	50.4 (13.9)
subtype3	13	55.7 (16.3)

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

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0681 (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 IV
ALL	21	23	14	6
subtype1	8	6	3	0
subtype2	6	16	8	4
subtype3	7	1	3	2

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 = 0.0308 (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	21	23	20
subtype1	8	6	3
subtype2	6	16	12
subtype3	7	1	5

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 = 0.383 (Fisher's exact test), Q value = 1

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

nPatients	N0	N1+N2
ALL	40	5
subtype1	12	0
subtype2	23	4
subtype3	5	1

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 = 0.22 (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	32	2	9
subtype1	11	0	2
subtype2	16	2	3
subtype3	5	0	4

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.838 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	25	39
subtype1	7	10
subtype2	14	20
subtype3	4	9

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

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.608 (ANOVA), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	93.3 (5.8)
subtype2	3	90.0 (10.0)
subtype3	5	86.0 (11.4)

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.088 (ANOVA), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	27.5 (21.5)
subtype1	3	12.0 (13.1)
subtype2	5	39.0 (24.4)
subtype3	2	22.0 (2.8)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S20. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	19	20	15	10

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.179 (logrank test), Q value = 1

Table S21. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	19	2	24.8 - 137.1 (76.9)
subtype2	19	0	2.5 - 151.9 (62.3)
subtype3	15	4	16.7 - 114.2 (66.5)
subtype4	10	1	0.6 - 98.3 (30.1)

Figure S18. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.345 (ANOVA), Q value = 1

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	19	47.7 (12.9)
subtype2	20	50.8 (15.4)
subtype3	15	51.6 (14.3)
subtype4	10	58.0 (14.6)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.217 (Chi-square test), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	7	6	5	1
subtype2	8	9	2	1
subtype3	1	7	5	2
subtype4	5	1	2	2

Figure S20. 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 = 0.108 (Chi-square test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	7	6	6
subtype2	8	9	3
subtype3	1	7	7
subtype4	5	1	4

Figure S21. 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 = 0.113 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	12	1
subtype2	15	0
subtype3	10	3
subtype4	3	1

Figure S22. 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 = 0.188 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	13	0	2
subtype2	9	1	2
subtype3	7	1	1
subtype4	3	0	4

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.487 (Fisher's exact test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	9	10
subtype2	9	11
subtype3	5	10
subtype4	2	8

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.264 (ANOVA), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	93.3 (5.8)
subtype2	3	93.3 (11.5)
subtype3	1	90.0 (NA)
subtype4	4	82.5 (9.6)

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	27.5 (21.5)
subtype1	3	14.3 (11.2)
subtype2	3	20.7 (16.3)
subtype3	3	50.0 (24.1)
subtype4	1	20.0 (NA)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S30. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	10	10	16	6	22

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0507 (logrank test), Q value = 1

Table S31. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	10	4	16.7 - 114.2 (60.0)
subtype2	10	1	0.6 - 98.3 (26.1)
subtype3	15	0	3.5 - 151.9 (72.7)
subtype4	6	0	2.5 - 109.2 (52.3)
subtype5	22	2	24.8 - 137.1 (75.4)

Figure S27. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.271 (ANOVA), Q value = 1

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	10	53.6 (12.7)
subtype2	10	55.1 (14.4)
subtype3	16	48.1 (14.1)
subtype4	6	60.3 (21.1)
subtype5	22	48.1 (12.8)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.278 (Chi-square test), Q value = 1

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	1	3	4	2
subtype2	5	2	1	2
subtype3	7	6	2	1
subtype4	0	4	2	0
subtype5	8	8	5	1

Figure S29. 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 = 0.197 (Chi-square test), Q value = 1

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	1	3	6
subtype2	5	2	3
subtype3	7	6	3
subtype4	0	4	2
subtype5	8	8	6

Figure S30. 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 = 0.105 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	6	3
subtype2	3	1
subtype3	12	0
subtype4	4	0
subtype5	15	1

Figure S31. 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 = 0.0973 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	4	1	1
subtype2	2	0	4
subtype3	7	1	2
subtype4	3	0	0
subtype5	16	0	2

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.654 (Chi-square test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	4	6
subtype2	2	8
subtype3	8	8
subtype4	2	4
subtype5	9	13

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.264 (ANOVA), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	1	90.0 (NA)
subtype2	4	82.5 (9.6)
subtype3	3	93.3 (11.5)
subtype5	3	93.3 (5.8)

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.105 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	27.5 (21.5)
subtype1	3	50.0 (24.1)
subtype2	1	20.0 (NA)
subtype3	3	20.7 (16.3)
subtype5	3	14.3 (11.2)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S40. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	20	23	21

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.168 (logrank test), Q value = 1

Table S41. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	19	4	0.6 - 151.9 (51.0)
subtype2	23	2	3.5 - 137.1 (79.0)
subtype3	21	1	11.2 - 114.2 (63.9)

Figure S36. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.00506 (ANOVA), Q value = 0.35

Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	20	58.2 (15.9)
subtype2	23	51.6 (12.9)
subtype3	21	44.0 (11.1)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.18 (Chi-square test), Q value = 1

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	6	4	7	3
subtype2	9	8	3	3
subtype3	6	11	4	0

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.142 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	6	4	10
subtype2	9	8	6
subtype3	6	11	4

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.722 (Fisher's exact test), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	10	2
subtype2	14	2
subtype3	16	1

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.292 (Chi-square test), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	8	1	5
subtype2	14	1	1
subtype3	10	0	3

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.654 (Fisher's exact test), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	6	14
subtype2	10	13
subtype3	9	12

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0288 (ANOVA), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	5	82.0 (8.4)
subtype2	2	95.0 (7.1)
subtype3	4	95.0 (5.8)

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.497 (ANOVA), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	27.5 (21.5)
subtype1	3	31.7 (14.6)
subtype2	3	14.3 (11.2)
subtype3	4	34.2 (30.2)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S50. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2
Number of samples	9	55

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.0756 (logrank test), Q value = 1

Table S51. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	9	2	0.6 - 75.7 (28.8)
subtype2	54	5	2.5 - 151.9 (70.3)

Figure S45. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.159 (t-test), Q value = 1

Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	9	58.4 (15.6)
subtype2	55	50.0 (14.0)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.254 (Chi-square test), Q value = 1

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	4	1	2	2
subtype2	17	22	12	4

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

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

P value = 0.245 (Chi-square test), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	4	1	4
subtype2	17	22	16

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

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

P value = 0.387 (Fisher's exact test), Q value = 1

Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	3	1
subtype2	37	4

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

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

P value = 0.071 (Chi-square test), Q value = 1

Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	2	0	3
subtype2	30	2	6

Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.463 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	2	7
subtype2	23	32

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.148 (t-test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	80.0 (10.0)
subtype2	8	92.5 (7.1)

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S59. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	10	15	15	10	14

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

P value = 0.249 (logrank test), Q value = 1

Table S60. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	10	2	3.5 - 97.0 (63.4)
subtype2	15	0	2.5 - 123.1 (51.0)
subtype3	15	2	24.8 - 137.1 (79.0)
subtype4	10	0	11.2 - 86.1 (67.2)
subtype5	13	3	0.6 - 151.9 (52.3)

Figure S53. 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 = 0.403 (ANOVA), Q value = 1

Table S61. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	10	50.2 (10.7)
subtype2	15	51.1 (15.9)
subtype3	15	48.6 (16.1)
subtype4	10	47.3 (12.1)
subtype5	14	57.6 (14.3)

Figure S54. 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 = 0.163 (Chi-square test), Q value = 1

Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	5	0	3	2
subtype2	6	7	2	0
subtype3	4	8	1	2
subtype4	2	5	3	0
subtype5	4	3	5	2

Figure S55. 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 = 0.0974 (Chi-square test), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	5	0	5
subtype2	6	7	2
subtype3	4	8	3
subtype4	2	5	3
subtype5	4	3	7

Figure S56. 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 = 0.56 (Chi-square test), Q value = 1

Table S64. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	6	1
subtype2	9	0
subtype3	11	2
subtype4	6	0
subtype5	8	2

Figure S57. 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 = 0.363 (Chi-square test), Q value = 1

Table S65. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	7	1	0
subtype2	4	0	3
subtype3	11	1	1
subtype4	5	0	2
subtype5	5	0	3

Figure S58. 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 = 0.194 (Chi-square test), Q value = 1

Table S66. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	7	3
subtype2	6	9
subtype3	5	10
subtype4	4	6
subtype5	3	11

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

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0859 (ANOVA), Q value = 1

Table S67. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype2	3	86.7 (5.8)
subtype3	2	95.0 (7.1)
subtype4	3	96.7 (5.8)
subtype5	3	80.0 (10.0)

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S68. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4
Number of samples	12	7	24	21

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

P value = 0.693 (logrank test), Q value = 1

Table S69. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	63	7	0.6 - 151.9 (63.9)
subtype1	12	2	0.6 - 98.3 (41.8)
subtype2	7	0	2.5 - 102.8 (25.9)
subtype3	23	2	11.2 - 151.9 (63.9)
subtype4	21	3	24.8 - 137.1 (76.9)

Figure S61. 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 = 0.052 (ANOVA), Q value = 1

Table S70. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	64	51.2 (14.4)
subtype1	12	57.6 (16.5)
subtype2	7	60.4 (15.3)
subtype3	24	47.2 (13.7)
subtype4	21	49.0 (11.8)

Figure S62. 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 = 0.61 (Chi-square test), Q value = 1

Table S71. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IV
ALL	21	23	14	6
subtype1	4	2	4	2
subtype2	2	4	0	1
subtype3	7	11	5	1
subtype4	8	6	5	2

Figure S63. 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 = 0.455 (Chi-square test), Q value = 1

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3+T4
ALL	21	23	20
subtype1	4	2	6
subtype2	2	4	1
subtype3	7	11	6
subtype4	8	6	7

Figure S64. 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 S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1+N2
ALL	40	5
subtype1	6	1
subtype2	4	0
subtype3	17	2
subtype4	13	2

Figure S65. 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 = 0.128 (Chi-square test), Q value = 1

Table S74. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	32	2	9
subtype1	5	0	3
subtype2	2	1	0
subtype3	10	1	4
subtype4	15	0	2

Figure S66. 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 = 0.242 (Fisher's exact test), Q value = 1

Table S75. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	25	39
subtype1	2	10
subtype2	3	4
subtype3	9	15
subtype4	11	10

Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.142 (ANOVA), Q value = 1

Table S76. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	11	89.1 (9.4)
subtype1	3	80.0 (10.0)
subtype3	5	92.0 (8.4)
subtype4	3	93.3 (5.8)

Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.497 (ANOVA), Q value = 1

Table S77. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	10	27.5 (21.5)
subtype1	3	31.7 (14.6)
subtype3	4	34.2 (30.2)
subtype4	3	14.3 (11.2)

Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'NUMBERPACKYEARSSMOKED'

Methods & Data

Input

Cluster data file = KICH-TP.mergedcluster.txt
Clinical data file = KICH-TP.clin.merged.picked.txt
Number of patients = 64
Number of clustering approaches = 8
Number of selected clinical features = 10
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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